calibration of deterministic parameters for reassessment of offshore … · oall values on offshore...

CALIBRATION OF DETERMINISTIC PARAMETERS FOR

REASSESSMENT OF OFFSHORE PLATFORMS IN THE ARABIAN GULF

USING RELIABILITY-BASED METHOD

By: Hassan Zaghloul

This thesis is presented for the degree of Doctor of Philosophy

University of Western Australia School of Mechanical Engineering

2008

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DEDICATION

To the loving memory of my father

To my mother who had the arduous task of raising four children by herself after

the sudden death of my father

To my beloved wife Mine’ who endured my absences and supported my passion

for this research

To our sons Taha and Zaccaria who make our life beautiful

To all those who have contributed to this thesis

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ABSTRACT

The Arabian Gulf oil and gas production reserves have made it one of the world’s

strategic producers since early 1960s, with many of the existing platforms stretched

beyond their original design life. Advances in drilling technology and reservoir

assessments have extended the requirement for the service life of those existing

platforms even further. Extension of the life span of an existing platform requires

satisfactory reassessment of its various structural components, including piled

foundations.

The American Petroleum Institute Recommended Practice 2A (API RP2A) is

commonly used in the Arabian Gulf for reassessment of existing platforms. The

API guidelines have been developed for conditions in the Gulf of Mexico, the

waters off Alaska and the Pacific and Atlantic seaboards of the USA. However, the

Arabian Gulf conditions are fundamentally different to those encountered in US

waters. Hence, there is a need to develop guidelines for reassessment of existing

offshore structures to account for the specific conditions of the Arabian Gulf.

This thesis performs statistical analyses on databases collected during this research

from existing platforms to calibrate relevant load and resistance factors for the

required guidelines. The developed guidelines are based on established approaches

used in developing international codes and standards such as API RP2A-LRFD.

The outcome of this research revolves around the following three main issues:

1. Calibration of resistance factors for axial capacity of piles driven in the

carbonate soils

API RP2A (1993, 2000) does not quantify limiting soil parameters for piles driven

in carbonate soils and provides a single factor to predict the capacity of piled

foundations. This research identifies a set of limiting engineering parameters and

calibrates corresponding capacity reduction factors to predict axial capacity of

driven piles in the carbonate soils of the Arabian Gulf.

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Further, this thesis shows that the use of a single capacity reduction factor of 0.7, an

approach that is adopted in API RP2A-LRFD (1993), does not consider that axial

pile capacity in existing platforms is influenced by many parameters identified in

this research, including implied risk level, manning levels, variation in pile wall

thickness along its depth, soil composition, hammer type, installation method,

penetration ratio and the level of optimization in the original design. The

reassessment guidelines developed in this research recommends a set of capacity

reduction factors within a range of 0.4-1.0 to reflect the influence of the factors

discussed above.

2. Development of open area live loads (OALL) on offshore platforms

API RP2A-LRFD (1993) refers to ASCE Standard 7-05 to quantify live loads.

However, ASCE Standard 7-05 is only applicable to building structures and does

not quantify values for OALL on offshore platforms. This thesis reveals that, unlike

building structures, the magnitude of OALL on an offshore platform deck is not

independent of loading conditions.

OALL values on offshore platforms are rather affected by factors such as platform

size, safe working load (SWL) of materials handling equipment, expected life span

of the platform, deck location on the platform (upper deck versus other decks) and

the selected influence surface (pile, primary beams, secondary beams or topside

columns). This research investigates those factors and recommends a simplified

formula to calculate OALL. The proposed formula is a function of the SWL of the

material handling equipment which dominates the magnitude of the OALL.

Reassessment applications require a combination factor for OALL, which is a

function of the coefficient of variation (COV) of the mean lifetime maximum live

loads. This thesis proposes a combination factor of 1.5 on the basis that the COV =

10% to 20% of the mean lifetime maximum live loads on offshore platforms, which

is calculated in this research, is similar to the COV (14%) used to develop the live

load combination factor (1.5) in API RP2A-LRFD (1993).

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3. Effect of extreme storm conditions on the reliability of existing platforms in

the Arabian Gulf

In the process of calibrating pile resistance factors and development of OALL, this

research develops a set of statistical parameters for load and resistance factors. The

statistical parameters are used to perform reliability analysis on a selected platform

in the Arabian Gulf. The platform is selected such that the outcome of the reliability

analysis is applicable to other platforms in that region.

The outcome of the reliability analysis reveals that operating overload conditions

dominate the failure mechanism in the Arabian Gulf. The reliability analysis

resulted in an insignificant (10-71) probability of failure under extreme storm

conditions compared to the higher value (10-6) under operating overload conditions.

Such extreme values are only possible in a mathematical model and have little

physical meaning. Nevertheless, and despite lack of a physical meaning to such

extremely low failure probability value, it demonstrates that operating overload

dominates the failure mechanism in the Arabian Gulf. The extremely low

probability of failure is partly a result of no wave-in-deck as the wave heights are

lower than the deck at high return periods.

Consequently, reassessment of existing platforms in the Arabian Gulf would be

sufficiently addressed by considering operating overload conditions only. This

contrasts with Section ‘R’ of API RP2A (1993, 2000), which focuses on extreme

environmental conditions when performing reassessment.

The probabilities of failure considered in this research do not include errors and

omissions (controlled by quality assurance procedures) or material deterioration

(controlled by choice of materials, detailing, protective devices, and inspection and

repair procedures) or reliability-based maintenance.

Addressing operating overload conditions requires attending to two issues, namely

the capacity of piles driven in carbonate soils and OALL, which have been

addressed in this research. The operational overload situation is likely to occur

during shutdown condition or during drilling or work over activities where

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significant OALL are usually applied to platform decks. Such operational overload

can be managed by placing signs at various open areas on the platform nominating

the maximum load limits (kPa), introducing procedures that ensure that maximum

load limits are not exceeded during operation and management of human behavior

by reinforcing the importance of following the procedures.

The outcomes of this research are expected to have a profound influence on

reassessment of existing platforms in the Arabian Gulf.

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TABLE OF CONTENTS

CHAPTER 1. 1

INTRODUCTION ................................................................................................................................1

1.1. BACKGROUND 1 1.2. PROBLEM STATEMENT 3 1.3. METHODOLOGY 3 1.4. JUSTIFICATION OF THE RESEARCH 4 1.5. OUTLINE OF THE THESIS 7

CHAPTER 2. 8

RESEARCH ISSUES ............................................................................................................................8

2.1. INTRODUCTION 8 2.2. ROAD MAP 9 2.3. REASSESSMENT APPROACHES 9 2.3.1. Screening Level Check 10 2.3.2. Design Level Check 10 2.3.3. Structural Reliability Analysis Method 12 2.3.4. Probabilistic Approach 17 2.4. DEVELOPMENT APPROACH 18 2.5. SELECTION OF CALIBRATION CODE 18 2.5.1. Assessment of WSD 18 2.5.2. Assessment of LRFD 20 2.6. HISTORICAL BACKGROUND OF LRFD CODES 21 2.6.1. LRFD for Steel Building Structures 21 2.6.2. LRFD for Offshore Piled foundations 23 2.7. AXIAL PILE CAPACITY IN CARBONATE SOILS 27 2.7.1. Deposition History of Carbonate Soils in the Arabian Gulf 28 2.7.2. Characteristics of Carbonate Sediments 29 2.7.3. Installation Experience of Piles Driven in Carbonate Soils 31 2.7.4. Review of Loading Tests in Carbonate Soils 33 2.7.5. Sources of Difficulty in establishing Engineering Parameters 35 2.7.6. Approach used in Industry Practice 37

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2.8. OPEN AREA LIVE LOADS (OALL) 38 2.8.1. Background 38 2.8.2. Live Load Surveys for ASCE Standard 7-05 39 2.8.3. Probabilistic Model in ASCE Standard 40 2.8.4. Applicability of Probabilistic Model to Offshore Structures 41 2.8.5. Reduction Factors in ASCE Standard 42 2.9. CHARACTERIZATION OF THE CLIMATOLOGY IN THE ARABIAN GULF 43 2.10. SUMMARY 46 2.10.1. Code to be used in Calibration 46 2.10.2. Axial Pile Capacity in Carbonated Soils 46 2.10.3. Target Reliability Level 47 2.10.4. Open Area Live Load (OALL) 47 2.10.5. Dominant Failure Mechanism 47

CHAPTER 3. 60

METHODOLOGY ............................................................................................................................. 60

3.1. INTRODUCTION 60 3.2. OUTLINE OF THE METHODOLOGY 60 3.3. DATA COLLECTION AND GROUPING 62 3.3.1. Challenges 62 3.3.2. Sub-grouping the Data 62 3.4. STATISTICAL ANALYSIS 63 3.4.1. Distribution Type 63 3.4.2. Distribution Properties 64 3.5. APPLICATION OF RELIABILITY- BASED METHOD 66 3.5.1. Calculation of Probability of Failure 66 3.5.2. Bayesian Update 69 3.6. PREDICTION OF AXIAL PILE CAPACITY 72 3.7. CALIBRATION OF PILE RESISTANCE FACTORS 73 3.8. DERIVATION OF OPEN AREA LIVE LOADS (OALL) 76 3.8.1. Influence Surface Concept 76 3.8.2. Extreme Value Analysis 77 3.9. COMPUTER SOFTWARE PROGRAMS 78 3.9.1. Risk Analysis Software - @RISK 78

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3.9.2. APIPILE - Pile Capacity Spreadsheet 79 3.9.3. GRLWEAP-Wave Equation Analysis Program 79 3.9.4. Structural Analysis Computer Software (SACS) 80 3.9.5. Pile Driving Analyzer (PDA) 82 3.9.6. Case Pile Wave Analysis Program (CAPWAP) 83 3.10. SCOPE DELIMITATION AND KEY ASSUMPTIONS 85 3.11. JUSTIFICATION OF THE METHODOLOGY 86 3.11.1. Reliability-Based Method 86 3.11.2. Extent of the Database 86 3.11.3. Bayesian Updating 87 3.11.4. Wave Equation Analysis Method 87 3.11.5. Influence Surface Method 88 3.12. SUMMARY 89

CHAPTER 4. 102

CALIBRATION OF PILE RESISTANCE FACTORS .......................................................................102

4.1. INTRODUCTION 102 4.2. CALIBRATION MECHANICS OF AXIAL PILE RESISTANCE FACTORS 102 4.3. PILE INSTALLATION DATABASE 103 4.4. PREDICTED AXIAL PILE CAPACITY 105 4.4.1. Limiting Engineering Parameters 105 4.4.2. Input Data to APIPILE 108 4.4.3. Output from APIPILE 109 4.5. ACTUAL AXIAL PILE CAPACITY 109 4.5.1. Methodology 110 4.5.2. Input Data for Wave Equation Analysis 110 4.5.3. Short Term Axial Pile Capacity 115 4.5.4. Time Effect - Computing Setup Factors 116 4.5.5. Back-Analysis Procedure 120 4.5.6. Sensitivity Analysis of the Computed Actual Pile Capacity 126 4.6. STATISTICAL PARAMETERS OF BIAS FACTORS 127 4.6.1. Statistics of the Complete Database 128 4.6.2. Grouping by Installation Method 129 4.6.3. Grouping by Soil Type 131

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4.6.4. Grouping by Optimized Design 132 4.6.5. Grouping by Penetration Ratio 133 4.7. BENCHMARKING STATISTICS OF BIAS FACTOR 133 4.7.1. Bias Factors in API RP2A-LRFD (1993) 134 4.7.2. Bias in the Statistics of Bias Factors 135 4.8. BAYESIAN UPDATE OF BIAS FACTOR STATISTICS 138 4.8.1. “Prior” Distribution 138 4.8.2. “Likelihood” Distribution 139 4.8.3. “Posterior” Distribution 139 4.9. TARGET RELIABILITY INDEX 139 4.10. CALIBRATION OF RESISTANCE FACTORS 140 4.10.1. Target Reliability Levels 141 4.10.2. Soil Types 141 4.10.3. Installation Methods 142 4.10.4. Penetration Ratio 142 4.11. SUMMARY 143

CHAPTER 5. 202

CALIBRATION OF OPEN AREA LIVE LOADS............................................................................. 202

5.1. BACKGROUND 202 5.2. DEFINITIONS USED IN DEVELOPMENT OF OALL 203 5.3. CALIBRATION MECHANICS OF OALL 204 5.4. NATURE OF LIVE LOADS ON OFFSHORE PLATFORMS 204 5.5. EQUIPMENT LOAD DATABASE 206 5.6. SUBGROUPING THE EQUIPMENT DATABASE 207 5.6.1. Subgrouping by Platform Function 207 5.6.2. Subgrouping by Location on Platform 208 5.6.3. Subgrouping By Crane and Monorail SWL 208 5.7. STATISTICAL PARAMETERS OF EQUIPMENT WEIGHTS 209 5.7.1. On Lower Decks 209 5.7.2. On Upper Deck 210 5.7.3. Conclusion 210 5.8. CALCULATION OF MEAN LIFETIME MAXIMUM PILE LOAD 210 5.8.1. Extreme Axial Pile Load Using Normal Distribution 212

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5.8.2. Extreme Axial Pile Load using Lognormal Distribution 213 5.8.3. Minimum Separation Distance 215 5.10. BENCHMARKING THE STATISTICS OF OALL 219 5.11. SENSITIVITY ANALYSIS OF OALL PARAMETERS 220 5.11.1. Varying the Deck Area 220 5.11.2. Minimum Separation Distance 221 5.11.3. Varying the Crane Capacity 222 5.12. LIVE LOAD FACTORS 222 5.13. SUMMARY 224

CHAPTER 6. 248

DOMINANT FAILURE MECHANISM............................................................................................248

6.1. OBJECTIVE 248 6.2. PERFORMANCE MODEL 249 6.3. APPROACH 249 6.4. LOGIC OF PLATFORM SELECTION 249 6.5. MATHEMATICAL MODEL 251 6.5.1. Structural Modeling 251 6.5.2. Foundation Modeling 255 6.5.3. Loading Model 257 6.6. PUSHOVER ANALYSIS 258 6.6.1. Extreme Storm Conditions 259 6.6.2. Operating Overload Conditions 260 6.6.3. Analysis of the Results 261 6.7. LOAD AND RESISTANCE STATISTICS 262 6.7.1. Resistance Statistics 262 6.7.2. Dead Load Statistics 263 6.7.3. Live Load Statistics 263 6.7.4. Environmental Load Statistics 263 6.8. PROBABILITY OF FAILURE CALCULATIONS 265 6.8.1. Using Pushover Analysis Results 266 6.8.2. Validating Probability of Failure under Operating Overload Condition 269 6.9. CALIBRATION OF ENVIRONMENTAL PARTIAL LOAD FACTORS 274 6.10. SUMMARY 277

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CHAPTER 7. 307

CASE STUDY .................................................................................................................................. 307

7.1. BACKGROUND 307 7.2. DESCRIPTION OF THE PLATFORM 308 7.3. MATHEMATICAL MODEL 309 7.4. ANALYSIS OF THE RESULTS 310 7.5. SUMMARY 312

CHAPTER 8. 323

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ......................................................... 323

8.1. SUMMARY 323 8.2. METHODOLOGY 323 8.2.1. Open Area Live Load (OALL) 324 8.2.2. Axial Pile Capacity in Carbonate Soils 324 8.2.3. Dominant Failure Mechanism 325 8.3. CONCLUSIONS 326 8.3.1. Dominant Failure Mechanism in the Arabian Gulf 326 8.3.2. Applicability of Section ‘R’ to the Arabian Gulf Conditions 327 8.3.3. Specifications for OALL 327 8.3.4. Limiting Engineering Parameters of Carbonate Soils 328 8.3.5. Bias Factors of Axial Pile Capacity in the Arabian Gulf 329 8.3.6. Target Reliability Levels 330 8.3.7. Specifications for Axial Pile Capacity in Carbonate Soils 330 8.3.8. Modification to Deterministic Method for Reassessment 332 8.3.9. Limitations of API RP2A Prediction Model 333 8.3.10. Modeling Pile-Soil Interaction 334 8.4. RECOMMENDATIONS FOR FUTURE RESEARCH 334 8.4.1. Technical Issues 334 8.4.2. philosophical issues 336

BIBLIOGRAPHY ............................................................................................................................. 343

AUTHOR’S PUBLICATIONS ......................................................................................................... 389

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APPENDICES..................................................................................................................................390

APPENDIX A: EQUIPMENT DATABASE 391

APPENDIX B: PILED FOUNDATION DATABASE 392

APPENDIX C: SOIL PROFILE DATABASE 393

APPENDIX D: PREDICTED STATIC CAPACITY OF PILES IN THE DATABASE 394

APPENDIX E: “ACTUAL” PILE CAPACITY USING BACK ANALYSIS PROCEDURE DEVELOPED

IN THIS RESEARCH 395

APPENDIX F: COMMON STATISTICAL DISTRIBUTIONS USE IN THIS RESEARCH 396

APPENDIX G: STRUCTURAL RELIABILITY ANALYSIS (SRA) 397

APPENDIX H: PREDICTION METHODS FOR AXIAL PILE CAPACITY IN “NORMAL” SOILS 398

APPENDIX I: APIPILE MANUAL 399

APPENDIX J: SACS INPUT FILES AND EXTRACTS FROM SACS OUTPUT FILES 400

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ACKNOWLEDGMENTS

I owe an immense debt of gratitude to my supervisor, Professor Beverley Ronalds,

for her support from the formative stages of this thesis to its final draft. Her sound

advice, careful guidance and conscientious reviews of several drafts of this thesis

were invaluable as I travelled through the journey of this research.

I extend my appreciation to Dr. Geoff Cole and Dr. Manolis Fakas for providing

valuable suggestions during this research and to Dr. Mostafa Ismail for the

conscientious proof reading and making valuable suggestions to the last draft of this

thesis. Thanks to Dr. Krystyna Haq for reviewing an earlier draft of this thesis.

This acknowledgment must include all scholars and academics listed in the

Bibliography for providing the necessary background to this research. In particular,

the direct and indirect contribution of Professor Robert Bea to this research was

immensely useful and greatly appreciated. This also extends to Professor Bengt

Fellenius and Dr. Frank Rausche, who provided guidance at the start of this research

and offered software free of charge.

Last but not least, I appreciate the outstanding support from UWA library and

administration staff at the School of Oil and Gas Engineering.

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DEFINITIONS

Definitions adopted by researchers are often not uniform, so key and controversial

terms are defined in this section to establish positions taken in the research.

Fellenius (1999) recommended some of the following definitions adopted in this

research.

Term Definition

Allowable Load Maximum load that may be safely applied to a foundation unit under expected loading and soil conditions and determined as the Capacity divided by the Factor of Safety.

Applied (Service) Load Load actually applied to a foundation unit

Axial, Bearing, Shaft and Toe Capacity

Ultimate Resistance of the unit.

Blow count During pile driving, the blow count represents the count of blows for a specified penetration of the pile into the soil. Typically, the count of blows is measured for a pile driven one foot into the soil and the blow count is recorded in a pile driving record.

Capacity The maximum or ultimate soil resistance mobilized by a foundation unit. It is used as a stand-alone term and is synonymous with Ultimate Resistance.

Capacity, bearing The maximum or ultimate soil resistance mobilized by a foundation unit subjected to downward loading. It is the sum of the shaft resistance and the toe or ‘end bearing’ resistance.

Dynamic Monitoring The recording of strain and acceleration induced in a pile during driving and presentation of the data in terms of stress and transferred energy in the pile as well as of estimates of capacity.

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Term Definition

Factor of Safety The ratio of maximum available resistance or of the capacity to the code allowable stress or load.

Loading Test Refers to the situation of a test performed by loading a pile while Load Test is a test for finding out what load is applied to a pile.

Limit State A state that defines the boundary between a safe and unsafe situation

Penetration Resistance Effort required in advancing a pile. When quantified, it is either the number of blows required for the pile to penetrate a certain distance or the distance penetrated for a certain number of blows.

Pile Head The uppermost end of a Pile

Pile Impedance A material property of a pile cross-section determined as the product of the Young's modulus (E) and area (A) of the cross section divided by the wave speed (c).

Pile Point A special type of pile shoe.

Pile Shaft The portion of the pile between the pile head and the pile toe.

Pile Shoe A separate reinforcement attached to the pile toe of a pile to facilitate driving, to protect the lower end of the pile and/or to increase the toe resistance of the pile.

Pile Toe The lowermost end of a pile.

Pore Pressure Pressure in the water and gas present in the voids between the soil grains minus the atmospheric pressure.

Probability of failure This is an unfortunate choice of wording because it can be mistakenly treated as being synonymous with the actual rate of failure. The prefix “nominal” or “notional” is often applied to the probability of failure to emphasize its formal nature (CIRIA, 1977, Ellingwood et al., 1980, Melchers, 1999). An alternative would be to use the reliability index, β, which is mostly free of such connotation.

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Term Definition

Quantitative Risk Assessment (QRA)

Formal and systematic approach for identifying potentially hazardous events and estimating likelihood and consequences of accidents developing from these events to people, environment and resources.

Ultimate Load Capacity evaluated from the results of a static loading test.

Set Penetration for one blow, sometimes penetration for a series of blows.

Setup or Soil Setup Describes the effect of resistance increase with time after driving. This term is sometimes referred to as Soil Freeze but this term will not be used in this thesis as it has a different meaning for cold regions of the world.

Shaft Resistance Calculated as the integral over the embedded pile area of the unit skin friction value

Structural Analysis Refers to the technique of making stiffness or stress calculations while Structural Assessment includes the whole process of modeling the problem, analysis and interpretation of the results.

Structural Reliability Analysis (SRA)

SRA aims at determining the probability of failure of a Limit State that, in its basic form, attains an unsafe situation. In SRA, a Limit State is represented, again in its basic form, by a Limit State equation which attains a negative value for unsafe situations and a positive value for safe situations. The Limit State equation incorporates basic random variables defined by probability density functions through (a) statistical analysis of existing sample data and (b) by experience and theoretical considerations. The representation of real structural systems may involve a number of Limit States (such as buckling, yielding, fatigue or excessive deformation under various loading conditions), some of which may be represented by a number of different failure equations.

In this case, the analysis needs to incorporate statistical correlation effects between the basic random variables as well as between Limit State equations (a set of basic random variables only affect the outcome of different

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Limit State equations).

In this thesis, SRA is primarily concerned with calculating the probability of ultimate collapse of the total substructure due to extreme environmental storm loading. It does not treat all possible hazards to the structure from a QRA viewpoint.

Toe Resistance Soil resistance acting on the pile toe

Transferred Energy The energy transferred to the pile head and determined as the integral over time of the product of force, velocity, and pile impedance.

Wave Speed The speed of strain propagation in a pile.

Wave Trace A graphic representation against time of a force or velocity measurement.

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NOMENCLATURE

Symbol Definition

A Side surface area of pile or a factor to account for cyclic or static loading

BOR beginning of restrike

BS base shear

c Constant that accounts for the errors associated with simplification of the equation describing reliability of pile groups

C Wave speed in m/s

CAPWAP Case Pile Wave Analysis Program

COV Coefficient of variation

COVQ Coefficient of variation of load

COVQD Coefficients of variation for dead load (QD)

COVQL Coefficients of variation for live load (QL).

COVR Coefficient of variation of resistance

COVχ Coefficient of variation of system effect

COVζ Coefficient of variation of group efficiency

CS Soil type dominated by clayey soils overlain by sandy soils

CC Carbonate content

C(x,y) Influence coefficient

Cdb Hammer damping factor

d Mean water depth

D Diameter of a pile or hammer damping input value

Dn Nominal dead load

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Symbol Definition

DOE Department of Energy

d/gT2app Dimensionless relative depth

EOD End of driving

E(W) Mean of the equipment weights

Eh Hammer efficiency

Er Manufacturer rated hammer energy

ETR Energy transfer efficiency

F Unit skin friction capacity or total axial force on the column using influence surface diagram

F(x) A value used to approximate Cumulative Distribution Function at each value of x

FORM First order reliability method

fs,si Limit on unit friction value for a silica sand with a carbonate content (CC) of 20% or lower

fs,80 Limit on unit friction value applicable with carbonate content (CC) of 80% or greater

FOS Factor of safety

g Acceleration of gravity

H/gT2app Dimensionless wave steepness

H Wave height

HAT Highest astronomical tide

Hs Significant wave height

Hb Breaking wave height

Js Damping constant for skin friction

Jp Damping constant for end bearing

kram Hammer cushion or impact block or ram stiffness

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Symbol Definition

k Initial modulus of subgrade reaction in force per volume units

K Coefficient of lateral earth pressure

kN Kilo Newton = Unit of pressure measurement

L Length of a pile or wave length

LAT Lowest astronomical tide

Ln Nominal live load

LT Lifetime of T years

LRFD Load Resistance Factor Design

m Shape parameter for Weibull distribution

MHHW Mean higher high water

MHLW Mean higher low water

MLHW Mean lower high water

MLLW Mean lower low water

MN Mega Newton = 1000 * kN

MPa Mega Pascal

mram Ram mass

MSL Mean sea level

N Bearing capacity factor

OALL Open area live load

Pa Pascal

p-y curve Lateral soil resistance-deflection curve

( )BAP j Posterior distribution on A

( )jABP

Likelihood function of the data

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Symbol Definition

P(Aj) Prior distribution on A

psf or lb/ft2 Pounds per square foot

PH Horizontal load

PV Vertical load

pu Ultimate bearing capacity at depth X in units of force per unit length

Pf Probability of failure

Pf a Annual probability of failure

Pf L Probability of failure for a lifetime of L years

PDA Pile Driving Analyzer

q-z curve Relation between mobilized end bearing resistance & axial tip deflection

Q Load as described in reliability formulation

Qt Total capacity of a pile

Qs Skin friction capacity of a pile

QP End bearing capacity of a pile

Qi Nominal load effect

Qmean Mean load

q Unit end bearing capacity

Q80 Limit on end bearing applicable to carbonate content of 80% or higher

Qsi Limit on end bearing applicable to silica sand with a carbonate content of 20% or lower

R Resistance as described in reliability formulation

R2 Correlation coefficient value – a measure of correlation between two sets of data.

RP Return period

Rmean Mean resistance

Rm Measured value of resistance

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Symbol Definition

Rn Nominal resistance or predict nominal pile capacity using API RP2A

Sb Set per blow

su Undrained shear strength

SRD Soil resistance to driving

SRA Structural reliability analysis

SACS Structural analysis computer software

SC Soil type dominated by sandy soils overlain by clayey soils

SPT Standard penetration test

t Mobilized soil adhesion

tmax Unit skin friction capacity

t-z curve Axial load transfer relationship

Tapp Apparent wave period

Tp Peak period

Tz Mean zero-crossing period

V Current speed

W Wind Load

WSD Working Stress Design

Wn Nominal wind load

Wram Hammer ram weight

x and y Normalized spatial variables ranging from zero to one

X and Y Dimensions to define tributary area for a column or a pile

z Local pile deflection

αn Dispersion parameter

βT Target reliability index

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Symbol Definition

θ Scale parameter for Weibull distribution

Φ Standardized Normal cumulative distribution function.

εc Strain which occurs at one-half the maximum stress on laboratory undrained compression tests of undisturbed soil samples

γi Load factor for load condition i

un Characteristic largest value (mode) of the extreme value Fn

φ' Angle of internal friction

δ Angle of skin friction

V Ram impact velocity

Z Variable

φ Resistance factor

λR,λQD &λQL Bias factors for the resistance, dead load and live loads, respectively

λζ Bias factor of the group effect

λχ Bias factor of the system

λR Bias factor of the resistance

λμ p,l,u Mean of the bias factor in the posterior, likely and updated conditions

λσ p,l,u Standard deviation of bias factors in the posterior, likely and updated conditions

μ Mean of the normal variables

μp, l, u Estimate of the mean in the prior, likelihood and updated distributions

ζ Standard deviation of the logarithms of the variables

σ Standard deviation of the normal variables

σ p, l, u Estimate of standard deviation in the prior, likelihood and updated distributions

σw Standard deviation of the equipment weights

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LIST OF FIGURES

Figure 2-1: A description of reassessment methods shows gaps in the body of knowledge in determining

axial capacity of piles in carbonate sands, OALL on offshore structures and the effect of environmental

loads on the dominant failure mechanism in the Arabian Gulf 54 Figure 2-2: The process of reassessment of existing platforms outlined in Section ‘R’ of API RP2A-LRFD

(1993). The process requires attending to extreme storm conditions only and does not address other

conditions such as accidental or operating conditions 55 Figure 2-3: Water depth profile in the Arabian Gulf showing that the maximum water depth is 100m 56 Figure 2-4: Installation experience in the Arabian Gulf and the Mediterranean showing free fall of a pile

as evident from the zero blow count in the charts (Nauroy and Le Tirant, 1986) 57 Figure 2-5: Photo of an actual platform floor deck in the Arabian Gulf showing that the open area is

mainly unloaded except for some pipes that are used as scaffolding for painting and maintenance works

58 Figure 2-6: A plot of load versus time showing the nature of sustained and transient (or extraordinary)

loads and the total live load 59 Figure 3-1: Analytical approach used to calibrate pile resistance factors and OALL for the conditions of

the Arabian Gulf. The calibration of OALL established the statistical parameters of the database and

employed influence surface method and extreme value analysis to define a uniformly distributed load.

Calibration of the pile resistance factors utilized a database to calculate bias factors and employed

FORM to calibrate resistance factors for axial capacity of driven piles in the Arabian Gulf 91 Figure 3-2: Flowchart showing the approach adopted in this research to perform reliability analysis on a

representative platform from the Arabian Gulf with the objective of defining the dominant failure

mechanism in the Arabian Gulf 92 Figure 3-3: Schematic showing the basis for calculating the probability of failure 93 Figure 3-4: An example of large scatter in a set of data which is intended for calibration. The chart

shows that the calibration of a uniform set of factors requires the data to be sub-grouped. The number of

subgroups can be increased without limits 94 Figure 3-5: Influence Surface for column axial load. Note that the influence area is 2X * 2Y (McGuire

and Cornell, 1974) or four times the tributary area for a column 95 Figure 3-6: Distribution palette in @RISK enables a choice of distribution type that best fits the data

being analyzed 96 Figure 3-7: Force and velocity fall measurements versus time for a free end condition. This illustration is

typical for a free situation where the pile “runs” under the hammer blow. In the chart, A is the pile cross

sectional area, E is the pile elastic modulus, C is the wave speed and F is the force generated at the

impact surface of the pile (Hannigan et al., 1997) 97

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Figure 3-8: Schematic of CAPWAP Analysis Method 98 Figure 3-9: Factors that have a dominant effect on the accuracy of CAPWAP prediction (Hannigan et al.,

1990) 99 Figure 3-10: Schematic of CAPWAP analysis method showing CAPWAP iteration matching process

(Hannigan et al., 1990). The trial and error iteration adjustment process results in refinement in the soil

model to obtain the best agreement between the measured and computed pile head forces. The resulting

soil model can then be considered to represent the best estimate of the static pile capacity. In this

example, initial capacity (1667kN) was refined to derive a final value of 2187kN in step 5 100 Figure 3-11: One of the platforms used in this research. It functions as living quarters on the upper deck

and process facilities on lower decks. The platform includes a helideck 101 Figure 4-1: Diagrammatic representation of the soil/ pile database showing a pile that was driven using

various hammers in soil strata. The graph shows the relationship between the resistance of the pile and

penetration depth. The diagram shows that various hammers (Menck 4600/150, 3000/150) were used to

drive the pile to the desired penetration depth. Firstly, Menck 3000/150 hammer was used to drive the

pile from the seabed to the desired penetration depth of 98m 167 Figure 4-2: A comparison of the calculated driving force (left) and stresses (right) against measured

values as suggested by Tagaya et al. (1979) method. The measured response was taken from an actual

offshore installation in the Arabian Gulf, which was reported by Tagaya et al. (1979) 168 Figure 4-3: Input parameters used for a demonstration pile are shown here. This 1219mm diameter steel

pile is 105m long and penetrates 79.9m into the soil. The pile was driven by MENCK MRBS4600

hammer with an assumed efficiency of 67% and 1.5m stroke. The water depth measured from the water

surface to the mudline is around 25m 169 Figure 4-4: This chart shows the complete pile driving record for a demonstration pile. The soil profile

and the blow count at the pile tipping depth are of interest for the back-analysis calculations 170 Figure 4-5: This chart shows the results of the GRLWEAP Bearing Graph analysis. The output screen

shows all input parameters that were assumed in the design such as the assumed efficiency in addition to

stresses in the pile and the relationship between blow count and predicted capacity 171 Figure 4-6: Estimate of setup factor was made using various start/stop data of the pile driving record.

The graph shows the relationship between SRD to elapsed time during driving. Inspection of the trend in

the chart indicated that a setup factor of 2 fairly represented long term effect of setup 172 Figure 4-7: Scatter plot of the ratio of GRLWEAP capacity to static loading test capacity at the

Beginning of Restrike (BOR) and End of Driving EOD) conditions (Thendean et al., 1996) 173 Figure 4-8: Blow count versus depth diagrams for four piles which were plotted by an installation

contractor in during actual pile driving installation in the Arabian Gulf. The pile driving records were

collated in this research. The pile driving records show that all piles were driven to around 56m with

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blow count of approximately 25 blows per foot 174 Figure 4-9: GRLWEAP input data screen for the analysis of a pile to validate the back-analysis results.

The pile has a cross section of 1193cm2 and penetrates 56m into the soil stratum. The pile was driven by

a Menck MHU 600 hammer. The soil parameters used in this analysis were selected from Table 4-9. The

computed pile capacity from GRLWEAP represented short term capacity. The long term capacity was

computed by allowing for a setup factor of 2 and compared to the output from the dynamic monitoring

results for the pile in question 175 Figure 4-10: Predicted axial capacity using GRLWEAP of the pile that had been subject to dynamic

monitoring. This pile was used to validate the back-analysis procedure 176 Figure 4-11: Pile makeup of the dynamically monitored pile. The pile makeup consists of 9 sections with

a uniform outside diameter of 1219mm. The minimum wall thickness used in the pile makeup was 20mm

and the maximum wall thickness was 44mm at the mudline 177 Figure 4-12: Modeling of the pile-soil interaction in GRLWEAP requires a breakdown of the system at

each layer and at each change in pile section 178 Figure 4-13: Results of GRLWEAP analysis when average pile wall thickness across the whole pile

length was assumed for the pile instead of using the actual wall thickness for every pile section 179 Figure 4-14: Mechanics of wave propagation in a pile (Cheney and Chassie, 1993). The mechanics of

driving a pile was used to explain the reason behind the divergence in results when an average pile wall

thickness - instead of actual variable thickness - was used when modeling pile wall thicknesses in

GRLWEAP 180 Figure 4-15: Sensitivity analysis of the computed ultimate capacity in GRLWEAP as a result of changing

hammer efficiency. The curves show that the computed capacity is relatively insensitive to the assumed

efficiency for low blow count such as those experienced in the Arabian Gulf 181 Figure 4-16: Influence of changing segment length on the computed pile capacity showing that pile

capacity is insensitive to the segment length for the range of pile capacities experienced in the Arabian

Gulf 182 Figure 4-17: Sensitivity of using various cushion types and manufacturers on the computed ultimate

capacity. The plot shows that pile capacity is relatively insensitive to the selected cushion type as soil

resistance, rather than pile characteristics, dominates the axial capacity 183 Figure 4-18: Bias factors for the complete set of data comprising 138 piles. The trend shows similar

number of piles with bias factor above and below 1.0. Hence, the trend implies no bias in the overall

database 184 Figure 4-19: Statistical analysis of the complete set of data shows that a normal distribution is most

appropriate. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36

for axial capacity of piled foundations in carbonate soils 185

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Figure 4-20: Statistical analysis of the complete set of data using the parametric method shows that a

normal distribution is most appropriate as described in Section 3.4.2. The statistical analysis produced

bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in

carbonate soils 186 Figure 4-21: Scatter plot showing bias factors for piles installed using supplementary installation

methods. The plot shows that, in situations when piles are installed using supplementary methods, the

use of API RP2A to predict pile capacity significantly (40% to 80%) overestimated the capacity and

therefore is on the unsafe side 187 Figure 4-22: This chart shows a histogram of all piles that were installed using supplementary

installation methods. An assumption of normal probability distribution provided the best fit to the data as

shown above. The statistical parameters of the drilled/ grouted piles were computed as λ = 0.65 and

COV = 0.40 188 Figure 4-23: An assumption of normal probability distribution for piles installed using supplementary

methods resulted in linear probability plot. The statistical parameters of the drilled/ grouted piled

foundations were computed as λ = 0.65 and COV = 0.40 189 Figure 4-24: Scatter plot for soil Type SC which describes predominant cohesive soil profile underlain by

cohesionless soil 190 Figure 4-25: Scatter plot for soil type CS which describes predominant cohesionless soil profile

underlain by cohesive soil 191 Figure 4-26: Probability plot of soil type SC showing that a normal distribution fits the data with mean =

0.77, standard deviation = 0.26, COV = 0.34 192 Figure 4-27: Probability plot of soil type SC confirming that the assumed fitted normal distribution is

appropriate with mean = 0.77 and COV = 0.34. This is in line with the non-parametric analysis using

@RISK which indicated similar statistical parameters 193 Figure 4-28: Statistical parameters of the bias factors for piles driven in soil type CS was subgrouped

further to piles with optimum design against those with overconservative design. This plot shows

statistical parameters of bias factors for piles which are optimally designed 194 Figure 4-29: Statistical parameters of the bias factors for piles driven in CS soils. This plot shows the

scatter diagram of the bias factor for those piles with an overconservative design. The definition of

overconservative design in this research describes piles with a factor of safety of 4 or more according to

API RP2A-WSD (2000). The API RP2A-WSD (2000) only requires piles to be designed for a factor of

safety of 2 under operating conditions 195 Figure 4-30: Probability plot for soil type CS with conservative design, The analysis showed that a

Normal distribution provided the best fit to the data with statistical parameters λ = 0.97, COV = 0.34

196

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Figure 4-31: probability plot for soil type CS with conservative design, λ = 0.97, COV = 0.34. The

results of this non-parametric statistical analysis are consistent with the parametric analysis described in

Figure 4-30 197 Figure 4-32: Probability plot of soil type CS with optimum design. The probability plot shows that a

normal distribution provides the best fit to the data with statistical parameters λ = 1.12, COV = 0.26. 198 Figure 4-33: Probability plot of soil type CS with optimum design, λ = 1.12, COV = 0.26 199 Figure 4-34: Calibrated resistance factors for the various subgroups identified in this research. The

chart shows that, for a certain target reliability level, the resistance factor for a pile driven using

supplementary methods should be lower than that for a pile driven without the need for drilling or jetting.

The plot also shows that API RP2A-LRFD (1993) recommends a single value for the resistance factor

and does not address the various conditions that affect the value of the resistance factor 200 Figure 4-35: A plot showing the effect of penetration ratio on the calibrated resistance factors. The

analysis shows that the calibrated resistance factors are insensitive to various penetration ratios 201 Figure 5-1: An example of an unloaded open area during normal operations of an offshore platform and

at times other than shutdown. The photo shows that there is usually minor loads and personnel on open

areas of offshore platforms 232 Figure 5-2: An example of a skid frame supporting a compressor skid unit which includes a compressor

driven by a turbine. In this example, a motor could be scheduled for maintenance during a shutdown,

which would require various components of the skid to be disassembled on the open area of the platform

233 Figure 5-3: Scatter plot showing equipment weight and their corresponding footprint for every piece of

equipment in the database that was used in this research. The scatter plot shows that the majority of the

surveyed equipment weighs less than 20 tonnes (200kN) with an approximate linear relationship between

an equipment weight and its footprint 234 Figure 5-4: Schematic of a platform elevation showing that equipment with larger size (and weight) tends

to be located on the upper decks to facilitate removal and maintenance. This is usually preferred by

operation and maintenance personnel to facilitate and optimize operations and maintenance costs 235 Figure 5-5: This chart shows histogram of the truncated equipment weights on lower decks. The

statistical parameters were calculated as mean = 36.1kN and standard deviation = 18.4kN 236 Figure 5-6: Histogram of the equipment weight on lower decks and an assumed fitted Exponential

distribution. Using the assumed distribution, the statistical parameters were calculated as mean =

135.4kN and standard deviation = 134.8kN 237 Figure 5-7: This chart shows a histogram and fitted distribution of the truncated database for equipment

weights on the upper deck. The fitted distribution is lognormal with mean = 81.5kN and a standard

deviation = 30.6kN 238

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Figure 5-8: This chart shows histogram of the equipment weight on the upper deck fitted an Exponential

distribution with mean = 627kN and standard deviation = 638kN 239 Figure 5-9: Plan view of the model platform decks used to demonstrate the application of the influence

surface concept for a 15m square floor deck 240 Figure 5-10: The relationship between the maximum lifetime live load on a pile and the number of

shutdown events for lower decks. The plot shows an asymptotic relationship with a maximum lifetime live

load on the pile of 275kN for a lognormal distribution and 250kN for a normal distribution 241 Figure 5-11: Relationship between number of shutdown and the standard deviation of the mean lifetime

live load for lower decks showing that the standard deviation ranges between 10%-20%. An average of

15% is used in this research 242 Figure 5-12: The relationship between the maximum lifetime live load on a pile and the number of

shutdown events for the upper deck shows an asymptotic relationship 243 Figure 5-13: The relationship between the number of shutdowns and the standard deviation of the mean

lifetime live load effect for the upper deck showing that the standard deviation ranges between 15%-22%

and depends on the distribution type and the number of occurrences 244 Figure 5-14: Sensitivity analysis results showing the effect of varying the deck area on the number of

sectors for lower decks. The analysis revealed that OALL is not sensitive to varying deck areas.

Doubling the floor area from 200m2 to 400m2 results in 11% reduction in OALL 245 Figure 5-15: Relationship between minimum separation distance and OALL for n = 50 on lower floors.

The chart shows that the calculated OALL is sensitive to the selected minimum distance when the

minimum distance is 3m or less as discussed in Section 5.8.3. 246 Figure 5-16: Relationship between crane capacity and OALL on lower decks showing that OALL is

sensitive to the SWL of the crane used on the deck 247 Figure 6-1: Illustration of the dominant failure mechanism under various conditions. The chart shows

that the dominant failure mechanism is determined by comparing the probability of failure under

operating overload against extreme storm conditions. When the probability of failure under operating

conditions is much lower than the probability of failure under extreme storm condition, then operating

conditions dominate the failure mechanism. Conversely, when the probability of failure under extreme

storm conditions is lower than that under operating conditions, then extreme storm dominate the failure

mechanism. When the probability of failure under extreme storm condition is similar to that under

operating overload condition, the dominant failure mechanism is determined on the basis of interaction of

both conditions 285 Figure 6-2: Performance model for the cases of dominant extreme storm (left) and operating overload

(right) conditions. In the dominant operating condition, the effect of horizontal load PH on the pile

system is relatively small when compared to the effect of the vertical load PV 286

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Figure 6-3: Plan view of the model platform decks used in the pushover analysis. The platform is 15m by

15m between the gridlines 287 Figure 6-4: The requirement for large open area on wellhead platforms is driven by the dimensions of

drilling rig. This figure shows a jack-up drilling rig adjusted for one well and also shows alternative

locations of the rig to drill other wells 288 Figure 6-5: Illustration of the probability of failure for the selected platform in this research under

various conditions. The direction of the arrows indicates increasing/ decreasing probability of failure.

The “X” indicates the selected platform positioning in relation to the population. This demonstrates that

the selected platform provided a lower bound solution for the extreme storm condition as a result of

choosing the deepest water in the Arabian Gulf and an upper bound solution for the operating condition

as a result of choosing a wellhead platform with large open areas 289 Figure 6-6: SACS computer model geometry showing the 4-legged jacket structure in 100m water depth

and the topside structure. The piles are driven 70m into the soil 290 Figure 6-7: A description of the nonlinear SACS computer model used in the static pushover analysis 291 Figure 6-8: A plot of the soil shear strength of two boreholes at a given site. The plot shows the variation

of the interpreted shear strength along the depth, but also within the same layer. In this analysis, the

average shear strength value in each layer was adopted 292 Figure 6-9: Input p-y curves for the soils in the case research platform. The curves were computed in

SACS using API RP2A procedure described 293 Figure 6-10: Input t-z curves for the various layers in the case research platform. The curves were

computed in SACS using API RP2A procedure but a reduction factor was applied to the calculated spring

stiffness to reflect the findings of this research showing the reduced axial capacity of piles in carbonate

soils 294 Figure 6-11: Assessing applicable wave theory for use in the analysis (Source: API RP2A-LRFD, 1993).

The vertical axis is entered with the maximum wave height and apparent wave period. The horizontal

axis is entered with the mean water depth and the apparent wave period. The outcome of the analysis

defines the applicable wave theory to be used to derive hydrodynamic loading on the structure 295 Figure 6-12: Results of the static pushover analysis showing the collapse mechanism to be shear

dominated, where the piles are subject to critical failure. The deflected shape is shown only for the

framed structure (in red) and not for the piles. The discontinuity shown between the piles and the frame

represents the deformation when the frame collapsed 296 Figure 6-13: The pushover analysis in the vertical direction was carried out to assess the dominant

failure mechanism in the Arabian Gulf under operating overload. The COLLAPSE analysis shows that

the dominant failure mechanism is in the piles 297 Figure 6-14: Extrapolation of maximum wave height in the Arabian Gulf shows its long term distribution

xxxii

follows a Weibull distribution 298 Figure 6-15: Extrapolation of current speed in the Arabian Gulf shows that its long term distribution

follows a Weibull distribution 299 Figure 6-16: Fitting the calculated base shears computed from long term maximum wave heights

indicated that a Weibull distribution provided the best fit compared to other distributions as evident from

the straightness of the trend line 300 Figure 6-17: The probability of failure under extreme storm condition was calculated from the

relationship between the long term base shear values and the corresponding return periods. Using the

collapse load calculated from pushover analysis, the relationship provides the return period which

corresponds to the collapse load. The probability of failure was (2.3*10-71) calculated as the inverse of

the return period 301 Figure 6-18: A comparison of the severity of environmental data in the Arabian Gulf to other parts of the

world. The graph shows normalized extreme environmental load versus return period and demonstrates

the dependence of platform reliability level on its environment (Van de Graaf et al., 1994). The

normalized extreme environmental load versus return period for the Arabian Gulf was derived in this

research 302 Figure 6-19: Effect of dead to live load on reliability index showing insensitivity of the D/L ratio on the

reliability index 303 Figure 6-20: Effect of changing factor of safety and resistance COV on the calculated reliability index for

single piles 304 Figure 6-21: The effect of system factor on the computed group reliability index. The green horizontal

line presents the reliability index for a single pile and the red curve presents the reliability index for a

group 305 Figure 6-22: Effect of changing the system and group coefficient of variation on the computed reliability

index 306 Figure 7-1: Results of the geotechnical analysis carried out by the consultant to calculate the capacity of

piled foundations using API RP2A LRFD (1993). In deriving the ultimate axial capacity along the depth

of the pile, the consultant used the capacity reduction factors as per API RP2A-LRFD (1993) but

employed subjective limiting parameters to predict the pile capacity 319 Figure 7-2: An isometric view of the platform analyzed in this research 320 Figure 7-3: Pile assembly of the platform investigated in this research. The diagram is extracted from

the pile drawing and shows pile diameters and penetration lengths in mm 321 Figure 7-4: Mathematical model showing the finite elements used to study the behavior of the structure in

the case study 322 Figure 8-1: Flowchart showing an outline of the specifications that can be used for reassessment of

xxxiii

existing offshore platforms in the Arabian Gulf under operating overload conditions 339 Figure 8-2: Flowchart showing a proposed method developed in this research to calculate OALL 340 Figure 8-3: Flowchart showing a proposed method developed in this research to predict axial capacity of

piles driven in carbonate soils in the Arabian Gulf 341 Figure 8-4: A proposed flowchart to identify the requirement and steps required to collect a future

database for pile capacities 342

CHAPTER 1: INTRODUCTION 1

CALIBRATION OF DETERMINISTIC PARAMETERS FOR EXISTING PLATFORMS IN THE ARABIAN GULF

Chapter 1.

INTRODUCTION

1.1. BACKGROUND

Advances in reservoir assessment and recovery techniques, subsea technology,

seismic and directional drilling techniques extend field life and impose higher

demands on existing platforms to support additional vertical and lateral loads. The

increase in vertical loads results from the need to support heavier and/or additional

pieces of equipment. The increase in lateral loads could be due to higher wave and

wind loads resulting from installation of additional risers on the platform.

Increasing vertical and horizontal loads on an existing platform entails reassessment

of the integrity of the platform to carry such additional loads.

In particular, reassessment of existing pile capacity is of interest in light of the

increased knowledge in the field of soil-structure interaction and unique behavior of

certain offshore soils. For example, the behavior of pile-foundations in carbonate

soils was not fully understood in the early 1960s during installation of the first series

of platforms in the Arabian Gulf. Later experience, ongoing research and full scale

test results showed significantly lower skin friction for driven piles in carbonate

soils than in siliceous sand (Kolk, 1999).

The outcome of reassessment determines the subsequent course of action. For

example, if a pile is reassessed and found to be “unsafe”, structural intervention may

be necessary or a new platform may be required. Both scenarios are very costly and

this may eventually compromise the economic viability of a development. A

rational reassessment of piles of existing platforms is therefore necessary to avoid

costly solutions while ensuring that the underlying risk is as low as practically

acceptable (ALARP).

A complete reassessment specification for offshore platforms requires attendance to

several conditions, including:



• Ultimate Limit States (ULS), arising from pre-service or in-service conditions,

including extreme events potentially leading to loss of the structure or of a major

component of it,

• Serviceability Limit State (SLS), arising from undesirable motions or

deflections, which may impinge on the functionality of key equipment or

discomfort of the crew,

• Fatigue Limit State (FLS) arising by cumulative effects of day-to-day operation

and leading to unsatisfactory performance and need for repairs, and

• Accidental Limit State (ALS), arising from accidental events such as due to ship

impact of Authorized and passing vessels or explosion and fire scenarios.

Attending to these conditions requires modeling of the physical processes that

govern the response of the structure and applying analytical methods to arrive at a

decision for each model. When these models are linked together, the combination

predicts the overall response of the system under consideration, enabling overall

decision regarding the reliability of the platform.

However, every model is usually valid within a certain range and for a specific use.

Consequently, models need to be chosen for their particular utility with regards to

the failure mechanism (e.g. corrosion, fatigue and overload) and cause of failure

(e.g. environmental loads, operating, earthquake and ship collision).

Treatment of all the models of an offshore structure in this research for every

conceivable loading condition, failure mechanism, failure mode, structural

configuration and type of structure would be impractical. Fortunately, for the

predominant class of fixed steel space-frame structures, which is the subject of this

research, only one specific scenario usually results in the dominant failure

mechanism. For example, a number of failure mechanisms may be of no major

concern in reassessment of existing platforms due to association with adequate

warning time (e.g. fatigue failures) or because these are of a strictly local nature and

pose no threat to the survival of the platform.

This research adopts a similar approach to that used in international codes and

standards and identifies a dominant failure mechanism for use in developing the

required guidelines for reassessment.



1.2. PROBLEM STATEMENT

Guidelines in international codes and standards such as Section ‘R’ of the API

RP2A-LRFD (1993), known as Section 17 in API RP2A-WSD, only consider

extreme storm condition on the basis that such condition dominates the failure

mechanism. A review of the state-of-the-knowledge in reassessment of existing

offshore platforms reveals that:

• Operating overload conditions, with specific reference to the selected values of

live loads and axial pile capacity in carbonate soils, are not covered by API

RP2A-LRFD (1993) or any other international codes or standards, and

• There are no studies to examine the effect of benign environments on the

dominant failure mechanism of offshore platforms.

• The thrust in this research is to develop specifications that can be employed for

reassessment of existing offshore structures in the Arabian Gulf. Development

of such specifications requires addressing the issues identified above since these

are not covered in international codes and standards such as API RP2A (1993,

2000).

1.3. METHODOLOGY

Solution to the core research problem is carried out within the framework of

reliability theory and employs calibration techniques similar to those used in the

development of API RP2A-LRFD (1993).

The research inherently assumes that the basic API RP2A formulation, which has

been based on many years of experience in US waters and combines the experience

of its expert Committee, is suitable for the Arabian Gulf. The focus of this research

is on calibrating deterministic parameters for loads and resistance factors, which can

be used with API RP2A-LRFD (1993) formulation, but employing a database

specific to the Arabian Gulf.

API RP2A is commonly used in the Arabian Gulf to perform design and

reassessment of existing platforms. The Author’s experience indicates that the

uptake of ISO Standards to perform design and reassessment in the Arabian Gulf

has been very slow. This is driven by weight of familiarity with API RP2A and the

fact that ISO Standard contains no specific data for offshore platforms in the



Arabian Gulf, hence providing little benefit and no incentive for a change.

Calibration of load and resistance parameters can only be carried out when load and

resistance factors are separated so that research efforts can be rationally focused to

calibrate for conditions not previously covered in the literature. It is reasonable to

treat these two entities (foundation and structural frame) as two separate

components of a weakest-link model. The probability of failure of each of these

components is obtained by a separate model capable of taking into account the

redundancy within each component. The foundation component may be further split

into two components: (1) Laterally loaded piles; and (2) Axially loaded piles. Such

approach was adopted as the basis for calibrating the API RP2A-LRFD (1993) and

was also used by Marshall and Bea (1976).

A “first-order” probabilistic procedure is used to determine the values of the

resistance and load factors. The “first-order” probabilistic procedure is a simplified

method that uses only two statistical parameters, namely mean values and

coefficient of variation, of the relevant variables and a relationship between them

termed the “safety index” or β.

1.4. JUSTIFICATION OF THE RESEARCH

The importance of this research is underpinned by economic and safety reasons

relating to the strategic importance of the world’s supply of oil and gas in general

and in the Arabian Gulf region in particular.

Re-assessment of existing fixed structures around the world is a major issue as there

are now over 1,200 existing platforms exceeding 20 years of age (Morandi, 2006).

These platforms were designed according to the practices of their time, which are

different from the improved standards adopted in the design of new structures.

Reassessment and requalification criteria were incorporated into Section ‘R’ in API

RP2A-LRFD (1993).

While new designs often achieve Reserve Strength Ratio (RSR defined as ultimate

mean capacity over 100 year mean load) values of 1.6 and over, Section ‘R’ requires

values of 0.6, 0.8 and 1.2 under extreme storm conditions. However, Section ‘R’

does not address operating overload conditions but the Author’s experience in the

Arabian Gulf indicates that these dominate the failure mechanism in that region.



The Arabian Gulf oil and gas production rate makes it one of the strategic producers

in the world. In 2003, oil production from the Arabian Gulf amounted to about 27%

of the world's oil with a total reserve of about 57% (715 billion barrels) of the

world's crude oil. Besides oil, the Arabian Gulf also has huge reserves (2,462

trillion cubic feet) of natural gas, accounting for 45% of total proven world gas

reserves. Given the large reserves, it is important to develop specific parameters for

the Arabian Gulf to avoid excessive conservatism or unsafe offshore platforms.

Currently, reassessment of existing platforms in the Arabian Gulf is based on

subjective parameters, which may have safety and/or cost implications. A

conservative reassessment may suggest unnecessary need for strengthening or

providing additional foundation supports, which is very expensive for offshore

platforms. For example, foundation intervention in North Rankin “A” platform in

the North West Shelf of Australia ended up costing Woodside Petroleum close to

US$350 million (Haggerty and Khorshid, 1989). Such excessive costs could

compromise the feasibility of oil and gas developments, thereby resulting in losses

to society as a whole. On the other hand, unsafe reassessment could lead to

excessive deflection or even catastrophic collapse of platforms and loss of life,

environmental pollution, reputation damage and monetary losses.

Generally, standards for structural engineering contain requirements to ensure that

structures perform satisfactorily under the effect of various loads. These provisions,

which include load factors, resistance factors, allowable stresses and deflection

limits, have evolved more or less subjectively through extensive successful and

unsuccessful professional experience in the Gulf of Mexico, examination of

available experimental data, theory and judgment. As a consequence, these criteria

do not necessarily ensure consistent levels of safety and performance in other

geographical regions and may be inappropriate for structural schemes or geographic

locations where little basis for judgment may exist.

The demand for development of the North Sea since the 1970s required that the Gulf

of Mexico practice and experiences be extended and backed by extensive research

and development programs on issues of particular importance to the North Sea,

including fatigue, dynamics, large-scale tubular joints, large diameter and high

capacity foundation piles. However, application of the Gulf of Mexico practices has

not been examined to determine their suitability to the Arabian Gulf conditions.



This research addresses load and resistance factors using the specific conditions of

the Arabian Gulf. In particular, API RP2A (1993, 2000) does not cover carbonate

soils, which dominate the geology of the seabed in the Arabian Gulf. Hence, the

need for a set of specifications to address the specific conditions of the Arabian Gulf

is underlined.

There is no consensus between researchers on suitable parameters to calculate axial

capacity of piled foundations in carbonate soils. There is virtual agreement amongst

researchers that pile loading tests are the only satisfactory method to establish the

capacity of piles in carbonate soils (Angemeer et al., 1975; 1984; Dutt et al., 1984 &

1985; Gilchrist, 1985). Unfortunately, unlike onshore projects which confirm the

static analysis by performing load testing on some piles to verify the reliability of

the design methods, the costs of performing loading tests on offshore projects are

prohibitively high. Hence, reliance on prediction methods to assess axial capacity

has a higher profile and wider acceptance in the offshore industry, because the

consequence of a prediction method being in error is significant for offshore

structures.

In the absence of deterministic parameters in codes and standards that address the

specifics of the Arabian Gulf, reassessment of piles of existing platforms in the

Arabian Gulf could generally be performed using reliability or probabilistic

methods. However, appreciation of inadequacies in system reliability procedures

has caused concern over their routine use and operators have generally favored

essentially deterministic decision making procedures, backed by approximate

reliability reasoning (Stewart et al., 1988). Hence, a deterministic procedure for

reassessment of existing structures in the Arabian Gulf is required.

The probabilistic approach to structural safety embodied in this research continues

to be adopted in the structural engineering community.

To calibrate design codes, the traditional practice of setting safety factors and

revising codes based solely on experience is insufficient in an environment where

such trial and error approaches to managing uncertainty and safety may have

unacceptable consequences. In an era where standards for public safety are set in an

increasingly public forum, more systematic and quantitative approaches to

engineering for public safety are essential.



1.5. OUTLINE OF THE THESIS

This section outlines the path towards the conclusion of the thesis.

Chapter 2 outlines several issues affecting the research and sheds light on key

aspects of the Arabian Gulf to identify gaps in the body of knowledge when

reassessment of existing platforms is required. A detailed methodology to fill those

technological gaps is presented in Chapter 3.

Chapters 4, 5 and 6 address the gaps in the body of knowledge through calibrating

deterministic parameters for reassessment of offshore platforms in the Arabian Gulf.

Chapter 4 describes the calibration of resistance factors for piled foundations in

carbonate soils. Chapter 5 identifies the nature of live loads on offshore platforms

and quantifies the magnitude of live loads on open areas of offshore platforms.

Chapter 6 employs the statistical parameters developed in chapters 4 and 5 and

derives statistical parameters for environmental loads, which are then used to define

the dominant failure mechanism in the Arabian Gulf.

Chapter 7 applies the findings from Chapters 4, 5 and 6 to a real life case to

demonstrate the value of this research.

The conclusions from analysis of the research problem, their place in the body of

knowledge and areas for future studies are summarized in Chapter 8.


Chapter 2.

RESEARCH ISSUES

2.1. INTRODUCTION

During the course of this research, a number of issues were identified which pointed

to gaps in the body of knowledge. Those issues are specifically related to the use of

API RP2A in reassessment of existing platforms in the Arabian Gulf.

API RP2A permits the use of either working stress design (WSD) or load and

resistance factor design (LRFD) methods to reassess the adequacy of platform

elements for specific performance requirements. These methods introduce factors

for nominal demand and/ or resistance to achieve a level of reliability, implying

safety levels that are not explicitly stated. The factors included in API RP2A-WSD

(2000) were derived from experience in US waters but were optimized by

minimizing the differences between the target reliability levels and the achieved

reliabilities over a sufficiently large and representative number of “calibration

points”. Consequently, actual reliabilities achieved by the code recommendations

may vary from situation to situation and may deviate substantially from the targets

for cases other than the calibration points.

The Arabian Gulf conditions deviate from those “calibration points”. This Chapter

identifies and discusses the following issues as related to the Arabian Gulf

conditions:

• Characterization of offshore soil in the Arabian Gulf and its effect on methods to

determine axial capacity of piled foundations;

• Identification of the nature of open area live loads (OALL) on offshore

platforms and determination of OALL that can be used with API RP2A

formulation; and

• Characterization and effect of extreme storm conditions on the reliability of

offshore platforms in the Arabian Gulf.

CHAPTER 2: RESEARCH ISSUES 9


2.2. ROAD MAP

To answer the research question identified in Section 1.2, there was a need to

investigate current methods used in reassessment of existing platforms to define

gaps in the body of knowledge. Figure 2-1 shows that API RP2A (1993, 2000)

identifies four main methods for reassessment of existing platforms. This Chapter

evaluated each method to establish a suitable framework that can be implemented to

answer the research question. The outcome of the evaluation revealed that the

design level check method is most appropriate for use in industry practice. The

screening level method is too coarse and the reliability-based and probabilistic

methods have limitations when used in reassessment.

However, the design level check only treats extreme storm conditions and does not

consider operating overload conditions. Reassessment of existing platforms under

operating overload conditions requires attendance to open area live loads (OALL)

and prediction of axial capacity of piles in carbonate soils. Hence, there was a need

to fill these gaps by developing OALL and calibrating resistance factors for piled

foundations in carbonate soils.

Calibration techniques and development methods were identified in this research.

Reliability-based method was employed to calibrate axial capacity of offshore piles

in carbonate soils. Extreme value analysis method was used to develop

deterministic values of OALL.

The outcome of this research is a set of specifications that can be used for


2.3. REASSESSMENT APPROACHES

Reassessment of existing platforms in accordance with API RP2A-LRFD (1993)

categorizes platforms according to life-safety and consequences of failure into high

(L1), medium (L2) and low (L3) consequences of failure. The categorization is

used to assign metocean parameters for the relevant platform category. For

example, API RP2A-LRFD (1993) recommends the use of current speed equal to

1.6 knots for reassessment of category L1 platforms in the Gulf of Mexico but

reduces the recommended current speed to 0.9 knots if the platform category is L3.

The API RP2A (1993, 2000) recommended approach for reassessment of existing



platforms is mapped out in Figure 2-2. The process starts with a screening level

check and follows on to a design level check if the results of the former indicate that

the platform in question does not pass reassessment. If the design level check also

indicates that the platform does not pass reassessment criteria, potential sequential

analysis checks include ultimate strength check, structural reliability analysis and a

probabilistic approach. A description of the various checks is outlined in the

following subsections.

2.3.1. SCREENING LEVEL CHECK

In the screening level check, reassessment is only required if triggered by any one of

the initiators defined in API RP2A (1993, 2000). In such case, reassessment may be

carried out using design level check, reliability based or probabilistic methods.

2.3.2. DESIGN LEVEL CHECK

The design level approach is simple yet more conservative than other advanced

approaches. Figure 2-1 outlines the components of a design level check and the

corresponding codes used to apply the method.

The design level check is applied in stages. First, loads (actions) on the various

structures of the platform (jacket, piles and decks) are calculated using an

appropriate code. Second, a mathematical model of the platform which represents

the geometry, loads and characteristics of the platform is constructed to derive

action effects on each member in the platform structure.

The mathematical model is then analyzed to generate internal forces (axial force,

shear forces in two planes, bending moments in two planes and twisting moments)

in every member of the structure. The internal forces are combined in each member

and checked against the capacity of the member using either working stress design

(WSD) or load resistance factored design (LRFD) methods.

2.3.2.1. Working Stress Design (WSD) Method

The deterministic provisions in structural codes and standards for load combinations

and strength address the risks and uncertainties in structural performance as code

and standard-writers have historically understood them.

To account for uncertainties in the load effects and the resistance on offshore



platforms, API RP2A-WSD (2000) and its previous editions require the use of a

factor of safety to account for the various factors that impact on the system

performance. Those factors include variations in the loads and material strengths,

inaccuracies in design equations, errors arising from poorly supervised construction,

possible changes in the function of the structure from the original intent,

unrecognized loads and unforeseen in situ conditions. Using the safety factor

approach, API RP2A-WSD (2000) has been formulated to achieve a specified level

of safety without the need to consider each of these factors separately and in explicit

detail.

In the working stress design (WSD) method, the design level check has the form:

ELDSFRn ++= Equation 2-1

Where Rn = Nominal resistance

SF = Safety factor

D = Dead load

L = Live load

E = Environmental load

The factor of safety is typically applied to the calculated ultimate capacity in routine

member design using working stress design (WSD). For example, from the first

edition in 1969, the recommended safety factors for pile design were 1.5 and 2.0 for

the extreme and operating conditions, respectively, and these have remained

unchanged to the present day. In fact, these factors of safety were based on even

earlier Gulf of Mexico engineering practice dating back to the 1950s.

2.3.2.2. Load and Resistance Factored Design (LRFD) Method

In the LRFD approach, the boundary between acceptable and unacceptable

performance is explicitly defined by a set of limit state equations. This approach

permits the identification of key factors affecting failure which may be somewhat

obscure within the traditional empirical rules of WSD. The limit state approach

lends itself for use within a probabilistic framework, allowing partial safety factors

to be developed based on rational risk analysis.

The general format of the LRFD specification is given by the formula:



nii RQ φγ ≤∑ Equation 2-2

Where Σ = Summation

i = Type of load (dead, live, wind, wave)

Qi = Nominal load effect

γi = Load factor corresponding to iQ

Rn = Nominal resistance

φ = Resistance factor corresponding to nR

φ Rn = Design strength

The right hand side of the formula relates to the resistance (capacity) of the structure

while the left hand side characterizes the loading effect acting on that structure.

The purpose of the load and resistance factors is to account for unforeseen and

unfavorable deviations from their specified values and for variations due to

uncertainties in the analysis. Those load and resistance values vary for different

conditions. For example, API RP2A-LRFD (1993) recommends a resistance factor

of 0.8 for piles in compression under extreme storm conditions but reduces the

resistance factor to 0.7 for piles in tension.

The fundamental premise in the LRFD approach is that risks can be analyzed

explicitly within a consistent and rational framework. Loading and resistance

factors are used to explicitly account for variances of the criteria, making it possible

to rationally account for differences between US regional conditions and other

localities by calibrating the load and resistance factors to provide more consistent

safety levels for data points other than the calibration points used for US waters.

2.3.3. STRUCTURAL RELIABILITY ANALYSIS METHOD

Structural Reliability Analysis (SRA) is one of the advanced methods that may be

implemented in reassessment applications if the platform does not pass the design

level check. The SRA approach calculates the so-called Reserve Strength Ratio

(RSR), which is then used in reliability calculations. Many researchers, including

Bea, Efthymiou, Van de Graaf and Tromans (1994) used this method in the

reassessment of platforms in US waters and the North Sea. A description of SRA is

included in Appendix G.



The reliability-based method does constitute a significant improvement over the

traditional approach of using experience alone to establish safety factors for

foundations in at least three ways:

• Foundation design becomes more cost-effective if the level of reliability can be

maintained at a consistent target value,

• It enables minimization of the incompatibility between structural and foundation

design which creates the undesirable effect of having to apply different loadings

on the structure and the foundation and may lead to potential confusion and

mistakes in design, and

• Probabilistic methods help to relieve the foundation engineer from the task of

having to intuitively assess the complex relationship between uncertainties and

risks. At the same time, it emphasizes the importance of engineering judgment

and experience on the other design aspects that are currently beyond the scope of

mathematical analysis.

In spite of its rational basis, the reliability-based method continues to have certain

limitations when used in reassessment as described below.

2.3.3.1. Prediction of “Actual” Reliability Level

Application of the reliability-based method in reassessment produces a quantitative

assessment of reliability levels. However, comparing the theoretical probability of

failure derived from reliability computations with a value established by actual case

histories is not straightforward.

A number of Authors (e.g., CIRIA, 1977; Smith, 1981; Livingstone, 1989) noted

that the theoretical probability of failure is usually significantly smaller than the

actual failure probability of failure, and that there will always be a gap between

predicted and experienced risks. This gap is generally 1 to 3 orders of magnitude

(CIRIA, 1977). Consequently, the predicted reliability levels are best referred to as

notional, rather than absolute, levels and are better suited to comparison purposes.

The discrepancy between the predicted reliability level and “actual” reliability level

can be due to the presence of a number of uncertainties which can be classified into

three categories: physical, knowledge based and human (Bea, 1993).

The first category (known as objective, Type I or aleatory) represents natural

randomness intrinsic to a variable, such as wind loading, and its uncertainty. It



cannot be reduced with additional information.

The second category (known as subjective, Type II or epistemic) uncertainties arise

from limitations in knowledge, including measurement, statistical and modeling

uncertainty. It can be reduced at a cost by collecting more data or adopting models

that are more realistic. Knowledge based uncertainty can be further subdivided into

statistical, model and phenomenological uncertainties. Statistical uncertainty arises

due to a limited number of observations being used to make up a sample which is

then taken to represent a population. Modeling uncertainty is caused by the use of

simplified relationships between variables to represent real behavior. Methods used

to simplify loads and structural responses, such as the limit state equations are

examples of modeling uncertainties. Phenomenological uncertainty arises because

unimaginable phenomena occur, which cause structural failure. They are

particularly important for novel structures or those which attempt to extend the

state-of-the-art.

The third category (Type III) is the hardest to quantify and modify. It is associated

with human and organizational factors. This category is mainly attributable to

human error and modeling uncertainty. Around 80% of accidents involving marine

structures are due to unanticipated actions of people that have undesirable outcomes

(Bea, 2000) and not due to overloads or damage accumulation.

Structural reliability methods have generally not included Type III (Human and

Organizational Factors) uncertainties and remained focused on well-defined loading

and failure conditions such as member failure under extreme storm loading or

fatigue of welded connections. By contrast, the actuarial reliability, as expressed by

statistics of failure, highlights that Type III uncertainties can result in blowouts,

collisions, fires or explosions.

SRA principles are also not well suited to incorporate gross errors since they usually

alter the very nature of the problem by changing the probabilistic models of the

basic variables and even the form of the limit state equation (Ellingwood, 1992).

Therefore, failure probabilities predicted by SRA are not likely to converge with

actuarial data, since SRA method generally excludes gross “human” errors.

Another main reason for the mismatch between SRA results and failure probabilities

reported in databases lies in the different root causes for the failures. Failures

reported in databases tend to be caused by poor design, poor construction and



inadequate operational practice as opposed to extreme events covered by SRA.

Consequently, comparisons of failure probabilities derived from SRA against those

entered into databases may not be relevant and failure probabilities should be

carefully interpreted.

Further, one main limitation with SRA is that data will be mostly available to

validate the probability density function (PDF) around their mean value, while the

design point from SRA will usually be the tail of the PDF, where less data is

available. However, this is an inherent problem in predicting long-term extreme

events by any method.

2.3.3.2. Target Reliability Levels

The outcome of any reliability analysis can only be meaningful in the presence of a

target value. A number of investigators have discussed selection criteria for target

reliability. The Author identified two main approaches to establish target reliability

levels, namely implicit and explicit approaches.

The first and more traditional approach identifies the theoretical reliability index

that is implicit in existing design codes (Ellingwood et al., 1980). The implicit

target reliability approach fundamentally relies on what has happened in recent past

with similar types of structures and in similar types of environments.

A quantitative assessment of implicit target reliability levels in different codes and

standards is presented in Table 2-1. Various committees also published their own

recommended target reliability levels. The American Society of Civil Engineers

(ASCE) Task Committee recommended target reliability indices between 2.3 to 3.4

for structural design, but considered that those target levels serve a useful function

of defining an approximate lower bound on the target reliability level. Nordic

Committee on Building Regulations (NKB, 1978) report gives a set of suggested

values for target reliability index for various failure types and consequences. NKB

recommended values range from 2.0 to 4.7 for annual target reliability levels.

Other researchers also provided recommendations on target reliability levels.

Meyerhof (1970) indicated that foundation probability of failure should be between

10-3 and 10-4, which corresponds to values of target reliability index values between

3.1 and 3.7. Bea (1983) reported that the reliability indices of offshore piles were 3

to 4 on an annual basis and 2 to 3 on a lifetime basis. From a calibration research



involving six representative structures, Criswell and Vanderbilt (1987) concluded an

acceptable target reliability index in the range of 2.7 to 3.2. Tang et al. (1989)

reported reliability indices ranging from 1.4 to 3.0 for offshore piles. Barker et al.

(1991) selected target reliability index of 2.0 to 2.5 in their resistance factor

calibration for single driven piles. Withiam et al. (1998) confirmed that this range

of target reliability index is within a reasonable conformity with the reliability

indices evaluated for the current design practice, considering that piles are usually

used in a group. Cornell (1995) suggested a target annual structural failure

probability of 10-4 for new designs (βT = 3.7), and indicated that values one order of

magnitude in either direction seem unreasonable.

The discrepancy in the implicit target reliability levels suggests that these should not

be specified without giving extensive guidance as to how assessment should be

performed, and what assumptions are reasonable regarding uncertainty of loading

and strength. Further, a quantitative target assumes almost perfect technical

knowledge and complete understanding of the socio-political factors that play a role

in target setting including such factors as exposure time (annual or lifetime),

expected consequence level (low, medium, high), calculation method, failure mode

or nature of failure and level of society perception to risk (ISO2394, 1998, Melcher,

1999, Keese et al., 1982).

The second, and perhaps most controversial approach, is to compute explicit target

failure probabilities. This method requires high technical competence with many

technical challenges. It can be applied using cost-utility evaluation.

The fundamental premise of the cost-utility evaluation is that the most attractive “or

best” development alternative is the one that produces the highest utilities or

measures of worth. In principle, the cost-utility evaluation is the only way to

determine a rational value for target probability of failure. By researching the

variation of the initial cost, maintenance costs and the expected failure, it is

theoretically possible to compute the most economical target probability of failure

for design (Baecher, et al., 1980; Mortensen, 1993). At present, such approach is

impractical due to the difficulties in evaluating failure costs (e.g., cost of human

lives and environmental safety) and the effect of component failure on the system

(Criswell and Vanderbilt, 1987; Whitman, 1984; Vick, 1992).



2.3.3.3. Uniqueness of Mapping Events

MacFarlane and Parry (1994) identified a factor which may undermine the

rationality of the reliability-based method, pointing to the potential non-uniqueness

of the mapping of events on to consequences. It is a general assumption that the

mapping process, which involves the process of structural analysis, is complete and

will always provide a consistent result. If the analysis method is not in fact unique,

the mapping may be flawed. This would be the case when any of the parameters in

the mapping process, for example, is not complete and the dependence and

interactive nature of events is not included in the mapping process, i.e. the analysis.

2.3.3.4. Bias in Prediction Models

Another limitation relates to the treatment of foundation failures, which is due to the

significant conservative bias in existing prediction models for foundation behavior

and failure. Bias is defined as the ratio of the true value of a random variable to the

predicted or nominal value. This bias may lead to predicted risk levels that are

perhaps unrealistically high.

The ‘separation’ between structural and foundation failure in fixed structures is a

helpful strategy for calibration. However, when a more accurate foundation model

is included, system failure may occur not due to a sequence of structural component

failures, but due to a progressive loss of foundation stiffness and excessive deck

displacements.

2.3.3.5. Simplification

Omission of the transient, dynamic non-linear effects on the loading and response of

the structure is a common simplification adopted in Structural Reliability Analysis.

This may be an implicit conservative bias in the static pushover analysis not

explicitly accounted for, as it will depend on many factors such as the type of

structure and soil conditions. It can be noted that dynamic effects may play an

important part in the response of a jacket undergoing softening effect due to yield.

However, dynamic effects are not significant for offshore structures in the Arabian

Gulf due to the benign environment and the built-in conservatism in the designs.

2.3.4. PROBABILISTIC APPROACH

The probabilistic approach achieved limited application in practice, partly due to



some inherent drawbacks, although there are growing efforts to overcome them.

Some drawbacks include the high sensitivity of the small risk values to the assumed

probability distribution and its dependency on the model used, which makes the

probabilistic approach outside traditional engineering education.

2.4. DEVELOPMENT APPROACH

Section 2.3 presented the various methods used in reassessment of existing

platforms and revealed limitations associated with the use of the reliability-based

and probabilistic methods in reassessment.

Despite those limitations, the reliability-based and probabilistic methods have been

primarily used in the development of design criteria since their conception. The

logic is that there is little justification for incorporating these methods as an integral

part of the design process, because everyday design procedures must remain simple

and easy to use so as to allow the engineer to focus on the complex detailing of the

elements of an offshore platform.

In summary, it would seem reasonable to continue having procedures, parameters

and safety factors that are deterministic in nature to maintain both simplicity and

practicality but use a probabilistic basis to provide uniformity of reliability among

similar components and structures to minimize costs and optimize safety.

As a result, the reliability-based method was used in the calibration of limit state

codes around the world, including the Canadian, American, British, Norwegian and

Australian codes. This gave credence to the use of the reliability-based method in

this research to perform the required calibrations and answer the research question.

2.5. SELECTION OF CALIBRATION CODE

Section 2.3.2 presented two the deterministic approaches used in industry practice to

perform reassessment of existing platforms. This section evaluates both methods

with an objective of selecting an approach to calibrate its deterministic parameters.

2.5.1. ASSESSMENT OF WSD

When API RP2A-WSD was first introduced, the failure rate of offshore structures

constructed in the Gulf of Mexico was relatively high. For example, in 1965 the



historical chance of platform loss was 1 in 200 per year (NRC, 1981). Such failures

were partly triggered by technology limitations, especially in relation to tubular and

their connections, in addition to the limited database dealing with environmental

conditions of wave heights, wave force effects, current and soil and foundation

characteristics.

With the widespread experience gained using API RP2A, the introduction of new

technology and availability of data, API continued to revise and update API RP2A-

WSD as a matter of policy to reflect experience and technology. Such policy

resulted in a significant reduction in the rate of failures. Today, the offshore

industry feels confident that platforms classified as high consequence that are

designed to meet current recommendations of API RP2A-WSD have acceptable

safety margins and that the use of API RP2A-WSD has produced platform structures

with high historical reliability levels. This may not be the case for platforms

classified as medium (L2) or low (L3) consequence with low (0.8 and 0.6) reserve

strength ratios (RSR) as was demonstrated under the forces of hurricanes Katrina

and Rita.

Despite the success associated with the use of API RP2A-WSD in practice, many

Authors (e.g., Simpson et al., 1981; Burland et al., 1981; Kulhawy, 1984) discussed

shortcomings and limitations associated with the single factor of safety approach

that forms the basis of the WSD method.

Fundamentally, the factor of safety is not unique and, depending on its definition,

can vary significantly over a wide range of situations. A particular factor of safety

is meaningful only with respect to a given assumption and equation (Kulhawy,

1984). Aside from non-uniqueness, the single safety factor approach makes less

efficient use of past experience. Since it is not defined within a consistent and

common framework, engineers cannot communicate and share their experiences

effectively. Furthermore, the approach does not distinguish between model and

parameter uncertainties, making it difficult to justify any reduction in the safety

level if there is additional information or advances in the state-of-the-art. There is

also currently no way to extrapolate the factor of safety rationally and consistently

to accommodate new scenarios.

Another significant source of ambiguity lies in the relationship between the factor of

safety and the underlying level of risk. A higher factor of safety does not



necessarily imply a smaller level of risk, because its effect can be negated by the

presence of greater uncertainties in the environment or soil variability.

In addition to the above limitations, the safety factor approach is typically not

accompanied by a carefully prescribed procedure for defining capacity (e.g. net or

gross capacity), for carrying out the analysis (e.g. empirical or rational method) and

for deriving the pertinent design soil parameters (e.g. correlations or direct

measurements). As a result, the same numerical factor of safety can imply very

different safety margins for a structure.

2.5.2. ASSESSMENT OF LRFD

Shortcomings of the factor of safety approach associated with the WSD method

necessitated the search for more sophisticated treatment of safety levels in design

(e.g., Rojiani et al., 1991; Berger and Goble, 1992; Becker et al., 1993; Ovesen,

1993). As a result, API Committee conducted its own research to examine implied

risk levels in API RP2A-WSD (2000) and found considerable scatter in the implied

component reliabilities. The API Committee found that the LRFD method would

provide many benefits, including an increased uniform reliability and therefore

implied economy (Moses and Larrabee, 1988), especially for platforms with high

gravity loads compared to environmental loads. Moses and Stahl (1998) considered

LRFD reliability-based format to be more flexible with regard to rationally and

systematically incorporating new technological findings, interpretation of observed

platform survival and failure experiences and reformulation for new geographic

areas as well as formulating design rules in frontier areas consistent with present

practices, platform types, new threats such as ice, earthquake and collisions and the

requirements for evaluation of older structures.

The LRFD method was also considered to provide a rational approach to distinguish

between various conditions (termed limit states) that affect structural performance,

to ensure safety under rare but high-hazard conditions and to maintain function

under day-to-day conditions.

In addition, advances in the field of structural reliability such as the first-order

reliability analysis, stochastic load modeling and availability of supporting statistical

databases enable many of the uncertainties in loads and strengths to be modeled

probabilistically.



The LRFD is a semi-probabilistic method, which is termed level 1 in structural

reliability analysis (SRA) approach. In Level 1 criteria, a single set of load factors

and another single resistance factor are applied to all situations, regardless of the

relative importance of the nominal loads in the load equation. Level 2 criteria are

known as First Order Reliability Method (FORM) and are based on the use of the

mean and coefficient of variation to calculate the reliability index which is a

measure of the safety level. Level 1 and 2 criteria can be made equivalent if the

resistance and load factors on level 1 are allowed to vary for different parameters

such as live-to-dead load ratios and beam span length. However, this would be

impractical and was therefore not adopted in the calibration of AISC or API RP2A.

2.6. HISTORICAL BACKGROUND OF LRFD CODES

Given the shortcomings associated with the traditional factor of safety approach and

the rationale offered by the LRFD method, API RP2A-LRFD (1993) was employed

as the base code to calibrate parameters for reassessment of existing platforms in the

Arabian Gulf.

This section provides a brief background of the API RP2A-LRFD (1993)

specifications with an objective of identifying the basis used in its calibration and

applicability to the specific conditions of offshore platforms in the Arabian Gulf.

2.6.1. LRFD FOR STEEL BUILDING STRUCTURES

API RP2A-LRFD (1993) only covers the resistance of tubular members and refers

to ANSI/AISC 360-05 for treatment of non-tubular members. For loading criteria,

ANSI/ AISC 360-05 refer to ASCE Standard 7-05. This section describes the history

of developing ANSI/AISC 360-05 and ASCE Standard 7-05 (formerly known as

ANSI A58).

Development research for steel buildings began in several industry projects in the

1970s, a time of marked activity in code development with increased understanding

in mathematics and physics, as the effects of loads on structures could be calculated

and knowledge of material and component behavior was developed through testing.

At that time, the Secretariat for American National Building Standard Committee

A58 on Minimum Design Loads for Buildings and Other Structures was

administered in the Structures Division of the Centre for Building Technology



(CBT). The antecedents at the National Bureau of Standards (NBS) for this

standard dated back to 1924 when the Building and Materials Division published a

report under the auspices of the Department of Commerce Building Code

Committee on Minimum Live Loads.

Research on probabilistic methods in structural codes was a central thrust in the

CBT throughout the 1970s. Ellingwood and Culver (1977) researched the

probabilistic analysis of live and snow loads while Ellingwood (1979) investigated

the load combinations for reinforced concrete design. This work stood at the

intersection of research and practice and various standard-writing groups in the

United States agreed that the ANSI A58 Standard was the logical place for material-

independent load criteria to facilitate the development of one loading code that can

be used with different construction materials.

The development of the first draft of ANSI A58 criteria had to deal with loads and

load factors about which there was no general agreement, since ANSI A58 code

included no provisions for load factors and the statistical make-up (type of

probabilistic distribution, mean values, coefficients of variation) of some of its load

types was not clearly specified in the standards.

To enable compatible ANSI A58 loads with material codes, it was necessary to

develop specific basis for ANSI A58 and use this basis to develop material codes.

With lack of coordination between loading and material codes, there was a risk that,

as different standard-writing groups moved toward probability-based limit states

design, each would develop load requirements independently and that these load

requirements would be mutually incompatible in structural engineering practice

where construction technologies usually are mixed.

The ANSI A58 loading code was reworked such that:

• The statistical characteristics of its loads would be defined, and

• Common load factors and load combinations as applied to all types of structural

materials would be provided.

The reworked ANSI A58 was developed on the basis of the load and resistance

statistics reported by Galambos (1979) and identified in Table 2-2.

In 1979, Ellingwood was joined by T.V. Galambos, J.G. MacGregor and C.A.

Cornell to develop a set of common probability-based load requirements for limit



states designs that would be compatible with all common construction technologies

using advanced structural reliability analysis methods and statistical databases. The

objectives of this joint effort were to:

• Recommend a set of load factors and load combinations for inclusion in the

ANSI A58 Standard that would be appropriate for all types of building

construction (e.g., structural steel, reinforced and pre-stressed concrete,

engineered wood, masonry, cold-formed steel and aluminum); and

• Provide a methodology for various material specification groups to select

resistance criteria consistent with the A58 load requirements and their own

specific performance objectives.

The outcome of this joint effort was NBS Special Publication 577, Development of a

Probability-based Load Criterion for American National Standard A5, which was

published in June 1980 but was first implemented through the voluntary consensus

process in the 1982 edition of American National Standard Institute ANSI A58. The

load provisions in the ANSI A58 standard has been published as American Society

of Civil Engineers (ASCE) Standard 7 since 1985 and is now called ASCE Standard

7-05.

The probability-based load criteria have appeared in all editions of that Standard

since then and have remained essentially unchanged since 1982. Subsequent

developmental work on probability-based codes in the United States in such diverse

applications as buildings, bridges, offshore structures, and nuclear power plants in

the intervening two decades can all be traced back to this one seminal document.

This research accounted for the development history of ANSI A58 and employed a

similar approach to develop live loads and calibrate resistance factors.

2.6.2. LRFD FOR OFFSHORE PILED FOUNDATIONS

Predicting the ultimate strength of piles supporting offshore platforms has been

under development since the 1950s. Pelletier et al. (1993) provided a

comprehensive history of those developments spanning over half a century. This

section summarizes the development history of pile resistance factors and the

parameters governing the computations of pile capacity in API RP2A (1993, 2000).

Prior to the 1950s, the prediction of the capacity of onshore driven piles was most



commonly based on dynamic driving formulas such as the Engineering-News

Formula (Bowles, 1988). The availability of static loading test programs gradually

shifted design practice more towards the use of static design equations (using

laboratory sample strength data or estimated skin friction) and these were adopted in

the first API RP2A (1969). Initially, estimated values of skin friction based on

general descriptions of various soil types and consistencies were used with these

equations. Later, laboratory strength data played a more important role in

establishing engineering parameters.

The design guidance for the axial capacity of piles in clay was initially based on

engineering practice that had previously been followed for about 30 years in the

Gulf of Mexico and largely followed the practice of McClelland Engineers. This

guidance was unchanged until the 6th edition in 1975 when it was replaced by the

so-called API method 2. The introduction of API method 2, which was more

conservative than the original method, was a substantial change leading to a

significant increase in design of driven piles. Due to industry concerns, the previous

method was reinstated a year later in the 7th edition for highly plastic clays such as

those found in the Gulf of Mexico. The API method 2 was categorized for use with

other types of clay. The design guidance for clays remained almost unchanged until

the 17th edition in 1987 when it was completely over-hauled and a new method

introduced; however methods 1 and 2 were retained in the commentary.

For sands and silts, bearing capacity factors and soil friction angles were

recommended for a limited range of soil types, along with limiting values, for the

first two editions of RP2A. In the 3rd edition, the limiting values were removed and

this guidance remained almost unchanged until the 15th edition was introduced in

1984.

Following an extensive review of all available test data the guidance was changed

extensively. One of the most significant changes was for piles under tension where

the earth pressure coefficient was increased from 0.5 to 1.0 for full displacement

piles (plugged or closed-ended). Other changes included expanding the range of

soil types covered by guideline parameters and to re-introducing limiting values on

end bearing and skin friction.

Pelletier et al. (1993) attribute the changes in engineering parameters between

various editions of API RP2A to two main reasons. First, lack of high quality data



from the results of full-scale testing of piles under axial loading precluded the

derivation of an accurate mathematical model. Various procedures were often used

in tests reported in the technical literature, leading to difficulty in evaluating results.

Also, many techniques were used to obtain properties of in situ soil and the results

of soil tests could often not be interpreted comparably. Furthermore, in only a few

instances, measurements were made using the necessary instrumentation to reveal

the detailed manner in which the foundation interacts with the supporting soils.

Second, the interaction between a pile and the supporting soil involves many

complexities, including influence of both installation method and construction

details on soil behavior.

Advanced Mechanics and Engineering Ltd (1999) investigated the effect of these

changes on the calculated ultimate axial capacity of piles. A one meter diameter

pile in a homogeneous profile of dense clean sand characterized by an internal

friction angle of 35 degrees was considered for that research. The conclusions

pointed to very little difference between the ultimate capacities predicted by the 1st

Edition and the 15th Edition. For the 3rd to the 10th Editions (1972 to 1984) there

was a reduction in ultimate compressive capacity of up to 30% for a given pile

length if the coefficient of lateral pressure was taken as 0.5. In terms of pile length

for a given pile load, the 3rd to 10th Edition would lead to a pile length up to 7m

longer than the 15th Edition for this example.

The historical background described above covered the prediction of axial capacity

of piled foundation using WSD format. However, for compatibility with structural

codes, there was a need to develop LRFD format.

Historically, the development of Geotechnical LRFD method took place in an

environment where the relevance of probabilistic design was still being debated

(Committee on Reliability Methods, 1995, Whitman, 2000) and predominantly

involved rearrangement of existing global factors of safety into a new design format.

The debate on using probabilistic methods in Geotechnical LRFD is due to a variety

of reservations, which can be categorized into three general classes (Kulhawy et al.,

2002). The first class of reservations concerns the relevance of using statistics to

model the variability of soil properties on the basis of the following arguments:

• Soil properties do not belong to a uniform statistical population;

• Probability distributions are not supported by sufficient data;



• Statistical parameters (mean and variance) cannot characterize extreme soil

properties,

• Soil heterogeneity is not modeled adequately, and

• “Random” soil properties might take values that do not exist in nature (Boden,

1981; Simpson, et al., 1981; Driscoll, 1984; Oliphant, et al., 1988).

In fact, most of these objections point toward limitations of classical statistics rather

than probability theory as a whole. For example, soil properties that do not belong

to the same population and soil heterogeneity can be modeled using the random

field theory (Vanmarcke, 1983). Insufficient information can be supplemented

consistently by subjective judgment using Bayesian statistics.

The second class of reservations concerns the higher complexity of the design

calculations associated with the use of probability theory and includes the following

issues:

• Statistical information is not sufficiently well-defined to warrant sophisticated

treatment,

• There is a greater risk of making computational errors, and

• A research into soil behavior prediction is reduced to a mere mathematical

exercise, diverting attention from the proper characterization of the ground mass

and appreciation of the physical, chemical and mechanical processes taking

place in it (Beal, 1979; Semple, 1981; Simpson et al., 1981; Boden, 1981).

These reservations are not without merit. Excessive preoccupation with maintaining

simplicity would ultimately be a disservice to the Geotechnical engineering

profession. Historical hindsight showed that the judicious use of rational methods,

as initiated by Terzaghi in 1943, primarily caused most of the significant advances

in soil mechanics. The cost of rationality is more complicated calculations.

However, this cost is more than offset by the benefits associated with the use of

rational methods. For example, the improvement in soil behavior prediction allows

less conservatism to be applied in the design.

The third class of reservations concerns the difficulty of interpreting the theoretical

probability of failure and its usefulness in design. Many Authors have criticized the

notion of trying to quantify safety explicitly (e.g., Mortensen, 1983; Driscoll, 1984;

Beal, 1979) due to the difficulty of handling gross errors. This reservation gives



merit to the debate on using LRFD in geotechnical engineering. CIRIA (1977)

identified that the probability of failure derived from theoretical analysis is smaller

than the actual failure probability because other important sources of uncertainties

such as gross errors and unpredictable “Acts of God” were not included in the

analysis.

Despite those reservations, the calibration of axial pile capacity in API RP2A-LRFD

(1993) was carried out using probabilistic methods and similar approach was

adopted in this research.

2.7. AXIAL PILE CAPACITY IN CARBONATE SOILS

The prediction of the axial capacity of offshore piles can be carried out using API

RP2A-LRFD (1993) guidelines. In parallel with this, Morgan and Finnie (2006)

identified other methods and their required soil input to predict axial capacity of

piles including University of Western Australia, Imperial College Pile Design

Method, Norwegian Geotechnical Institute and Kolk and van de Welde equations.

With any method, there are various caveats (often in the form of text rather than

equations) that limit its use, or there may be a need for modifications to the method

to make it applicable to unusual soils or situations (e.g. high carbonate content,

volcanic soils). Limitations are imposed on the method to reflect the approach used

to calibrate that method.

For example, Section 6.4.3 in API RP2A-LRFD (1993) limits the use of its method

to predict axial capacity of piles in carbonate soils, stating that “to date, general

design procedures for foundations in carbonate soils are not available”.

Other international codes and standards also do not provide, exploit or quantify

guidance for driven piles in carbonate soils. Angemeer (1973, 1975) and others

found that general design procedures for foundations in siliceous sands dramatically

overestimate the capacity in regions with carbonate sediments.

This section presents a background to carbonate soils and identifies the required

engineering parameters to predict axial capacity of piles driven in carbonate soil.



2.7.1. DEPOSITION HISTORY OF CARBONATE SOILS IN THE

ARABIAN GULF

The Arabian Gulf measures approximately 1000 kilometers in length and 200 to 300

kilometers in width. The water depth profile is generally rather shallow, with the

deepest water (about 100m) being located near Iran as shown in Figure 2-3.

The area represents a shallow tectonic depression which was formed in the late

tertiary period (approximately 7 million years ago) by down warping of the earth’s

crust. At this time, the sea reached a maximum elevation of 150 meters above

present levels with subsequent deposition of typical ‘shallow carbonate Shelf Sea’

materials including limestone, marls and clastics (Agarwal et al., 1977).

Stevenson and Thompson (1978) described the formation of carbonate soils starting

18000 years ago when global cooling and the onset of glacial conditions in higher

latitudes marked the beginning of the Flandrian period. During this time, the sea

reached a minimum elevation of approximately 120 meters below its current level,

exposing the previously deposited marine sediments to sub-aerial and fluvial

weathering processes. As the climate steadily moderated towards the end of this

period and into the beginning of the Holocene (10,000 years ago), the Gulf

gradually became inundated again. However, this process was far from regular and

large zones of the region experienced irregular periods of sub-aerial and marine

sedimentation. Post-glacial sedimentation included shelly sands in the shallow

zones, clean carbonate mud in deeper depressions and impure carbonate mud or

marls along the axis of the Gulf.

The presence of gypsum and carbonate cementation in the Arabian Gulf soil profile

is common. Gypsum is one of the less soluble of these salts and is therefore

precipitated in relatively large amounts. It is precipitated in pore spaces of onshore

deposits when seawater is drawn inland as groundwater and is then evaporated on

rising to the ground surface.

Gypsum can be present in various forms including the thick accumulations of

gypsum developed on tidal flats or areas periodically flooded by saline water or the

thin crusts of gypsum usually inter-bedded with the prevailing sediments. These

sediments may be precipitated from standing water in highly restricted lagoons.

Cementation of loose carbonate particles requires little environmental change in



temperature or carbon dioxide concentration and can be concurrent with submarine

deposition. Often precipitation of cement in coarse-grained sediments is much

localized laterally and vertically, and recently cemented carbonate layers may

overlie uncemented material. Thus, some carbonate sediments are able to support

high overburden pressure long before consolidation occurs if cementation is

contemporaneous with deposition. This may result in high void ratios at depth but,

with the increasing overburden pressures, creep and re-crystallization will be

induced and porosity will eventually be reduced. Cementation may also occur under

hot, arid sub-aerial and super-saline lagoonal conditions, similar to those imposed

during the sea recessions in the Arabian Gulf. Upward movement of saline

groundwater induced by high evaporation rates at ground level transports salts to the

surface where they are precipitated, binding loose sediments to form a hard

cemented layer or duricrust.

Groundwater movement through, and therefore cementation of, fine grained

sediments is relatively restricted but an additional effect of the sub-aerial period on

these soils was to impose overconsolidation characteristics by the processes of

desiccation and removal of overburden by erosion.

In conclusion, the deposition history of the Arabian Gulf soils results in extremely

variable sediments. In particular, the distribution and occurrence of the

predominantly carbonate materials commonly encountered in the top 100m of the

sediment column tend to be laterally and vertically variable. Occasionally this

lateral variability can occur over relatively short distances with attendant

significance for offshore foundation capacity.

2.7.2. CHARACTERISTICS OF CARBONATE SEDIMENTS

The deposition history of the Arabian Gulf established that its soils are dominated

by sediments of carbonate origin. This section presents an overview of the nature of

carbonate sediments.

Carbonate sediments are biologically derived gravel, sand, silt and clay sized

sediments, which may have undergone post depositional alteration such as

cementation and chemical replacement (McClelland, 1974). Carbonate sediments

may be found in a variety of forms from soft microscopic oozes through to large

complex structures of coral to massive strong rock.



The word carbonate is used as a generic description to indicate a soil that contains a

significant proportion of carbonate material. The term ‘significant’ is somewhat

subjective. For example, the content of calcium carbonate could be up to 100

percent.

Carbonate sediments are distinctive in that they are composed of material of marine

origin, which are significantly different from the more-familiar land-based

(terrigeneous, often siliceous) materials and often have different engineering

properties from such soils. In general, it may be said that the mineral of which these

materials are formed is significantly softer than most terrigeneous materials and is

susceptible to crushing. However, high interparticle forces can develop due to

interlocking of unusually shaped particles and more significantly cementation. The

minerals are to some extent soluble in the pore water. This solubility can alter the

structure of the deposit with time and in some cases within the economic life of the

foundation. The particle shape is often hollow, resulting in unusual physical

characterization of the deposits. Finally, and as a consequence of these

characteristics, the deposits often have very high void ratios. As a result of these

distinctive features, carbonate soils exhibit unexpected behavior compared to

terrigeneous soils which form the basis of classical theory of soil mechanics.

Indeed, the difference can be so dramatic as to lend the theory inapplicable.

This group of soils has received special attention due to its worldwide distribution

along the continental shelves of the oil-rich zones and the increased need to develop

in these zones.

Carbonate sediments are often encountered in the relatively shallow water

environment of the tropics between the latitude 30 degrees North and 30 degrees

South including the Red Sea, the southern part of the Arabian Gulf, the Continental

Shelf of Western Australia and Bass Strait at the southern tip of Australia, the Java

Sea, in North America off the west coast of Florida, in central America off the

Yucatan peninsula and in Barbados (Stevenson and Thompson, 1978).

Geologic processes described in Section 2.7.1 control the soil structure within these

deposits and therefore its mechanical behavior. Celestino and Mitchell (1983)

suggested that grain hardness and intra-granular porosity, soil fabric, cementation

and carbonate content account for differences in behavior between carbonate

deposits and their terrigeneous counterparts.



The fabric of carbonate soils is an important characteristic feature. The term “soil

fabric” covers a variety of sediment characteristics including the arrangement,

shape, and size distribution of grains, the intra-granular porosity or void ratio and

the relative amount of carbonate materials. These are interrelated parameters in the

sense that carbonate soils with highly angular particles often have a high in situ void

ratio due to particle orientation. Due to these features, grains of carbonate soil crush

readily under relatively low compressive and shear stresses. It is that crushability

that is responsible for mobilization of low lateral stress, which is required to develop

skin friction of driven piles.

Cementation is perhaps the most distinguishing feature of carbonate soils, and its

effect on the frictional soil-pile response and on the lateral stresses that are

mobilized against the pile wall has been much discussed in the literature. The

degree of cementation may range from weak fragile bonds at particle interfaces to

highly cemented concretions in which the matrix voids are virtually filled with

calcite. Cementation may increase the strength but it causes the soil to respond to

load in a brittle manner and undergo degradation due to crushing and

compressibility of the material leading to strain softening and “unzipping” type

failure. Cementation may promote arching following shear-induced volume

reduction, resulting in reduced lateral pressures on a driven pile.

2.7.3. INSTALLATION EXPERIENCE OF PILES DRIVEN IN

CARBONATE SOILS

Due to the complex nature of carbonate soils, installation experience of piled

foundations in carbonate soils has been highly unpredictable. For installation in

very weakly cemented and very compressible formations, it is not unusual to

experience the free fall of a pile for several meters or even several tens of meters as

shown in Figure 2-4. The free hammer fall can be under the own weight of the pile

or as a result of only a few blows of a hammer.

The first awakening to the unique behavior of carbonate materials came not from

borings but from pile driving operations during the construction of a platform in

Iranian waters in 1968 (McClelland, 1988). In this case, offshore borings identified

layers of calcarenite and thick layers of sand containing visible shell fragments but

the significance of the high carbonate content of the sands was not recognized.



During installation, a 30-inch (760mm) diameter pile fell freely for 17m after first

penetrating 8.5m of variably cemented material. Fortunately, lithified sediments at

50m provided high end bearing capacity.

Surprises continued as carbonate sediments were encountered in construction of

piles in other parts of the world. In May 1980, the first Garoupa field platform in

the Campos Basin offshore Brazil was installed in 120m water (Matos and Mello,

1982). When the 48-inch (1219mm) diameter pile was placed, no penetration

occurred under the combined weight of the pile, the pile follower and the Vulcan-

560 steam hammer. After five blows, the pile suddenly broke through and ran out

from under the hammer to an uncontrolled penetration of 50 m. The plunge was so

rapid that the Vulcan 560 hammer was suspended in mid-air and continued driving

upon itself. This led to failure of an auxiliary hook which supported the hammer

and the hammer plunged to the sea bottom where it was lost. Subsequent

investigation revealed that there was a thin grout layer over the sea-bottom as a

result of spill over from pre-drilling well and cemented zones of carbonate subsoil

underlay this grout layer.

The installation of the North Rankin ‘A’ structure (NRA) on the Northwest shelf of

Australia in the 1980’s added yet another dimension to a rather unique engineering

problem. The large 72-inch (1800mm) diameter piles required high capacities and

deep penetration into carbonate sediments. A very comprehensive site investigation

and engineering program was undertaken. In spite of this, a surprise occurred once

again after installation and the piles would not meet their axial design requirements.

Limited remedial measures were first investigated but these ultimately were judged

ineffective leading to an exhaustive research of many strengthening alternatives.

The final price tag reached around US$350 million (Haggerty and Khorshid, 1989).

The free fall of piles was found to be due to crushing of the tiny shells constituents

of the carbonate soil thus exerting almost no effective lateral pressure against the

pile wall. Piles were thus driven very easily but developed little capacity in either

downward bearing or uplift. On the other hand, driving may prove to be ineffective

in strongly cemented levels and drilling (cleaning out the inside of hollow pipe) may

be the only solution for running through the hard but relatively thin layer.

Alternatively, an insert pile is set in place in the case of a thicker layer, with

subsequent grouting of the annulus.



In the process of experiencing the unique behavior of carbonate soils, extensive

basic knowledge was added through fundamental and detailed investigations into

almost every aspect of foundation design and analysis for carbonate sediments,

ranging from site investigation procedures to material characterization as presented

in the following sections. However, the prediction of axial capacity of piled

foundations in carbonate soils remains highly subjective and site-specific.

2.7.4. REVIEW OF LOADING TESTS IN CARBONATE SOILS

Stemming from the failure of conventional methods to predict pile behavior in

carbonate sediments, several loading tests were performed and reported in the

literature.

The first full-scale pile loading tests in carbonate soils were performed by Angemeer

et al. (1973) in the Bass Strait, offshore south-eastern Australia. In 1977, Agarwal

et al. reported the results of Geotechnical site investigation that was performed at

three sites offshore India.

The research was conducted to understand the effect of varying percentages of

carbonate content on the engineering properties of the soils. The soil carbonate

content at the site ranged from 15% to 96%. The main conclusions from the

research pointed to the significant influence of carbonate content on stress-strain

characteristics of soil, particularly at higher normal stress and at carbonate contents

higher than 45%. Further, it was shown that the effect of quartz sand becomes

prominent if the carbonate content is less than 30% and that the strength of the soil

reduces for sands with a carbonate content above 45%. The research also concluded

that carbonate content in clay appears to have beneficial effects on strength

properties. Further, the sensitivity (ratio of the undisturbed undrained shear strength

to the remoulded undrained shear strength) of carbonate clay was reported to vary

mostly between 4 and 5.

Datta et al. (1979) described results of an experimental investigation designed to

determine the amount of crushing in carbonate sands under low and elevated cell

pressures and to assess the influence of crushing on the shear characteristics of such

sands. They found that the extent of crushing is influenced by particle

characterization and suspected the significance of intraparticle voids. They

concluded that the significant reduction in the maximum principal effective stress



ratio with crushing partly provides an explanation for the low skin friction around

piles observed in the field for piles in carbonate sands. The research used

dimensionless crushing coefficients to describe crushing of sands but was unable to

relate changes on shear behavior induced by crushing to a quantitative measure of

crushing.

In 1980, Datta et al. confirmed that particle crushing and cementation in carbonate

sands affect the axial load capacities of piles. Particles of these sands differ in their

nature, shape and form and exist in both cemented and uncemented states.

Based on field and model tests on piles in carbonate soils, Nauroy and LeTirant

(1983) suggested an inverse correlation between end bearing and compressibility

index. However, this correlation seems to apply best to uncemented soils that are

actually rather atypical.

In 1984, Dutt and Cheng reported the results of several Geotechnical studies on soils

from the Gulf of Suez with similar skeletal carbonate sands to that investigated by

Nauroy and Le Tirant (1983).

The soils were primarily weak to moderately cemented carbonate sands and silt with

carbonate contents greater than 90%. A subsequent series of twelve pullout tests

was used to define long term pile capacity parameters. The research concluded that

analyses that consider the lateral earth pressure coefficient K and the soil friction

angle δ as one variable provided a better evaluation of frictional response of piles in

carbonate soils with high carbonate content. For the high carbonate content sands

and silts encountered at the site under investigation, a limiting value of K.tanδ of

0.14 was found to be a best fit of the pullout test data. Incidentally, this value

coincided with the limiting value range (10kPa-14.4kPa) observed by Angemeer et

al. (1975) in the Bass Strait of Australia.

Nauroy and Le Tirant (1985) described a series of laboratory tests combined with

full-scale tests undertaken by the research institute AGREMA to determine the skin

friction and end bearing of driven piles and grouted piles installed in carbonate soils.

They reported a decrease in the skin friction of driven piles with the increase in the

compressibility of the soil. They suggested that the compressibility of carbonate

sand caused the very low horizontal stresses acting on the pile shaft which then

accounts for the very low values of friction observed on driven piles in carbonate

soils.



Since then, many examples of low, and often erratic, measured pile capacities were

published (Dutt and Cheng, 1984; Dutt et al., 1985; Gilchrist, 1985; Puyuello et al.,

1983). A summary of the reported limiting skin friction based on static loading test

interpretation is shown in Table 2-3.

The wide range (1.0kPa to 33.1kPa) of limiting values shown in Table 2-3 may be

attributed to the test or research conditions in the investigations. In the well-

controlled tests conducted by Nauroy and LeTirant (1983), the shaft resistance is

negligible, whereas the test by Angemeer et al. (1975) represents the other extreme.

By comparison, API RP2A-LRFD (1993) recommends a limiting shaft resistance

for medium dense silica sand of about 80kPa.

The main finding of these tests showed that skin friction for steel piles could be very

low and appears to be uniform over the entire pile length. The conclusion was that

field loading test was the only satisfactory method of supporting capacity

calculations.

Most researchers advise caution in extrapolating any of their test results to other

sites and recommend the use of full-scale pile loading tests at the site where piles

are to be installed. Unfortunately, loading tests in offshore sites are rarely carried

out due to the prohibitive costs although Murff (1987) believes that such loading

tests could be essential to produce cost effective designs through full understanding

of the soil behavior at that site.

2.7.5. SOURCES OF DIFFICULTY IN ESTABLISHING

ENGINEERING PARAMETERS

Difficulties associated with predicting the behavior of carbonate sediments stem

from different sources, including deposition history, difficulties associated with in

situ testing, ambiguity associated with degree of cementation and disagreement over

in situ testing, the severe changes in the properties of soils caused by pile driving

and reconsolidation and drag down of soil from one layer into another and pile-soil

interaction during loading. As a result, there is potentially a significant margin of

error in the predicted pile capacity. If a conservative approach is adopted, the piled

foundation is safe against failure but may become uneconomical. In the terrestrial

environment, prediction difficulties could be minimized by performing loading tests.

For large diameter offshore piles, there have been very few loading tests thus



requiring careful consideration of prediction methods.

This section provides a discussion of a number of the difficulties identified above.

2.7.5.1. Deposition History

Environmental conditions at the time of deposition control the quantitative

relationships between these key sediment components at a particular location.

Water depth, temperature and energy level (wave and currents) influence the

selection of grain types, the sorting and shredding of particles and the degree of

cementation. Thus, carbonate sediments in one part of the world may be totally

different to another part, resulting in lack of worldwide consensus on a methodology

for the calculation of the capacity of driven piles in carbonate sediments.

Hence, a general method for predicting the axial capacity of piles in carbonate soils

can only be developed to cover a specific geographic location. Such method can not

be generalized to cover carbonate soils around the world.

2.7.5.2. Difficulties associated with In Situ Testing

Great variability of the cementing agents in carbonate soils creates difficulties in

identification, categorization of unique properties and behavior, sampling, handling,

testing and employing empirical correlations. The brittle, crushable nature of

carbonate sands complicates site investigation and laboratory testing procedures.

Inserting a sampler, particularly by offshore percussion methods can damage

cementation bonds and cause crushing of the soil grains. Sample trimming also

results in additional disturbances. Furthermore, changes in temperature, pressure

and carbonate concentration that occur during sampling may give rise to changes in

the sample cementation during retrieval (Beringen et al., 1982).

2.7.5.3. Ambiguity associated with Degree of Cementation

Due to the complex nature of carbonate soils, it has not been possible so far to

identify a parameter to quantify the degree of cementation. This is largely due to

current offshore drilling and sampling practice which tends to destroy the

cementation bond. However, even if a parameter is developed, the degree of

cementation in any deposit is usually not uniform which makes it difficult to

interpret test data and conclusively identify trends. Most investigators (Angemeer et

al. 1973, 1975; Beringen et al., 1982; Datta et al., 1985; Hagenaar, 1982; Nauroy



and LeTirant 1985) agree that cementation is very important but proposed no clear

explanation of the mechanism by which it acts. Several researchers (Angemeer et

al., 1975; Beringen et al., 1982; Datta et al., 1980; Dutt and Moore, 1985) argued

that uniform and well-cemented carbonate sands give high shaft resistance while

weak and partial or irregular cementation is a cause of low shaft resistance. It is

argued that partial cementation can cause arching around the pile and can cause the

soil in the annulus created by driving to develop incomplete, low-pressure and

irregular contact with the pile. While these hypotheses may be plausible, they

generally leave much unsaid.

To date, little success has been achieved in obtaining high quality samples that

might provide insight into these effects. Consequently, an assessment of in situ

conditions based on sample properties has met with little success (Angemeer et al.,

1973; Beringen et al., 1982; Dutt and Moore, 1985).

The above reasons demonstrate the ambiguity and uncertainty associated with the

behavior of carbonate soils, which makes it difficult to establish relevant

engineering parameters. The difficulty is due to the fact that conventional soil

mechanics theory and experience rely on data developed primarily from terrestrial

sediments with hard particles that do not crush but displace during pile installation

thus packing more densely. In quartz type sands overburden influence is highly

significant, whereas it appears to have little effect on the offshore carbonate

sediments encountered to date, especially when cemented.

2.7.6. APPROACH USED IN INDUSTRY PRACTICE

The inability of classical soil models to provide adequate design guidelines for

carbonate soil-structure interaction problems makes it necessary for current pile

design practice to rely on limited correlations with published loading tests in these

soils rather than on any consistent guidelines.

The reliance on correlations for carbonate soils generally involves a significant

amount of engineering judgment, often imposing a lower limit of soil parameters

(e.g., bearing capacity factor, pile-soil interface friction and coefficient of lateral

earth pressure) in addition to the use of large factors of safety to account for various

uncertainties and lack of knowledge. This practice is almost wholly empirical and

highly site-specific but is likely to continue until the effects of cementation and



grain crushing are fully understood and sufficiently quantified so as to enable the

development of a rational modeling for piled foundations in carbonate soils.

This research provides an approach that can be used in industry practice to predict

axial capacity of piles driven in carbonate soils. The development leading to the

proposed approach is presented in Chapter 4.

2.8. OPEN AREA LIVE LOADS (OALL)

2.8.1. BACKGROUND

Relatively accurate techniques are available to assess structural behavior under

given loads, yet the loads themselves remain an estimate based in part on field

measurements, in part on professional logic and experience and in part on trial and

error.

One of the loading conditions prescribed in building design codes is equivalent

uniformly distributed live load (EUDL) which depends on the occupancy type. It is

multiplied by a reduction factor to account for the observed decrease in unit load as

the loaded or tributary area increases.

As discussed in Section 2.3.2, API RP2A-LRFD (1993) refers to ASCE Standard 7-

05 to estimate the live loads. The ASCE Standard 7-05 provides requirements for

general structural design but does not provide live load values for offshore topside

structures. To the best knowledge of the Author, quantifying live load on offshore

platforms has not been addressed by other researchers.

An example of an open area on an existing offshore structure is shown in Figure 2-5.

It can be described as the area covered by grating or floor plates and not supporting

fixed equipment.

Other international codes and standards including BS 6349 (2000), BS EN ISO

13819 (1998) and DOE (1990) also provide no guidance to establish OALL. The

only exception, however, can be found in DNV (2000) which disregards OALL for

substructure design but then requires that “Global load cases shall be established

based upon ‘worst case’ characteristic load combinations complying with the

limiting global criteria to the structure”.

Lack of guidance for offshore platforms can be attributed to the dominance of



extreme storm conditions on the reliability of offshore platforms in the Gulf of

Mexico and US waters, where API RP2A originated. Under extreme storm

conditions, live loads are not generally combined with other load cases.

Consequently, the use of subjective OALL values has traditionally not resulted in

any concerns to complete a design or reassessment of a platform in regions where

the failure mechanism is dominated by extreme storm conditions.

This is evident in Section ‘R’ of API RP2A-LRFD (1993), which limits its guidance

to extreme storm conditions when performing reassessment of existing platforms.

However, the Author’s experience in the Arabian Gulf consistently demonstrated

that benign environmental conditions elevated the importance of operating overload

conditions and hence the need for realistic estimation of live loads.

In industry practice, there is no consensus on the OALL value to be adopted in

reassessment of existing platforms. Some operators suggest that open area live

loads need not be considered for piled foundation design while others stipulate

values as high as 17kPa.

The development of OALL for offshore platforms in this thesis employed similar

methodology to that used in the derivation of EUDL for building codes and

standards. A description of the live load survey data and the probabilistic model

used to derive EUDL in building codes and standards is presented in this section.

2.8.2. LIVE LOAD SURVEYS FOR ASCE STANDARD 7-05

A survey of the literature revealed that the ASCE Standard 7-05 nominal live

loading is based on surveys compiled by Chalk and Corotis (1980). Statistics of the

instantaneous sustained loads were obtained as area weighted averages from all

surveys for a particular use as shown in Table 2-4 (Chalk and Corotis, 1980).

Available live load data principally addressed offices, dwellings, school classrooms,

retail and merchant stores, hospitals and health clinics, storage areas and light and

heavy industries. Some surveys permitted extraction of data pertaining to other uses

incorporated in the preceding occupancies such as library rooms and office lobbies.

Inspection of the existing load surveys shown in Table 2-4 reveals no relevance to

offshore platforms. Hence, a specific database was required for the purpose of this

research. Such database was collated in this research and is presented in Chapter 5.



2.8.3. PROBABILISTIC MODEL IN ASCE STANDARD

Live load provisions in many building codes are found to correspond approximately

to the mean or a percentile of the lifetime maximum value (Ellingwood and Culver,

1977; McGuire and Cornell, 1974).

As such, it can be used with Load and Resistance Factor Design (LRFD) or

Working Stress Design (WSD) code provisions without the need to conduct

additional sophisticated numerical analyses.

Since all live load surveys are conducted over a short period of time, i.e., on an

arbitrary point-in-time basis, the statistics of the extremes have to be derived

analytically. The issue then was to relate the statistics (mean and standard

deviation) of instantaneous intensity of sustained live load to those of lifetime

maximum values.

Using the survey data for buildings structures, the magnitude of live loads in

structural codes such as ASCE Standard 7-05 was derived employing a probabilistic

model to the survey database.

The use of probabilistic approach provided a logical framework for incorporating

the effect of randomness in the magnitude and placement of individual live loads.

The code design load concept is based on the total mean lifetime maximum load

effect, which considers that live loads vary in time and space in a random manner.

This means that the load was represented as stochastic processes to determine

OALL, which will in turn be used to produce (statistically) the same load effect on a

structural member as the actual random set of loads.

Melchers (1999) described two probabilistic approaches to the “stochastic” or time-

dependent random variables. The first approach is termed the classical (also called

time-integrated) approach and the second approach is termed first-passage (also

called time-dependent) concept which is more general than the classical approach.

The first passage approach was used during the 1970s to describe spatially

distributed floor live loads and derive EUDL for various buildings. Examples of

such models are included in the research work by Pier and Cornell (1973), McGuire

and Cornell (1974), Ellingwood and Culver (1977), Corotis and Doshi (1977), Chalk

and Corotis (1980) and Wen (1979). In those live load models, the spatial

variations were assumed homogeneous in a first approximation. The variation in



time is divided into two components, sustained load and transient (also called

extraordinary or intermittent) loads. Figure 2-6 illustrates the various loading types.

The sustained load includes furnishings and personnel normally found in buildings

and it is the load that is usually measured in live load surveys. The load magnitude

according to the model represents the time average of the real fluctuating load.

Sustained loads are spatial in character and remain fairly constant until a load

change takes place usually at Poisson arrivals such as at the beginning of a new

tenancy (Andam, 1986). These weights are usually recorded in live load surveys.

The extraordinary load represents all types of live loads which are not covered by

the sustained load. The extraordinary load is usually associated with special events

that lead to high concentrations of people, although it may be due to stacking of

furniture or other items. In general, transient loads have shorter duration than the

sustained load. Many Authors (Andam, 1990; Asantey and Andam, 1996; Chalk

and Corotis, 1980) regarded transient load data as very rare and considered it

unavoidable to adopt standard data.

The total load history is the sum of the two load components and its maximum

represents the largest load that occurs on a given floor area during the lifetime of the

structure.

2.8.4. APPLICABILITY OF PROBABILISTIC MODEL TO

OFFSHORE STRUCTURES

The nature and the characteristics of loading on platform open deck areas are

different to buildings and offices in various ways. Firstly, histograms produced

from existing office and building load surveys placed equal weight on all values of

the variables and averaged the loads per room (Corotis and Doshi, 1977). However,

on platform decks, heavier pieces of equipment on a platform produce the governing

load effects on piles. Consequently, the use of average loads to develop OALL was

considered to be unsuitable for the purpose of this research. Secondly, unlike live

loads in offices and buildings, which are generally spatially random, OALL deck

loads are generally carried out in designated areas which are termed open areas.

Consequently, the rationale behind the reduction according to area that is commonly

used in existing models may not be applicable to offshore platforms.



Thirdly, transient loads do not apply to offshore platforms. Andam (1990) explained

that transient load comprised of three forms: emergency, meeting and redecoration.

The emergency form results from the crowding of people in emergency situations.

The meeting form is due to assembly of an abnormally large number of people for a

meeting (such as an open day or a party). The redecoration form models the

stacking of furniture so that redecoration can take place or at the time of occupancy

changes.

Consequently, the first passage approach used in live loads in existing building

codes and standards such as ASCE Standard 7-05 is not suitable for open area live

loads on offshore platforms. An alternative probabilistic model was required to

derive OALL. The development of OALL is covered in Chapter 5 of this thesis.

2.8.5. REDUCTION FACTORS IN ASCE STANDARD

When a specific value of OALL is established, another question that is commonly

posed relates to the reduction in live loads due to the effect of multiple floors or

large open areas. The various load reduction factors currently recommended by

different standards are used in design of building structures. These load reduction

factors were tracked back almost 60 years ago where it was determined (Dunham,

1946), somewhat subjectively, from load survey data gathered in two federal office

buildings. While it is generally accepted that some reduction in live loads is

permitted, there is considerable uncertainty as to the level of reduction that can be

considered for offshore piles. The Australian Standards AS/NZS 1170.1 (2002)

allows 20% reduction in live loads for design of building columns supporting

tributary area of 35m2, increasing to 50% for areas above 200m2.

Such reduction becomes questionable when applied to offshore piles. Det Norske

Veritas (DNV) rules for fixed offshore platforms make no allowance for reduction,

while API RP2A-LRFD (1993) recommends a 60% carry-down factor.

Another interesting aspect is the reduction of drilling and supply loads during storm

conditions. Eri et al. (1977) stated that DNV found it somewhat disturbing that

philosophies vary greatly, with one company claiming the feasibility of a 30%

reduction based on “previous experience”, which, after some investigation, was

found to be improperly documented.



For that particular platform in question, the reduction amounted to a considerable

3000 tons. After meticulous investigation into the subject, DNV found no reason to

approve the reduction.

Eri et al. (1977) reported that DNV was skeptical to the use of this reduction and

considered that “a reliable proof appeared virtually impossible to obtain”.

Consequently, there was a need to develop live load reduction factors that

correspond to the required OALL. The treatment of live load reduction factors is

covered in Chapter 5.

2.9. CHARACTERIZATION OF THE CLIMATOLOGY IN

THE ARABIAN GULF

API RP2A-LRFD (1993) has been developed for the Gulf of Mexico (GoM) and US

waters on the basis that extreme storm conditions dominate the failure mechanism in

these regions. The dominance of extreme storm conditions on the failure

mechanism is a result of the large physical uncertainty in extreme storm loads.

Failure incidents in the Gulf of Mexico support this presumption. Botelho et al.

(1994) reported the toppling of 10 major platforms and 25 satellite platforms

(mostly caissons) during the period of August 24-26, 1992 when Hurricane Andrew

moved through the Gulf of Mexico with sustained winds of 140 miles per hour (62

m/s). In addition, Andrew caused significant damage to another 26 platforms and

140 satellite platforms. In 2005, the devastations caused by hurricane Katrina

provided another reminder of the dominance of extreme storm conditions in the Gulf

of Mexico.

However, the Author’s experience in reassessment of a large number of offshore

platforms in the Arabian Gulf has demonstrated that extreme storm conditions do

not govern the integrity of those platforms. Hence, there was a need to explore the

climatology in the Arabian Gulf with an objective of determining the dominant

failure mechanism in the Arabian Gulf.

The climate of the Arabian Gulf is distinguished by two well-defined principal

seasons separated by two transitional periods, and reflects the general pattern of

barometric pressure distribution over the Arabian Gulf. The principal seasons are

winter and summer. Winter extends from December through March with relatively



mild weather, but most of the annual amount of precipitation falls during these

months. In the winter, a trough of low pressure extends from the Gulf of Oman

along the Iranian coast maintaining north-westerly winds over the area during much

of the season.

Whilst winter is mild, it is characterized by the frequent passage of “traveling”

depressions moving from the Mediterranean along the Iranian mountains and south-

eastwards down the Arabian Gulf. These often interrupt the settled weather and

bring sudden changes. Their approach is heralded by southeast winds which may

reach gale forces at times and their arrival is often preceded or accompanied by

clouds and sometimes by rain and thunderstorms. With their passage, fresh or

strong north-westerly winds known locally as “Shamals” set in. These Shamals may

persist for several days often reaching gale force and are accompanied by

widespread sandstorms or dust storms and rough seas. In addition, characteristics of

the winter weather in the region are strong and gusty north-easterly winds, locally

called “Nashi”, which occur at times. Such winds do not travel along the Gulf as

“Shamals” but are more localized. Summer lasts from June to September and is

characterized by hot weather and almost permanently cloudless skies. In summer,

the atmospheric pressure is normally low over Iran but higher over Arabia.

Metocean criteria were retrieved using several records which were analyzed and

presented in confidential reports for the Arabian Gulf. The reports present extremes

of winds, waves, currents and associated periods for various water levels in different

directions.

The records contained in those confidential reports were obtained from several

sources, including:

• Observation of wave height, period and direction made by ships on passage or

voluntary observing fleet (VOF). The observations were collected and

processed by national meteorological services under the auspices of the World

Meteorological Organization (WMO) and entered into logbooks. The data were

archived and made available for analysis and are fully described in Shearman

(1982). The VOF data represent long term climatology.

• Satellites with radar altimeters (Geosat; ERS-1; TOPEX) at different periods

(November 1986 to December 1989; April 1992 – October 1993; October 1992

to July 1996).



• Monthly frequency tables of wind speeds and directions from the UK

Meteorological Office (UKMO) for the period 1986 to 2000.

• 3-hourly records of wind speeds and directions at different locations in the

Arabian Gulf from January 1988 to December 2000 from the Department of

Civil Aviation and Meteorology in Qatar.

• Wave data recorded by Hunting Survey Report from September 1985 to

February 1987.

• Proudman Oceanographic Laboratory (POL) Arabian Gulf Model of tidal level

and current in the form of 1 year of hourly tidal current predictions.

Analysis of the data and derivation of metocean criteria is presented in confidential

reports. Some measurements from wave rider buoys were used to adjust the data

before finalizing the criteria.

The metocean criteria were derived by analysis of independent maxima extracted

from samples of data. The distribution independent maximum (e.g. the maximum

wind recorded in each storm over a number of years) was fitted with an extreme

value distribution and this was used for the estimation of extreme values. Results of

the analysis and recommended metocean criteria are shown in Table 2-5.

In the deeper water of the North Sea and the Gulf of Mexico, wave height depends

on wind speed and on the distance (fetch) and duration over which the wind blows.

For example, tropical cyclones in the Gulf of Mexico are caused by winds which

grow with great velocity and generate ferocious and violent seas. Each year, around

100 tropical disturbances develop over the Atlantic Ocean. About 25 of these

disturbances develop into tropical depressions, of which 10 become tropical storms,

5 become hurricanes and 2-3 are likely to strike the US coast (Kaiser and Pulsipher,

2006). Storms that grow into 75 mile per hour sustained winds are classified as

hurricanes. Hurricanes are characterized by pressure, wind speed and storm surge.

However, there is not a one-to-one relationship between these elements, so

maximum wind speed is typically used to establish the so-called Saffir-Simpson

category as shown in Table 2-6.

A comparison of the maximum wind speeds for the Arabian Gulf shown in Table

2-5 against those categories shown in Table 2-6 reveals the benign environment in

the Arabian Gulf. Unlike deep waters, waves interact with the seabed in the shallow

waters of the Arabian Gulf resulting in a slow down and loss of energy and



consequently a reduction in wave height. Further, large ocean swells are prevented

from entering the enclosed Arabian Gulf area.

As a result of the benign environment in the Arabian Gulf, one of the objectives of

this research was to examine the reliability of offshore platforms in the Arabian Gulf

under environmental conditions. This is covered in Chapter 6 of this thesis.

2.10. SUMMARY

This Chapter identified and elaborated on a number of issues related to components

addressed in the course of reassessment of piled foundations of existing platforms.

In-depth analysis of those issues revealed gaps in the body of knowledge worthy of

research. A summary of those issues are identified in this section.

2.10.1. CODE TO BE USED IN CALIBRATION

This Chapter evaluated methods used in reassessment of existing platforms and

concluded that the design level check method is most suitable for use in industry

practice, while reliability-based and probabilistic methods would be most

appropriate to develop deterministic parameters.

The design level check method can be carried out using WSD or LRFD methods.

This Chapter shows that the LRFD method is appropriate for development of

deterministic parameters.

2.10.2. AXIAL PILE CAPACITY IN CARBONATED SOILS

This Chapter identified that API RP2A-LRFD (1993) provides no guidance to

predict the axial capacity of piles driven in carbonate soils, a key issue in the

Arabian Gulf. The need for guidance stems from poor foundation performance in

carbonate soils and the financial consequences of the remedial measures.

To predict axial capacity of piles driven in carbonate soils, some design methods

have evolved but these remain highly site specific and dependent on local

experience. The range of opinions on limiting soil parameters (shaft resistance and

end bearing) is so wide that use of a single objective value for each parameter is

likely to be either unconservative or uneconomical.

The characteristics of carbonate soils differ between geographic locations so there



was a need to define limiting soil parameters for the Arabian Gulf region.

Most researchers recommend the use of site-specific loading tests to avoid costly

foundation installations problems and the frequent remedial strengthening

experienced in these soils. However, site-specific loading tests offshore are very

expensive and are sometimes impractical.

Hence, the need to develop specifications addressing axial pile capacity in carbonate

soils was underlined and is presented in Chapter 4 of this thesis.

2.10.3. TARGET RELIABILITY LEVEL

This Chapter revealed that the selection of target reliability level has been a subject

for debate and highlighted the need to specify target reliability levels for the purpose

of this research. It was necessary for the specified target reliability levels to be

compatible with existing practice. The selection of appropriate target levels for this

research is presented in Chapter 4.

2.10.4. OPEN AREA LIVE LOAD (OALL)

Reassessment of existing platforms requires quantifying OALL to enable calculation

of load effects on piles. This Chapter revealed lack of specifications to determine

live loads on open areas on offshore platforms. To overcome this limitation in

current international codes and standards and guide the research efforts, this Chapter

provided a historical background describing the process used by various researchers

and code committees to develop live loads in existing codes and standards. Similar

process was adopted in this research and is described in Chapter 5.

2.10.5. DOMINANT FAILURE MECHANISM

The guidelines for reassessment of existing offshore structures contained in Section

‘R’ of API RP2A-LRFD (1993) attend to the effect of extreme storm loads only.

However, the Author’s experience shows that gravity conditions dominate the

failure mechanism in the Arabian Gulf. Chapter 6 presents an investigation of the

dominant failure mechanism in the Arabian Gulf using a rational approach.

CHAPTER 2: RESEARCH ISSUES


Table 2-1: Implicit target reliability levels βT in various structural codes and standards (Bhattacharya et al., 2001)

Probability of Failure

Standard Remarks βT 30-yr life

Lfp Annual

afp

Failure Type

Gravity (D + S + L) 3.0 2.0*10-3 6.7*10-5 Component

Gravity + Wind 2.5 8.0*10-3 2.7*10-4 Component AISC LRFD

Gravity + EQ 1.75 6.0*10-2 2.0*10-3 Component

Implicit 5.9*10-5 Component API RP2A

4.2*10-6 System

Implicit 3.5 4.0*10-4 1.3*10-5 Component AASHTO LRFD 5.5 3.3*10-8 1.1*10-9 System

Great Risk to life or environment 3.5 4.0*10-4 10-5

Small risk to life or environment 10-4 CAN/CSA

Impaired function only 10-3

Eurocode Normal distribution 3.5 4.0*10-4 1.3*10-5

Dangerous 10-6 CIRIA

Onshore 10-6 to 10-7



Table 2-2: Load statistics for ANSI A.58 load code (Reference: Galambos et al., 1982). The subscript n denotes nominal values

Load Type Mean COV Distribution

Dead load 1.0 Dn 0.10 Normal

Live Load 50 year lifetime value Ln 0.25 Type I

Arbitrary point-in-time live load value

0.25 Ln 0.4 to 0.8 Gamma

Wind load 0.78 Wn 0.37 Type I

CHAPTER 2: RESEARCH ISSUES 50 50


Table 2-3: Limiting skin friction values derived from various static pile loading tests in sand. The tests were conducted during the 1970s and 1980s in various locations around the world

Peak Values

Range Mean

Standard Deviation COV

Reference Year No. of Tests

kPa kPa kPa

Angemeer et al. 1973 7 9.2-18.3 13.4 3.3 0.25

Angemeer et al. 1975 1 33.1 - - -

Hagenaar et al. 1981,2 5 16.7-22.5 20.3 2.2 0.11

Dutt and Cheng 1984 12 9.8-18.2 13.3 2.5 0.19

Dutt et al. 1985 4 9.5-17.3 - - -

Gilchrist 1985 4 11.5-21.0 17 4.3 0.25

Nauroy&Le Tirant 1983 1 1.0 - - -

WOPHHZ

Rectangle



Table 2-4: Result of surveys of instantaneous sustained loads for occupational groups which was used to derive the design live loads in ASCE Standard 7-05 (Chalk and Corotis, 1980)

Occupational Group Total Surveyed Area in ft2 (m2)

Offices 3,800,000 (353 031)

Offices lobbies 17,000 (1 580)

Residences 204,000 (18 952)

Patient rooms 79,000 (7 339)

Hospital surgeries 34,000 (3 159)

Health clinics 173,000 (16 072)

School classrooms 31,000 (2 880)

Libraries – stack rooms 6,000 (557)

Hotel guest rooms 670,000 (62 245)

Warehouse and storage 197,000 (18 302)

Industrial heavy 74,000 (6 875)



Table 2-5: Metocean criteria in the Arabian Gulf for the worst conditions from the northwest called “Shamal” (Source: Basis of Design Documents in the Arabian Gulf and from confidential reports). The 100 year return period wave was calculated using extreme value analysis and has a 1% probability of exceedance in every given year

Return Period (Years) Engineering parameter

100 50 10 1

One minute mean wind speed (m/s) 32.0 31.0 29.0 25.0

Significant Wave height (m) 5.4 5.2 4.7 3.8

Maximum wave height (m) 9.8 9.6 8.7 7.1

Surface current speed (m/s) 1.00 0.98 0.96 0.91

Crest to Crest Period (s) 9.9 9.7 9.1 8.2

Tidal rise (MHHW) (m) 1.3 1.3 1.3 1.3

Storm surge (m) 0.6 0.6 0.5 0.4



Table 2-6: Saffir – Simpson Hurricane Scale (Kaiser and Pulsipher, 2006)

Scale Number Category Winds m/s Damage

1 33 – 42 Minimal

2 43 – 45 Moderate

3 46-58 Extensive

4 59 – 69 Extreme

5 > 69 Catastrophic



Figure 2-1: A description of reassessment methods shows gaps in the body of knowledge in determining axial capacity of piles in carbonate sands, OALL on offshore structures and the effect of environmental loads on the dominant failure mechanism in the Arabian Gulf



Figure 2-2: The process of reassessment of existing platforms outlined in Section ‘R’ of API RP2A-LRFD (1993). The process requires attending to extreme storm conditions only and does not address other conditions such as accidental or operating conditions


CALIBRATION OF DETERMINISTIC PARAMETERS FOR OFFSHORE PLATFORMS IN THE ARABIAN GULF

Figure 2-3: Water depth profile in the Arabian Gulf showing that the maximum water depth is 100m


CALIBRATION OF DETERMINISTIC PARAMETERS FOR OFFSHORE PLATFORMS IN THE ARABIAN GULF

Figure 2-4: Installation experience in the Arabian Gulf and the Mediterranean showing free fall of a pile as evident from the zero blow count in the charts (Nauroy and Le Tirant, 1986)



Figure 2-5: Photo of an actual platform floor deck in the Arabian Gulf showing that the open area is mainly unloaded except for some pipes that are used as scaffolding for painting and maintenance works



Figure 2-6: A plot of load versus time showing the nature of sustained and transient (or extraordinary) loads and the total live load

CHAPTER 3: METHODOLOGY 60


Chapter 3.

METHODOLOGY

3.1. INTRODUCTION

Chapter 2 discussed a number of research-related issues and furnished background

for development of guidelines that can be used for reassessment of existing

platforms. This Chapter presents methodologies used in this research in order to

develop such guidelines.

3.2. OUTLINE OF THE METHODOLOGY

Using the conditions of the Arabian Gulf, which were outlined in Chapter 2, the

following elements were developed to provide specifications for reassessment of

existing platforms in the Arabian Gulf:

• Establishment of limiting engineering parameters that can be used to calculate

axial pile capacity in the Arabian Gulf;

• Calibration of axial pile capacity resistance factors;

• Development of OALL values;

• Investigation of live load factors; and

• Examining the dominant failure mechanism of offshore platforms in the Arabian

Gulf.

Figure 3-1 describes the tasks to develop items 1 to 4, while Figure 3-2 describes

development of item 5.

These developments were accomplished using established probabilistic and

reliability-based methods. In particular, the development of pile resistance factors

was conducted using a similar approach to the one described by Ellingwood et al.

(1982) to develop nominal member capacities. Firstly, the levels of reliability

implied by the use of the then current design standards and specifications were



estimated and developed by optimization such that the material-oriented

specifications had a reasonable choice of resistance factors φ to meet a target

reliability index β. Secondly, a format for the proposed criteria that balances

theoretical consistency and appeal with ease of use in practice was selected.

Thirdly, a set of load combination factors were selected in order to make it possible

for material specification writers to prescribe resistance criteria that would result, on

the average, in designs similar to those currently obtained. Lastly, calculation aids

were provided to enable material specification writing groups to develop resistance

factors corresponding to desired reliabilities without further computer operations.

The use of probabilistic and reliability-based methods required a database which

was developed in this research and is described in Section 3.3. The database

included over 400 pieces of equipment and installation record data for 138 piles and

Geotechnical data at 33 locations in the Arabian Gulf. The collected database was

employed in the calibration of live loads and axial pile capacities using the

methodology described in Figure 3-1.

Calibration of resistance factors for driven piles required calculation of bias factors.

The bias factor was obtained by dividing the predicted capacity over the “actual”

capacity of each pile. The capacity of each pile was predicted using the empirical

approach described in API RP2A-LRFD (1993) and presented in Appendix D. The

development of axial pile capacity was carried out using Wave Equation Analysis,

which is described in Appendix E. The statistical parameters of the bias factors

were derived and employed in First Order Reliability Method (FORM) to calibrate

the resistance factors.

The development of OALL utilized an equipment database that was collated from

the Arabian Gulf during the course of this research. The statistical parameters of the

equipment weights were calculated and used to compute the maximum live load on

a pile using the influence area concept. To derive the mean of the lifetime

maximum live load on a pile, extreme value analysis was implemented.

The effect of extreme storm loading on the implied risk level on offshore platforms

in the Arabian Gulf was examined using reliability analysis as described in Figure

3-2. The probability of failure under extreme storm conditions was compared to that

under operating overload conditions. The outcome defined the dominant failure

mechanism in the Arabian Gulf.



3.3. DATA COLLECTION AND GROUPING

A common need for calibration of codes and standards is a readily accessible

database. Essentially, every author who has presented recommendations for a new

or revised design method established a data bank and compared predictions made

using the new method with measured test results. For example, calibration of pile

resistance factors in API RP2A-LRFD (1993) employed a database collated by

Olson and Dennis (1982) for the Gulf of Mexico conditions. Consequently, it was

necessary to obtain a database that covers the Arabian Gulf conditions in order to

determine the required statistical values of parameters for that region.

3.3.1. CHALLENGES

A crucial limitation at the start of this research was lack of published data relating to

the Arabian Gulf conditions. Two databases were required for the purpose of this

research. One database was required to calibrate resistance factors for piles driven

in the carbonate soils of the Arabian Gulf, since the database used in the calibration

of API RP2A-LRFD (1993) excluded carbonate soils. A second database was

required to develop OALL since the calibration of live loads in ASCE Standard 7-05

was based on surveys with no relevance to offshore platforms.

Fortunately, the Author had the advantage of accessing a significant amount of data

through work in the offshore industry with an owner/ operator in the Arabian Gulf,

which enabled compilation of the required databases. The Author identified that

engineering calculations and installation reports of existing platforms would offer

the required source of data. Typically, those calculations and installation reports are

found in project dossiers.

Live load data were collated using equipment lists of more than sixty (60) platforms

and pile installation data were identified for 33 out of those 60 platforms. The

salient features of the platform structures are listed in Table 3-1, which identifies the

function, water depth, number of piles and jacket legs, pile diameter and pile

penetration for each platform. The database is formed from 138 piles.

3.3.2. SUB-GROUPING THE DATA

The use of a single set of data would provide consistency with the approach used in



calibrating axial pile capacity resistance factors in API RP2A-LRFD (1993).

However, inspection of the data revealed distinct advantage in subgrouping the data

into sub-populations. The question then was the determination of factors to consider

in stratifying the data and the extent of which the data could be separated, since sub-

grouping of the data could be influenced by a large number of parameters. For

example, the database could be grouped to account for platform configuration,

Jacket configuration (monopod, tripod, 4 or more piles), installation method (float-

over, lifting), supplementary pile driving methods and hammer type (impact or

vibratory). In this research, the data points were subgrouped but a limitation on the

number of subgroups was introduced.

3.4. STATISTICAL ANALYSIS

Calibration of deterministic parameters employed the reliability-based method,

which rely on availability of statistical parameters. The basic information required

in the statistical analysis is the probability distribution of the data and estimates of

its mean and standard deviation or coefficient of variation. This section presents the

approach used in the statistical analysis of the data.

3.4.1. DISTRIBUTION TYPE

Distribution types expressing input uncertainty can be derived from either statistical

parameters or subjective probabilities. When input factors are based on historical

data, the first approach is appropriate. In cases where input factors are not derived

from historical data or when the back-up data are not available, the analysis must

utilize subjective probabilities to describe input uncertainties.

The selection of a probability distribution must be based on a skewness of the

distribution (left, symmetric or right) and a variance or degree of uncertainty (low,

medium or high).

Once a distribution that can model the occurrence of the observed data is found, the

statistical parameters can be derived to perform a reliability analysis and to study the

effect of specified risk levels. Due to the nature of the loading process, including

the element of human control and the large number of variables (leading to a high

degree of uniqueness for any given circumstance), it is not likely that a single

distribution can be found to precisely predict observed values in a particular



situation. However, a distribution that provides a good model to the character of the

data was considered sufficient to provide load and resistance parameters that lead to

a realistic assessment of reliability.

In this research, the basic resistance variable was taken as the strength of the

structural element in question. The basic load variable was the load effect,

dimensionally consistent with the resistance. These can be used directly in the

reliability analysis when the failure criterion is formulated as a linear combination of

resistance and load variables.

3.4.2. DISTRIBUTION PROPERTIES

The properties of an underlying distribution, from which the data have been drawn,

are of interest. To infer the properties, two approaches may be employed, namely

nonparametric and parametric.

In the nonparametric analysis, no assumption is made regarding the distribution

from which the sample data has been drawn. The construction of histograms from

the sample data is a common form of nonparametric analysis. The sample mean,

variance and other statistics can be obtained from the data without reference to a

specific distribution.

Nonparametric analysis allows identification of the nature of the distribution from

which the data has been drawn without selecting one particular distribution. When

there is sufficient number of data points, representation of the distribution by a

histogram or with sample statistics can be quite helpful. The sample statistics are

estimates of random variables properties that do not require the form of the

underlying probability distribution to be known.

In many situations, the amount of data may be insufficient to construct a realistic

histogram with enough resolution to enable sample statistics. Such situations occur

frequently in reliability engineering. Under such circumstances, rank statistics

provide a powerful graphical technique for viewing the cumulative distribution

function (CDF). They also serve as a basis for the probability plotting for use in the

parametric analysis.

The Parametric analysis encompasses both the choice of the probability

distribution and the evaluation of the distribution parameters. Probability plotting is



used to provide parameter estimates and also aids as a visual representation of how

well the selected distribution describes the data. Construction of probability plots is

performed using rank statistics technique.

To employ this technique, the random variable is ranked in ascending order. The

cumulative distribution function (CDF) is then approximated at each value of x and

the CDF could reasonably be approximated as (Lewis, 1996):

( ) NiNixF i ....,,2,1, == Equation 3-1

Where F(0) = 0 if the variable is defined only for x > 0

If N is not a large number, say less than 15 or 20, the above equation may seriously

overestimate F(x) and can be improved as follows (Lewis, 1996):

( ) NiN

ixF i ....,,2,1,1

=+

= Equation 3-2

This quantity can be derived from a rigorous statistical argument and is known in

the statistical literature as the mean rank.

Another statistical argument that may be used to obtain a slightly different

approximation for F(x) is the median rank. The form of the median rank is:

( ) NiNixF i ....,,2,1,

4.03.0

=+−

= Equation 3-3

In practice, the randomness and limited amounts of data introduce more uncertainty

than the particular form that is used to estimate F (Lewis, 1996). For large number

of data, both expressions yield identical results for F(x) after the first few samples.

In this research, Equation 3-2 was used when ranking statistics was employed.

Using the ranked statistics, reduced variate can be obtained from a table of standard

normal distribution in Microsoft Excel by using the NORMINV function. The

reduced variate can be plotted against the data to derive the quantile plot. A straight

line is then constructed through the data and the distribution parameters are

determined in terms of the slope and intercept.

The goal of the quantile plot is to determine the values of parameters for a function

that best fits the database. Quantile plots visually portray the quantiles, or

percentiles, of the distribution of simple data. As sample sizes increase, the quantile



plot would more closely mimic the underlying population cumulative distribution

function.

The probability plot is one variation of the quantile plot, which is commonly used to

determine how well the data fit a theoretical distribution, such as Normal,

Lognormal or Gumbel. By expressing the theoretical distribution as a straight line,

departures from the distribution are more easily identified. Probability plots are

therefore plots of the quantiles of sample data versus the quantiles of the

standardized theoretical distribution.

3.5. APPLICATION OF RELIABILITY- BASED METHOD

Within the offshore industry, the term reliability has different interpretations. For

example, reliability of components (number of failures per year) is catalogued in

several databases such as Oreda offshore reliability handbook (1995). Structural

reliability analysis (SRA) is related to but distinct from the catalogued database in

that it applies a quantified probabilistic framework to evaluate the “probability of

failure” of a structure component or a system due to functional or other

superimposed loads. The term “failure” in this thesis implies system collapse

mechanism as opposed to local yielding or component failure.

3.5.1. CALCULATION OF PROBABILITY OF FAILURE

In structural reliability analysis (SRA), the structural resistance R is compared with

the applied load Q to provide a measure of the safety of the structure. For level 2

reliability, two measures of the distribution can be utilized. The first is a measure of

central tendency (mean or μ) and the second is a measure of variability (coefficient

of variation or COV).

The probability of failure is governed by the form of the probability distributions

and their separation as shown in Figure 3-3. The area of overlap between the two

curves provides a qualitative measure of the probability of failure and depends on

three factors:

• The relative position of the two curves: as the distance between the two curves

increases, the probability of failure decreases. The positions of the two curves

may be represented by the means (μQ or μR) of the two variables,



• The dispersion of the two curves: if the two curves are narrow, the overlap and

the probability of failure are small. The standard deviation (σR or σQ)

characterizes the dispersion of the two variables and

• The probability density functions (fR(r), fQ(q)) represent the shapes of the curves

for the resistance and load, respectively.

The equations for these distributions are then combined mathematically to derive

another equation which describes the distribution of possible outcomes.

This approach requires a description of the distributions as equations followed by a

method to combine distributions analytically as described below. However, the

information about the probability density function is usually difficult to obtain and

common practice is to formulate an acceptable design methodology using only the

information on the means and standard deviations as described in Section 3.4.2.

When the global load (Q) and ultimate structure resistance (R) are normally

distributed, it can be shown that a new variable, Z, can be defined, which is also

normally distributed and has the following description:

QRZ −= Equation 3-4

QRZ μμμ −= Equation 3-5

22QRZ σσσ −= Equation 3-6

Z

Z

σμ

β = Equation 3-7

The probability of failure may then be obtained from the β value using the following

relationship:

( )β−Φ=fp Equation 3-8

Where β = Safety index

Ф( ) = Standardized normal cumulative distribution function

When both global load (Q) and ultimate structure resistance (R) are lognormally

distributed, the expression for the safety index β is based on first-order second-

moment reliability methods (Thoft-Christensen and Baker, 1982) and is given by:



( )( )[ ]222

2

22

11ln

11

ln

QLQDR

R

QLQD

mean

mean

COVCOVCOV

COVCOVCOV

QR

+++

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

+

++

=β Equation 3-9

In using this equation, variables which are not lognormally distributed are

transformed into equivalent lognormal variables with the same values of density

function and cumulative distribution function at the design point. The

transformation can be carried out as follows (Thoft-Christensen and Baker, 1982):

2

21ln ξμκ −= Equation 3-10

⎟⎟⎠

⎞⎜⎜⎝

⎛+= 2

22 1ln

μσξ Equation 3-11

where μ = Mean of the normal variables

σ = Standard deviation of the normal variables

κ = Mean of the logarithms of the variables

ζ = Standard deviation of the logarithms of the variables

The reliability index can be related to the traditional working stress design (WSD)

with factors of safety, FS, through the following equation (McVay et al., 2000):

( )( )[ ]222

2

22

11ln

11

1ln

QLQDR

R

QLQD

QLQD

R

COVCOVCOV

COVCOVCOV

QLQD

QLQDFS

+++

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

+

++

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎟⎠

⎞⎜⎜⎝

⎛+

=

λλ

λ

β Equation 3-12

n

mR R

R=λ Equation 3-13

where Rmean = Mean of the resistance

Qmean = Mean of the load

COVR = Coefficient of variation of the resistance

COVQD, COVQL = Coefficient of variation of dead and live load effects, respectively



where λR = Bias factor for the resistance

Rm = Measured value of resistance = back-calculated capacity

Rn = Predicted capacity by API RP2A method

λQD = Bias factor for the dead load

λQL = Bias factor for the live load

FS = Factor of Safety

QD, QL = Nominal value of dead and live loads

λQD, λQL = Bias factor for the dead and live loads

COVR = Coefficient of variation for the resistance

COVQD = Coefficient of variation for the dead load

COVQL = Coefficient of variation for the live load

3.5.2. BAYESIAN UPDATE

One of the main difficulties, if not the main difficulty, with the use of failure

probabilities evaluated using the reliability-based method is the interpretation of the

result. Philosophers have struggled for decades over the question of what

probability exactly means, and this resulted in two philosophical schools in modern

theory. The first is based on a Frequentist Interpretation and the other is based on

a Bayesian Interpretation or degree of belief. These are also known as the

objective and the subjective interpretation, respectively.

Both approaches acknowledge that our limitations in sampling and measurement,

along with variability, result in uncertainty. Consequently, both methods approach

the problem of making inferences about unobservable quantities in different ways

both philosophically and mathematically. They converge to the same answer as the

amount of data increases (Dakins and Goodrum, 2004).

In the frequentist philosophy, a probability is an objective property of some event.

A structure with an annual failure probability of 0.01 cannot ‘fail’ by 1%; a structure

either fails or it does not fail. A frequentist interpretation implies that for 1000

nominally identical, but uncorrelated structures, on average 10 will fail in any year.

The frequentist interpretation of probabilities can be further subdivided into:

• Priori probabilities, e.g. games of chance - poker, roulette, baccarat, etc., where



the odds of an outcome can be derived exactly from a knowledge of the system;

and

• Empirical probabilities, where probabilities, or rather statistics, are obtained

from past data. Complete knowledge of the system or sample space rarely

exists, and such statistics must often be determined from sample data.

The Bayesian or degree of belief interpretation was named after Thomas Bayes

(1702 -1761), an English clergyman and mathematician. Bayesian interpretation is

applied to decision making and inferential statistics that deal with probability

inference using the knowledge of prior events to predict future events. Bayes first

proposed his theorem in his 1763 work, which was published two years after his

death, An Essay towards Solving a Problem in the Doctrine of Chances. Bayes'

theorem provides a mathematical method that could be used to calculate, given

occurrences in prior trials, the likelihood of a target occurrence in future trials. Any

evidence from the universe is considered as conditional to the priors. According to

Bayesian logic, the only way to quantify a situation with an uncertain outcome is

through determining its probability.

Bayesian interpretation of probability is epistemic – a degree of belief, as against the

frequentist tradition where it is a limiting ratio of a repeatable phenomena (Howie,

2002). In situations where it is difficult or impossible to obtain repeatable events

like undertaking offshore pile loading test, the probability formed will be a belief

about something that one is uncertain about (von Plato, 1994). This ‘degree-of-

belief’ interpretation is why the calculated probabilities are referred to as notional.

This belief in a Bayesian sense is based on prior information and conditional

evidence or statistically calculated posteriors that are reciprocal to priors and

conditional evidence (Zellner, 1987). In other words, prior information based

hypothesis and conditional evidence can through Bayes’ rule update the prior to

posterior hypothesis such that the posterior can be a new prior, which with further

conditional evidence will lead to subsequent updating (Lange, 1999).

The knowledge about an existing unique event may be more or less uncertain. It

may range from the purely subjective (i.e. professional judgment with no

qualification) to a classical case that reflects the degree to which available

information supports a given assumption. Such uncertainty may conveniently be

modeled in probabilistic terms. This type of model does not describe properties of



the event, but properties of the knowledge about the event.

The Bayesian interpretation has proved to be the most fruitful approach for

structural reliability analysis as it is possible to introduce model and statistical

uncertainties into the analysis. With the Bayesian interpretation, the evaluated

safety measure, or reliability, changes with the amount and quality of the

information on which it is based. Thus, rather than being a scientific approach

aiming at a description of the “truth of nature”, structural reliability theory is

considered to be a comparative tool; one of its main uses is in decision analysis.

The Bayesian updating procedure combines a “prior” distribution of the bias factor.

This research adopted historical records to represent the “prior” distribution and the

results of this research to represent the “likelihood” distribution. By combining the

two using standard Bayesian methods, the updated or “posterior” distribution of the

bias factor is obtained, which can then be used to calibrate the resistance factors.

The Bayes’ rule states:

( ) ( ) ( )( ) ( )∑ =

=

= ni

i ii

jjj

APABP

APABPBAP

1

Equation 3-14

where ( )BAP j = Posterior distribution on A

( )jABP

= Likelihood function of the data

( )jAP = Prior distribution on A, where A can take on a finite number of

values (the summation is over the possible values of A)

Bayes’ rule can be solved mathematically if the sets of probability distributions can

be used in combination with each other, which are referred to as conjugate

distributions.

The “posterior” distribution of the bias factor was obtained in this research using the

Bayesian Theorem of probability theory. Bayesian updating yields the mean and

variance of the updated (posterior) distribution according to the following formula

(Ang and Tang, 1975):

22

22

lp

pllpu σσ

σμσμμ

+

+= Equation 3-15



22

222

lp

lpu σσ

σσσ

+= Equation 3-16

where μ = Mean

σ = Standard deviation

subscript p, l & u = Stands for prior, likelihood and updated (posterior) estimate

3.6. PREDICTION OF AXIAL PILE CAPACITY

API RP2A-LRFD (1993) adopts an empirical approach to predict pile capacity

driven in “normal” soils. In the “empirical” approach, simplified models, coupled

with experience or judgment factors to the prediction of pile behavior, are used.

This research introduced modifications to the limiting engineering parameters

within the API RP2A-LRFD (1993) approach in order to account for the effect

of carbonate soils.

Numerous procedures have been suggested for the empirical approach which can

generally be divided into static and dynamic methods. Briaud and Tucker (1988)

presented several static methods for predicting the ultimate load of a pile, including

Coyle (Coyle & Costello, 1981), Alpha (Tomlinson, 1971), Beta (Burland, 1973),

Lambda (Vijayvergiya and Focht, 1972), Meyerhof (Meyerhof, 1976), Mississippi

State Highway Department (MSHD, 1972) and Schmermann (Schmermann, 1978)

methods.

Another approach that can be used to predict axial capacity of piles is termed

“engineering mechanics”. In this approach, the designer attempts to develop an

accurate model for the behavior of the whole system. Appropriate data are fed into

the solution algorithm and pile-soil response is predicted.

In addition to the analytical approaches, the capacity of piles can also be established

directly using field measurements employing a pile driving analyzer (PDA).

The empirical approach must be pursued vigorously because it deals with the

immediate need to find simple methods of analysis suitable for use in the immediate

term. It may uncover field effects that need to be taken into account in the

engineering mechanics approach. Appendix H presents details of the various

methods used to predict axial pile capacity of driven piles.



3.7. CALIBRATION OF PILE RESISTANCE FACTORS

The process of replacing the single factor of safety in the API RP2A-WSD method

with load and resistance factors in the API RP2A-LRFD approach is commonly

known as “code calibration” and includes the choice of a desired level of reliability

or “target reliability”.

Ellingwood and Galambos (1980) were probably the first to introduce rationally

calibrated load factors for buildings using First Order Reliability Method (FORM)

and available statistical data. Reliability-based code calibration has been formulated

by several researchers, such as Ravindra and Galambos (1978), Ellingwood et al.

(1980) and Rosenblueth and Esteva (1972).

Calibration of resistance factors may be performed using one or more of the

calibration methods:

• Calibration by professional judgment, which is a subjective method as it is based

on professional and personal judgment,

• Calibration by fitting with a working stress design (WSD), an example of which

is the use of the existing WSD method to calibrate LRFD factors. This method

results in resistance and load factors that provide the same results for LRFD as

for WSD. With this method, there is no advantage in using LRFD over WSD.

Situations and conditions that fall outside the database inherent in the WSD

method cannot be handled, and

• Reliability-based calibration, which uses the statistics of a given database to

calibrate the resistance factor. For this method, a set of target reliability indices

can be considered in the calibration of the resistance factors. This approach

produces factors that best represent the resistance of a structure and was used in

the calibration of API RP2A-LRFD (1993) as well as in this research.

Calibration of the load and resistance factors is based on the use of reliability index

β method, in which uncertainties are described by means and coefficients of

variation (standard deviation divided by the mean). The reliability index is a

measure of reliability and is related to the number of standard deviations that makes

the mean safety margin fall in the safe region as described in Figure 3-3.

A piled foundation was treated in this research as a structural element in the

calibration process in a lumped resistance model, which is a similar approach to that



used in the calibration of API RP2A-LRFD (1993).

As mentioned earlier, the API code committee utilized reliability-based methods to

calibrate API RP2A-LRFD (1993) and applied a lumped parameter to represent the

resistance. The use of a lumped parameter approach for the resistance side of the

LRFD equation arises, in part, from tradition. Historically, LRFD format was

introduced by committees that were concerned primarily with structural loading

(e.g., Allen, 1975; Ellingwood et al., 1980). In the calibration of loads, the rationale

for applying more than one load factor is that the uncertainties involved in

estimating dead and live loads are significantly different. The same situation applies

to foundation capacity where the uncertainties underlying different components such

as side resistance and tip bearing may be significantly different.

Therefore, separation of the various uncertainties and assignment of various factors

would be statistically more appealing. However, until sufficient pile loading tests

that show a breakdown of the side and tip bearing capacities are available, the only

method to calibrate resistance factors is to use one factor.

In this model, all uncertainties associated with the resistance are lumped into one

parameter. Those uncertainties include capacity prediction method, effect of

uncertainty in soil parameters, loading rate and post-installation consolidation

effects. Moses and Larrabee (1988) adopted this approach and so did ASCE Manual

and Report 74 (Task Committee on Structural Loading, 1991) with the aim of

preserving a common calibration scheme between the structure and foundation.

Hence, the resistance side was left as a generic lumped parameter that could be

easily tailored to suit the diverse strength formulae for different materials.

For a given target reliability index βT and considering only dead plus live loads,

Barker et al. (1991) and Withiam et al. (1997) established that the resistance factor,

φ, can be calculated as follows using the statistical parameters for loads and

resistance and assuming lognormal distributions:

( )( )[ ]{ }222

2

22

11lnexp

11

QLQDRTQLQD

R

QLQDQLQDR

COVCOVCOVQLQD

COVCOVCOV

QLQD

+++⎟⎟⎠

⎞⎜⎜⎝

⎛+

+

++⎟⎟⎠

⎞⎜⎜⎝

⎛+

=βλλ

λλλφ Equation 3-17



Where: βT = Target reliability level

λR, λQD, λQL = Bias factor for the resistance, dead and live loads

γQD, γQL = Load factors for the dead and live loads

COVQD,COVQL = Coefficient of variation of dead and live loads

QD, QL = Dead and live loads

It can be seen that the resistance factor is a function of load statistics, load factors,

resistance statistics, ratio between dead to live load and the target reliability index.

The above calibration procedure includes certain implicit assumptions regarding its

objectivity and variance with time. First, the calibration procedure is not a wholly

objective exercise. For example, the required degree of uniformity in the reliability

level is subjective as shown in Figure 3-4. The size of the calibration domains will

have to be reduced if minor deviations from the target reliability level cannot be

tolerated. In the extreme, there would be so many different domains and sets of

resistance factors that the analysis becomes impractical, although the ideal condition

of uniform reliability is achieved. Judgment is required in this regard to ensure that

the resulting format does not become overly cumbersome to use.

Another subjective aspect of the calibration procedure is the use of constraints to

ensure that the resistance factors emerging from the optimization process are

physically meaningful and not too different from those currently being used in

foundation design.

Thirdly, the time element of the loading is modeled (in part) using random variables

with extreme value distributions. In reality, the resistance of the structure is being

continuously degraded through corrosion, fatigue, wear and tear, abrasion or

erosion, denting and accidental damage. This continuing reduction in structural

resistance as the structure ages leads to an increase in the probability that the

resistance of the structure will be exceeded at some point and that the structure will

fail. However, assessment of time-variant reliability is more involved than time

invariant analysis. The problem can be analyzed using stochastic process theory, in

which case it is termed a time-variant reliability analysis. An alternative approach is

to use smaller intervals for the exposure period, such that the resistance can be

assumed constant over the interval, and the time effect on reliability can be

accounted for. Generally, in the case of corrosion for instance, it is necessary to



assume some function for corrosion rate with time - typically this is obtained from

field measurements of typical installations, experience or theory, and is uncertain.

For each interval the resistance can be assessed, and the reliability for a short

reference period, typically an annual period can be evaluated. That is, calculations

are repeated at increments of time with a reducing resistance.

In this research, the calibration assumed time-invariant resistance which is

consistent with assumptions made in the calibration of API RP2A-LRFD (1993).

3.8. DERIVATION OF OPEN AREA LIVE LOADS (OALL)

This research examined the approach used in ASCE Standard 7-05 to derive EUDL

for building structures and concluded that the first passage approach, described in

Section 2.8.3, is unsuitable for deriving OALL on offshore platforms and that an

alternative model was required.

According to Melchers (1999), perfect models are not possible owing to insufficient

data, imperfect understanding and the necessity to predict future loading. Melchers

(1999) considered that efforts spent on data collection of the loads and on load

modeling might be more productive than refinement of the reliability estimation

techniques. Further, because of the large degree of variability in the loads, Corotis

(1972) considered that highly sophisticated analysis was not warranted and utilized

a classical approach to show the influence of load location on the supporting pile.

Hence, it was sufficient to employ a classical approach to reflect the influence of

random loading on the supporting piles. Application of the classical approach

employed the influence surface concept (Corotis, 1972), and extreme value theory

(Ang and Tang, 1984) to develop the extreme axial load on the pile during its

lifetime.

3.8.1. INFLUENCE SURFACE CONCEPT

The influence surface concept is used to indicate the effect of a unit load, randomly

located anywhere on the deck, on the axial load of a pile (McGuire and Cornell,

1974, Corotis and Doshi, 1977). The influence area of the axial load on the pile is

four times the conventional tributary area. Influence surfaces for other

configurations (such as beams or the influence surface associated with the several

columns) are covered by Pucher (1977). This research is only concerned with



open area live loads on piles due to the uncertainty associated with axial

capacity of piles in carbonate soils.

Corotis (1972), McGuire and Cornell (1974), Corotis and Doshi (1977) and Chalk

and Corotis (1980) described application of the influence surface concept in

deriving loads on columns. Consequently, the influence surface concept was

utilized in this research to model the axial pile load effect.

The influence surface approach was derived from the Navier solution for plate

bending to determine the effect of substituting concentrated loads with equivalent

uniform loads. For a column and beam configuration, the influence surface of the

load on the pile axial loading is shown in Figure 3-5 (McGuire and Cornell, 1974,

Corotis and Doshi, 1977, Chalk and Corotis, 1980).

The load position with respect to the column influences the extent of loading

supported by the pile. Intuitively, a closer load to the pile has more significant

effect on that pile, and vice versa.

Corotis (1972) provided the following equation to describe the influence surface:

( ) ( )( )3232 2323, yyxxyxC −−= Equation 3-18

Where C(x,y) = Influence coefficient

x and y = Normalized spatial variables ranging from zero to one.

The normalized spatial variables of x and y can be obtained by dividing the

coordinates of each sector by the sector length or width. A sector area is assumed to

represent a random load. The locations of the force in terms of the normalized

coordinate pairs and the corresponding influence coefficients were computed as

described above.

3.8.2. EXTREME VALUE ANALYSIS

Statistics of the lifetime maximum load effect was of interest, as it represents the

load magnitude specified in design codes. In fact, the live load provisions in many

building codes are found to correspond approximately to the mean of the lifetime

maximum value (Ellingwood and Culver, 1977; McGuire and Cornell, 1974). The

lifetime maximum load effect on a pile was derived in this research using extreme

value analysis (Ang and Tang, 1984).



Statistically, the largest value of the pile axial load pertains to the maximum values

from a number of events. An event describes a survey of all loads on open areas at

any given time. Conceivably, if several events (sample of size n) were repeated,

other maximum (and minimum) values will be obtained; thus, the possible largest

value comprises populations of their own (Ang and Tang, 1984).

Consequently, the maximum extreme value of the column axial load may also be

modeled as a random value with respective probability distribution. Such

distribution and its associated parameters have special characteristics that are unique

to the extreme value. The asymptotic theory of statistical extremes as presented by

Ang and Tang (1984) was used to develop the required statistics (mean and

coefficient of variation) of the maximum axial load effect on a pile.

3.9. COMPUTER SOFTWARE PROGRAMS

To process the databases collected in this research, it was necessary to utilize a

number of computer software programs. This section provides a brief description of

those programs.

3.9.1. RISK ANALYSIS SOFTWARE - @RISK

Section 3.4 provided a background of the methodologies to derive statistical

parameters. @RISK is a commercially available software program which was used

in this research to calculate statistical parameters of the variables in this research.

@RISK was used to perform nonparametric analysis by fitting distribution to the

data. The software includes a “BestFit” module. BestFit is Windows-based

software, which finds the distribution that best fits the data from a set of 38 different

continuous and discrete distribution functions. The different functions modelled in

@RISK are shown in Figure 3-6.

Automatic goodness of fit testing displays the accuracy of BestFit's answers.

BestFit also calculates the standard distribution type and parameter values that best

fits the data.

@RISK software is comprised of three main components:

• @RISK Model window for listing inputs and outputs, viewing input

distributions, fitting distributions, and defining correlations. @RISK Model also



allows pop-up graphical definition of distributions for components of cell

formulas,

• @RISK add-in to Excel, which includes distribution functions, statistics

functions, output functions and simulation reports in Excel, and

• @RISK Results window for interactive graphs of simulation results, statistics,

data and sensitivity and scenario reports.

Each of the three components shares a common user interface including an

“Explorer-style” listing of simulation inputs and outputs and customizable toolbars

and icons.

3.9.2. APIPILE - PILE CAPACITY SPREADSHEET

Appendix D describes the API RP2A-LRFD (1993) method to predict axial capacity

of driven piles. Analysis of pile capacity for a large number of load tests is tedious

and there was the danger of occasional errors when hand analyses were performed.

To minimize these problems, a computer software coded APIPILE was developed

by the Author for this research. The program uses Microsoft Excel and adopts API

RP2A-LRFD (1993) empirical formulation. The computer program also performs

analysis for pile resistance to driving. A description of the required data under each

entry for a circular pile is listed in Appendix I.

3.9.3. GRLWEAP-WAVE EQUATION ANALYSIS PROGRAM

After the rationale of the WEA approach was recognized, several researchers

developed a software program using WEA. For example, the Texas Department of

Highways supported research at the Texas Transport Institute (TTI) in an attempt to

reduce concrete pile damage. FHWA sponsored the development of both TTI

program (Hirsch et al., 1976) and the WEAP (Goble and Rausche, 1976) which

stands for wave equation analysis program. FHWA supported the WEAP

development to obtain analysis results backed by measurements taken on

construction piles during installation for a variety of hammer models. The WEAP

program was updated several times under the FHWA sponsorship. Later, and based

upon assumptions of hammer efficiency and soil properties, GRL Inc. (Goble

Rausche Likins and Associates Inc., 1996) developed the software coded

GRLWEAP. GRLWEAP uses the wave equation method and takes into account



quake and the damping of the soil. GRLWEAP was used in this research to predict

axial pile capacity using blow count information.

3.9.4. STRUCTURAL ANALYSIS COMPUTER SOFTWARE

(SACS)

The computer software SACS, which was developed by Engineering Dynamics, Inc.

(EDI, 1996), was used to undertake a series of linear elastic analyses in addition to

the non-linear pushover analyses using its module COLLAPSE. The complete

description of the software utilities and capabilities is included in the software

Manual (EDI, 1996). The main compatible SACS files to run the analysis were:

• Model input file which contains the general information of the computer model

viz., the geometry, member sizes, materials, loads and load combinations and

analysis options.

• Pile Soil Interaction or PSI input file which was used to model the soil in the

form of P-Y, T-Z & Q-Z curves. Non-linear springs were modeled to support

the pile and the surrounding soil. The PSI input file was obtained from the soil

report.

The following SACS program modules were used in this research:

• SEASTATE module was used to generate the dead weight and buoyancy of the

modeled members and to compute the environmental loadings (wave, current

and wind) on the structure. The SEASTATE runs combined basic load cases to

form various load combinations required in the analysis.

• SACS IV which refers to three of the program modules of the SACS system,

namely the pre-processor, the solver and the post-processor modules perform the

general purpose static structural analysis.

• COLLAPSE was used to perform pushover analysis of offshore type platform

structures.

COLLAPSE employs basic energy variation principles. Forms of these methods

have been used as tools for the analysis of engineering structures of forces and

displacements for more than a century. For an in-depth description of the

COLLAPSE module formulation, reference is made to the theory Manual of the

program (EDI, 1996). This section provides a qualitative description of three



aspects of particular interest for validation and application in this research, namely

the elastic element, the plasticity model and the failure criteria.

The elastic element in this program follows an updated Lagrange (incremental-

iterative) procedure and uses a nonlinear Green strain formulation with the von

Karman approximation. Thus, the COLLAPSE beam element is valid for large

displacements, but restricted to moderate strains. The stiffness formulation of

COLLAPSE is derived from potential energy consideration. Total and incremental

equilibrium equations are established by taking the first and second variation of the

internal strain energy and the potential of the external work of the elastic beam-

column. The influence of axial force on the bending stiffness of the element is

introduced by the nonlinear terms in the Green strain formulation. The tangent and

secant stiffness matrices are then obtained by introducing interpolation functions for

the element displacements. The shape function in COLLAPSE is taken as the exact

solution of the 4th order differential equation for a beam-column. With these shape

functions, all integration in the element stiffness expressions is carried out

analytically, giving closed-form solutions of the nonlinear elastic stiffness matrix.

The plasticity model is represented by concentrated yield hinges, which reflect the

nonlinear material behavior. Hinges may be introduced at element ends and/ or

element midspan. The plasticity model is formulated in stress resultant (‘force’)

space based on the bounding surface concept. Two interaction surfaces are used,

one yield surface representing first fiber yield and one bounding surface

representing the full plastic capacity of the cross section. When the cross section is

loaded, the force point travels through the elastic region until it reaches the yield

surface. At this stage a yield hinge is introduced. When further loading takes place,

the yield surface travels with the force point, such that the force point stays on the

yield surface. This approach allows for an explicit formulation of the beam-element

stiffness matrix, including geometrical nonlinearities and nonlinear plastic behavior

with material hardening and gradual plastification of the cross section. The plastic

behavior of the member is thus defined by the (nonlinear) elastic stiffness, the strain

hardening and a parameter describing the elastic-plastic transition for each cross

sectional force component (axial force, bending moment etc.). COLLAPSE uses

self-equilibrating plastic forces to correct for the elastic stress distribution for each

sub area in addition to employing a kinematic strain hardening formulation with a

moving plastic surface.



Failure is predicted in SACS COLLAPSE module in accordance with recognized

failure criteria and design formulation. The formation of a yield hinge is not the

limit of the load-carrying capacity and structural failure is indicated once a sufficient

number of plastic hinges has formed to make a kinematic mechanism. The peak

force in the member P-δ behavior defines column buckling. The non-linear

formulations automatically calculate the total (1st + 2nd order) bending moments and

also include the effects of cross-section geometry, boundary conditions and loading.

3.9.5. PILE DRIVING ANALYZER (PDA)

PDA is a computerized system that applies Case Method (Goble et al., 1975)

equations on measured pile dynamic data in order to determine, among other

quantities, the ultimate bearing capacity of a pile.

The dynamic testing system consists of a minimum of two strain transducers and

two accelerometers bolted to diametrically opposite sides of the pile to monitor

strain and acceleration and account for non-uniform hammer impacts and pile

bending. The reusable strain transducers and accelerometers are generally attached

two to three diameters below the pile head. The data acquisition system, such as the

Pile Driving Analyzer (PDA), conditions and converts the strain and acceleration

signals to force and velocity records versus time.

The most useful and convenient quantities for measurement are force and

acceleration at the pile top. As the transducer is deformed by the passing stress

wave, signals proportional to the strain magnitude are generated. Acceleration

measurements can be made using any of a number of commercially available

accelerometers modified to be attached to the pile. The results of the measurement

activity are matching records of force and velocity along the pile in the ground. The

force is computed from the measured strain (ε) times the product of the pile elastic

modulus (E) and cross sectional area (A). The velocity is obtained by integrating the

measured acceleration record (a).

Prediction of the capacity of piles from PDA is carried out in several steps. First,

reasonable estimates of the soil resistance distribution and quake and damping

parameters are made. Then, the measured acceleration is used to set the pile model

in motion. The program then computes the equilibrium pile head force which can

be compared to the PDA determined force. Initially, the computed and measured



pile head forces will not agree with each other. Adjustments are made to the soil

model assumptions and the calculation process repeated.

The case shown in Figure 3-7 is a typical illustration of a free fall situation, where

the pile “runs” under the hammer blow. The free fall is characterized by a small

force and large velocity during pile installation.

3.9.6. CASE PILE WAVE ANALYSIS PROGRAM (CAPWAP)

The PDA data may be further evaluated by the rigorous numerical analysis program

CAPWAP to determine static bearing capacity and to distinguish between the toe

resistance and the distribution of the skin resistance along the pile (Hannigan, 1990).

In the CAPWAP model depicted in Figure 3-8, the pile is modeled by a series of

continuous pile segments and the soil resistance modeled by elasto-plastic springs

(static resistance) and dashpots (dynamic resistance).

The force and acceleration data from the Pile Driving Analyzer (PDA) are used to

quantify pile force and pile motion, which are two of the three unknowns. The

remaining unknown is the boundary condition which is defined by the soil model.

It is not possible to determine the soil response from the measured force and

velocity records. However, it is possible to analyze a pile under the action of either

the force or the velocity record with an assumed soil model. The other unused

record is then plotted and compared against an equivalent computed plot.

Differences between the measured and the computed curves lead an experienced

engineer to conclusions regarding the differences between the actual soil behavior

and the assumed set of soil parameters. The parameters may then be modified to

obtain a better match in a second iteration.

CAPWAP was written to facilitate this type of analysis. Soil reaction forces can be

accurately expressed as a function of pile motion only. It is generally assumed that

the soil reaction consists of an elasto-plastic component, and a linear viscous

component. In this way, the soil model has at each point three unknowns: the

ultimate static resistance, the quake or elastic soil deformation, and a damping

constant. An error minimization procedure is used to assess the differences between

the measured and computed curves, and quantify the sum of these differences with

the so-called Match Quality Number (MQN).



( )∑

⎭⎬⎫

⎩⎨⎧ −

=i

jmjc

Fff

ABSMQN Equation 3-19

Where fjc = Computed pile top variables at time step j

fjm = Measured pile top variables at time step j

Σ = Summation over a time period

Fi = Top force at the time of the maximum pile top velocity

Reducing the MQN to a minimum value subject to several constraints will result in a

unique solution.

In the CAPWAP matching process, the ability to match the measured and computed

waves at various times is controlled by different factors. Figure 3-9 illustrates the

factors that most influence match quality in a particular zone.

The assumed shaft resistance distribution has the dominant influence on match

quality beginning with the rise of the record at time t, before impact and continuing

for duration of 2L/C thereafter, where L is the pile length in meters and C is the

wave speed in m/s. This is identified as Zone 1 in Figure 3-9.

In zone 2, the toe resistance and toe model (toe damping, toe quake and toe gap) are

most influential in the wave match. Zone 2 begins where zone 1 ends and continues

for duration equal to the rise time (t) plus 3ms. During zone 3, which begins where

zone 1 ends and continues for duration of the rise time t, plus 5ms, the overall

capacity controls the match quality. A good wave match in zone 3 is essential for

accurate capacity assessments. Zone 4 begins at the end of zone 2 and continues for

duration of about 20ms. The unloading behavior of the soil most influences the

match quality in this zone.

With each analysis, the program evaluates the match quality by summing the

absolute values of the relative differences between the measured and computed

waves. The program computes a match quality number for each analysis that is the

sum of the individual match quality numbers for each of these four zones. An

illustration of the CAPWAP iteration process is shown in Figure 3-10.

Through this trial and error iteration adjustment process, the soil model is refined

until maximum agreement can be obtained between the measured and computed pile

head forces. The resulting soil model is then considered to represent the best



estimate of the static pile capacity, the soil resistance distribution, and the soil quake

and damping characteristics. In this example, the initial computation indicated a

value of 1667kN for the pile capacity, which was refined to derive a final value of

2187kN.

3.10. SCOPE DELIMITATION AND KEY ASSUMPTIONS

This research has similar limitations to those implicit in API RP2A-LRFD

formulation. Moses and Stahl (1998) discussed some of those limitations, including

load effect uncertainties, historical risk and cost of failure.

This research is applicable to reassessment of existing offshore structures located in

the Arabian Gulf. Developments in this research rely on the use of existing platform

data. The results may not be applicable for designing new platforms. Further, the

results of this research may not be applicable to other geographic locations around

the world since data collection only covered offshore platforms in the Arabian Gulf.

This research is limited in scope to changes from original design condition which is

described in Section ‘R’ of the API RP2A-LRFD (1993) as “reassessment

initiators”. Other changes in a platform such as fatigue or accidental damage are not

considered by API RP2A-LRFD (1993) under Section ‘R’ and similar approach is


An implicit assumption in the calibration of the resistance factors in this research is

that resistance remains constant for the life of the platform. Deterioration in the

capacity can be treated using physical surveys and updating of the reliability model

while human intervention through maintenance can be used to halt the deterioration.

A specific treatment of human and organizational factors is excluded from this

research. This follows a similar approach used to calibrate API RP2A–LRFD

(1993).

Piles can be installed using various installation methods and techniques, including

driving, jacking, drilling and grouting. In the drilled and grouted technique, the

steel pipe is grouted into pre-drilled oversize hole. Over the last two decades,

alternative construction methods emerged such as drilling the formation and then

pressure-grouting along the drilled lengths through pre-installed valves. However,

the majority of installations carried out in the 1960s, 1970s and 1980s used driven



piles. This research addressed the driving technique only because it represents the

most common technique used to install existing platforms in the Arabian Gulf.

In API RP2A-LRFD (1993), calibration of resistance factors for piled foundation

revolved around the selection of resistance factors that can be used with the ultimate

strength calculations. Further, those pile resistance factors were calibrated from API

RP2A-WSD without explicit reference to the loads, definition of the

characteristic/nominal soil parameters, the method of transforming soil parameters

to engineering parameters and the bias in the calculation methods (Kulhawy et al.,

2002). Similar approach was adopted in this research, except that the developed

parameters are calibrated to account for the specific geological conditions in the

Arabian Gulf.

3.11. JUSTIFICATION OF THE METHODOLOGY

This section justifies the use of various methodologies adopted in this research.

3.11.1. RELIABILITY-BASED METHOD

Despite the limitations associated with using the reliability-based method in

reassessment as discussed in Section 2.3.3, it was used in the calibration of codes

such as API RP2A-LRFD (1993). Hence, its use was justified in this research.

The use of the reliability-based method in calibration has the benefit of repeatability

and familiarity as long as the standards do not radically alter. When such alterations

are under consideration there is an onus on the standard makers to ensure that the

new product is soundly based. In addition, and despite being empirical, this

approach does possess a major advantage of keeping the new design methodology

compatible with the existing experience base. This approach is also consistent with

the evolutionary nature of codes and standards that require changes to be made

cautiously and deliberately. The simplicity of the method also permits a new format

to be readily fine-tuned to local conditions (Turner et al., 1992).

3.11.2. EXTENT OF THE DATABASE

The target population in this research is existing platforms in the Arabian Gulf.

Figure 3-11 shows one of the platforms used in this research. The database in this

research was undertaken using real data from actual offshore installations in the



Arabian Gulf. The extent of the database in this research was guided by databases

used to develop other codes such as API RP2A-LRFD (1993).

Calibration of the API RP2A-LRFD (1993) was based on 44 pile loading tests. By

comparison, the database in this research comprised 138 piles. Hence, the extent of

data was considered of appropriate size for the purpose of this research.

Similarly, the total surveyed area in this research was compared to those used to

develop ANSI A58, which is described in Table 2-4, and found to comparable. For

example, ANSI surveyed 20,400m2 to derive live loads for residential buildings and

67,000m2 to derive live loads for hotel guest rooms. By comparison, the surveys

conducted in this research covered approximately 35,000m2 of combined topside

deck area.

3.11.3. BAYESIAN UPDATING

Bayesian updating of the “prior” statistical parameters was employed in this

research. Sundarajan (1995) recognized that updating statistical parameters using

observations would be limited if the analysis deals with component risk while the

observations are actually system risk. In this research, both analytical calculations

and observations deal with component risk. Hence, the use of Bayesian updating

was justified.

3.11.4. WAVE EQUATION ANALYSIS METHOD

For the most part, wave equation analysis has been utilized to research potential

installation problems in the field and to select appropriate hammers. McClelland et

al. (1969) and Holloway et al. (1978) discouraged the use of the Wave Equation

Method to predict axial capacity of piles based on blow count information. A major

criticism of this approach had to do with the uncertainties regarding hammer and

cushion properties at the time of installation, uncertainty about the soil resistance to

pile driving, the viscous damping forces that are mobilized and the difficulty of

predicting factors affecting soil rheology (load deformation behavior) in the vicinity

of a pile, especially under dynamic loading conditions. Attempts to relate known

static resistance to the dynamic resistance phenomenon introduced additional

uncertainties into the analysis and further inaccuracies can enter the problem

through potential misrepresentation of the pile driving system in the mathematical



model. The shape of the impact stress wave transmitted to the pile is a prime factor

in the pile driving performance. Incorrect estimation of the actual hammer

efficiency in the field or improper representation of the mechanical process can lead

to significant prediction errors, except for low blow counts.

In spite of such criticism, pile-driving data (blow count versus depth) was used by

many researchers to predict the axial capacity of piles. Vijayvergiya et al. (1977)

utilized the wave equation results and blow counts before and after driving to

compute setup and estimate the static capacity in chalk. Agarwal et al. (1978) used

a similar concept to research soil setup effects in carbonate clays. Mothewell and

Husak (1982) utilized the wave equation method of analysis to conclude that the

lower driving resistance recorded in the field reflected the ‘true’ axial capacity.

Further, significant developments and correlation studies of the computer software

program GRLWEAP since the 1970s were able to circumvent some of the

inaccuracies in dynamic resistance idealization by relating a viscous damping force

to the static resistance by either correlating measured blow count with measured pile

capacity or by selecting damping parameters based on experience or from the

literature. Hussein and Rausche (1988) considered that the engineering mechanics

approach using a software coded GRLWEAP provided reliable results due to the

improved models for soil behavior and their ability to describe the process of pile

installation and provide means of estimating the relevant soil properties. The

availability of diagnostic tests in the laboratory and in the field validated the

reliability of the wave equation analysis approach.

Hence, evidence in the literature indicated practical significance of the wave

equation analysis approach which provided credence to its use in this research.

3.11.5. INFLUENCE SURFACE METHOD

To assess the accuracy of the influence surface model and justify its use in this

research, Corotis (1972) computed the theoretical column loading as a random

variable using the influence surface approach and compared this to the theoretically

correct approach, which considers the location of the load to be a random variable.

Corotis (1972) found that the contribution to the reduction in the coefficient of

variation as a result of lumping all loads within a section at the point of average

influence for that section to be small.



3.12. SUMMARY

Chapter 2 identified key issues to answer the research question and determined that

the development of guidelines for reassessment of existing platforms in the Arabian

Gulf required attending to the following topics:

• Calibration of axial pile capacity driven in carbonate soils,

• Derivation of open area live loads on platforms in the Arabian Gulf, and

• Determination of dominant failure mechanism in the Arabian Gulf.

This Chapter provided methodologies to address every issue.

Calibration of axial pile capacity in this research followed a similar approach to that

used in calibrating API RP2A-LRFD (1993).

The approach used to develop live loads in this research adopted a probabilistic

approach termed the classical method which is more appropriate to offshore

platforms than the first passage approach used in AISC Standard 7-05 to derive live

loads on building structures. The application of the classical method utilizes

influence surface method and extreme value analysis.

Establishment of the dominant failure mechanism in the Arabian Gulf required

calculation of probability of failure under extreme storm as well as operating

overload conditions. The probability of failure calculations relied on the use of

SRA.

Application of the methodologies described above employed a number of computer

software which were identified and described in this Chapter.



Table 3-1: Salient characteristics of platforms forming the pile database

SN Platform Type # Legs

# Pile

Year Depth m

Dia mm

Penetration m

1 Production Platform 8 8 1965 34 762 43

2 Wellhead Platform 3 3 1966 36 762 61 3 Wellhead Platform 4 4 2000 36 1219 52 4 Riser Platform 6 6 1982 19 1219 52 5 Compressor Platform 8 8 1978 20 1219 87 6 Wellhead Platform 3 3 1978 19 762 66 7 Wellhead Platform 3 3 1979 32 762 61 8 Wellhead Platform 3 3 1979 21 762 85 9 Glycol Generator 4 4 1978 20 762 72 10 Wellhead Platform 3 3 1980 39 762 57 11 Wellhead Platform 4 4 1991 29 914 50 12 Wellhead Platform 3 3 1993 22 762 60 13 Living Quarters 4 4 1978 20 762 80 14 Wellhead Platform 4 4 1995 21 914 66 15 Compressor Platform 8 8 1978 36 914 82 16 Wellhead Platform 4 4 1995 13 914 62 17 Glycol Generator 4 4 1978 36 762 63 18 Wellhead Platform 4 4 1997 30 1219 45 19 Water Disposal 4 4 1998 27 914 64 20 Wellhead Platform 4 4 1998 41 1067 51 21 Living Quarter 4 4 1978 36 762 67 22 Wellhead Platform 4 4 2000 25 1219 72 23 Wellhead Platform 4 4 2000 13 914 74 24 Wellhead Platform 4 4 1981 20 914 56 25 Wellhead Platform 4 4 1981 20 914 56 26 Wellhead Platform 4 4 2004 27 1219 56 27 Wellhead Platform 3 3 1992 35 762 84 28 Wellhead Platform 4 4 1981 36 914 62 29 Wellhead Platform 4 4 1999 36 1219 51 30 Riser Platform 6 6 1983 36 914 83 31 Wellhead Platform 3 3 1978 26 762 59 32 Wellhead Platform 3 3 1980 23 762 52 33 Wellhead Platform 3 3 1981 30 762 66

Number of Piles 138 138



Figure 3-1: Analytical approach used to calibrate pile resistance factors and OALL for the conditions of the Arabian Gulf. The calibration of OALL established the statistical parameters of the database and employed influence surface method and extreme value analysis to define a uniformly distributed load. Calibration of the pile resistance factors utilized a database to calculate bias factors and employed FORM to calibrate resistance factors for axial capacity of driven piles in the Arabian Gulf



Operating Conditions dominate the failure mechanism

Extreme Storm dominate the failure mechanism

NO

YES

Single Pile + Group / System Effect

Extreme StormPushover Analysis

Reliability AnalysisChapter 6

Pf Gravity>>>>

Pf Ext storm

Nonlinear Model DnV UltiGuide

Rreassessment of existing platforms considers

operating conditions only

Calibrate Environmental Loads or consider both

effects

STOP

Areas for Future Research

Mathematical Model

Statistical Parametersfrom Chapters 4 & 5

Calculate Probability of Failure

Reliability Analysis

GravityPushover Analysis

Probability of Failure under Operating Overload Conditions

Probability of Failure under Extreme Storm Conditions

Use similar procedure to that used in the GoM or the

North Sea

Figure 3-2: Flowchart showing the approach adopted in this research to perform reliability analysis on a representative platform from the Arabian Gulf with the objective of defining the dominant failure mechanism in the Arabian Gulf



Figure 3-3: Schematic showing the basis for calculating the probability of failure



Figure 3-4: An example of large scatter in a set of data which is intended for calibration. The chart shows that the calibration of a uniform set of factors requires the data to be sub-grouped. The number of subgroups can be increased without limits



X X

YY

Pile or Column

Beam

Influence Surface for pile/ column

Figure 3-5: Influence Surface for column axial load. Note that the influence area is 2X * 2Y (McGuire and Cornell, 1974) or four times the tributary area for a column



Figure 3-6: Distribution palette in @RISK enables a choice of distribution type that best fits the data being analyzed



Figure 3-7: Force and velocity fall measurements versus time for a free end condition. This illustration is typical for a free situation where the pile “runs” under the hammer blow. In the chart, A is the pile cross sectional area, E is the pile elastic modulus, C is the wave speed and F is the force generated at the impact surface of the pile (Hannigan et al., 1997)



Figure 3-8: Schematic of CAPWAP Analysis Method



Figure 3-9: Factors that have a dominant effect on the accuracy of CAPWAP prediction (Hannigan et al., 1990)



Figure 3-10: Schematic of CAPWAP analysis method showing CAPWAP iteration matching process (Hannigan et al., 1990). The trial and error iteration adjustment process results in refinement in the soil model to obtain the best agreement between the measured and computed pile head forces. The resulting soil model can then be considered to represent the best estimate of the static pile capacity. In this example, initial capacity (1667kN) was refined to derive a final value of 2187kN in step 5



Figure 3-11: One of the platforms used in this research. It functions as living quarters on the upper deck and process facilities on lower decks. The platform includes a helideck


Chapter 4.

CALIBRATION OF PILE RESISTANCE FACTORS

4.1. INTRODUCTION

The literature includes numerous publications investigating axial pile capacity in

carbonate soils in various parts of the world except in the Arabian Gulf. The results

of those publications cannot be directly applied to the Arabian Gulf conditions due

to the site-specific nature of the carbonate soils which preclude extrapolation of

results between geographic regions.

This Chapter describes a methodology to calibrate axial resistance factors for piles

driven in carbonate soils. The calibrated axial capacity factors may then be used in


4.2. CALIBRATION MECHANICS OF AXIAL PILE

RESISTANCE FACTORS

Calibration of resistance factors in this research followed a similar approach to that

used in the calibration of API RP2A-LRFD (1993). The calibration employed the

statistics of the bias factors. The bias factor was computed by dividing “actual”

capacity over predicted capacity for each pile. To calibrate resistance factors, the

First Order Reliability Method (FORM) was applied to the statistics of the bias

factors.

The prediction of pile capacities used the empirical formulation recommended in

API RP2A-LRFD (1993), which is described in Appendix E. However, API RP2A-

LRFD (1993) does not specify limiting soil parameters for carbonate soils. Hence, a

set of parameters was required for this research.

In the calibration of API RP2A-LRFD, “actual” pile capacities were derived using

loading tests. Survey of the literature revealed lack of pile loading tests in the

CHAPTER 4: PILE RESISTANCE FACTORS 103


carbonate soils of the Arabian Gulf. Further, it was impractical to perform loading

tests due to the prohibitive costs. Such tests would have to be carried out offshore to

capture the characteristics of the geological settings described in Section 2.7. Even

if tests were to be performed, the reported pile capacity would not be a unique

number because it depends on the interpretation method. There are various

interpretation methods such as Davisson and Decourt methods. Fellenious, (1980)

reported that the “actual” pile capacity using Davisson Method could be as much as

40% less than “actual” pile capacity using Decourt Method.

To derive “actual” pile capacities in the carbonate soils of the Arabian Gulf, this

research employed an analytical approach of the one-dimensional wave equation

analysis (WEA) to analyze installation records collated in the course of this

research. The analytical procedure considered appropriate parameters and model

effects of soil plug, soil resistance to driving profile and time effects. The analytical

procedure was validated using the results of a field dynamic pile monitoring.

The statistical parameters of the bias factors were then calculated and supplemented

using Bayesian updating method. As discussed in Section 3.5.2, Bayesian update

requires a prior distribution and a likelihood distribution to derive the posterior

distribution. The prior distribution was based on the statistical parameters used to

calibrate API RP2A-LRFD (1993). The likelihood distribution was based on the

bias factor statistics calculated in this research.

Calibration of resistance factors also required determination of target reliability

levels. An investigation of target reliability level used in the calibration of API

RP2A-LRFD (1993) was conducted and a set of target levels was determined to

meet the objectives of this research.

Using the posterior bias factor distribution and the selected target reliability levels,

resistance factors were calibrated using the First Order Reliability Method (FORM)

employing the methodology described in Section 3.7.

4.3. PILE INSTALLATION DATABASE

The calibration of the API RP2A-LRFD (1993) was based on 44 pile loading tests,

with only 20 test piles having penetration greater than 100 ft (30.5m). The database

was compiled by Olson and Dennis (1982) but it excluded carbonate soils.



Due to lack of pile loading tests in the Arabian Gulf, this research used pile

installation records collected in this research and discussed in Section 3.3. The data

collection efforts enabled a well-documented pile installation case worthy for use in

research work. The installation data for each pile include:

• Pile configuration and splice schedule,

• Pile penetration resistance versus penetration depth, including installation delay

records due to welding add-on and hammer and cushion changes,

• Details of the hammers used in the operations,

• Details of the pile and follower make-up,

• Compilation of Geotechnical and installation data including shear strength

profile, soil reports identifying engineering parameters, and

• Pile dynamic monitoring results of four piles.

Figure 4-1 provides an example of one pile installation record. The pile is

composed of three (3) sections. The lowest section is called “pile shoe”. Many

piles are installed with an internal driving shoe to reduce internal skin friction.

Comparative data of piles in clay with and without a shoe indicate that an internal

shoe can reduce the driving resistance and the extent to which the pile plugs during

driving (Heerema, 1979). Opinions vary regarding the reduction in internal skin

friction caused by a shoe during continuous driving. Some believe that an internal

shoe completely eliminates internal skin friction in stiff clay whereas others assume

reductions of 30 to 50 percent (Toolan and Fox, 1977; Durning and Ernie, 1978;

Heerema, 1979).

Figure 4-1 also represents the driving record for that pile, which is usually

completed by the installation contractor during the installation campaign of any

offshore platform. During driving operation offshore, the installation personnel

record the number of blows for every foot for pile penetration into the soil together

with the time at the start of the driving operation. If there is a requirement to stop

the driving, the number of blow counts is recorded at the end of driving together

with the time at which the driving stopped. At the restart of driving operation, the

time and the initial number of blow counts are also recorded.

An example of the first sheet of a PDR for one pile is shown in Table 4-1, which

includes the date of installation, platform and pile identification, water depth,

hammer type and location of installation in latitude and altitude. Table 4-2 shows



an intermediate sheet in the PDR and Table 4-3 shows the last sheet of the PDR.

The columns showing “penetration depth” and “blow count” formed the required

data for this research.

The first sheet of the PDR shows the blow count at each penetration and the hammer

type (3000/150) used at the start of the driving operation and time of starting the

driving operation (10th March 1978) at 5:00pm. The intermediate (pen-ultimate)

sheet of the pile driving record (PDR) provides part of the history during the driving

operation and shows that the operation was stopped when the penetration reached

201 ft at 5:04 am and restarted on the 22nd March 1978 at 11:53 pm. This sheet also

indicates a change in the hammer type (4600/150) from what was used at the start of

the driving operation (3000/150). The last sheet of the PDR indicates that the final

penetration depth was 262 ft (79.9m) and was achieved with 25 blow counts per

foot. This sheet also shows that the target penetration was achieved at 6:12 pm on

the 23rd March 1978, which means that the driving operation for this pile took

around 2 weeks to complete (from 10th March 1978 to 23rd March 1978).

The delay time, type of hammer and cushion, pile make-up and number of hammer

blows per foot of pile penetration – at end of driving (EOD) and beginning of

restrike (BOR) - were noted in the pile driving records.

Appendix B includes a summary of the final blow count for each pile in the

database. The soil layers for each pile are shown in Appendix C.

4.4. PREDICTED AXIAL PILE CAPACITY

The prediction of the capacity of each pile in the 138 pile database was carried out

using the APIPILE spreadsheet described in Section 3.9.2. However, Section 2.7

revealed that API RP2A-LRFD (1993) excludes the use of its limiting soil

parameters to predict the axial capacity of piles in carbonate soils. Hence, there was

a need to define limiting engineering parameters that can be employed in this

research to calculate axial capacity of piles in carbonate soils.

4.4.1. LIMITING ENGINEERING PARAMETERS

For engineering practice, researchers identified various limiting friction and bearing

values for driven piles in carbonate sands (McClelland, 1974, Agarwal et al., 1977,

Datta et al., 1980, Beringen et al., 1985, Denis and Olson, 1983).



Table 4-4 presents suggested limiting parameters by a number of authors.

McClelland (1974) suggested limiting value of 20kPa for the shaft resistance and

5MPa for the end bearing, but cautioned that generalization of results was highly

speculative. Agarwal (1977) recommended an increase to the values suggested by

McClelland, but only for high percentage of carbonate content, while Datta et al.

(1980) suggested limiting values of 15kPa and 3MPa for friction and end bearing

respectively for piles driven in uncemented carbonate sands. For cemented

carbonate sands, Datta et al. (1980) considered that a more precise statement of

appropriate magnitudes of engineering parameters was not possible, primarily due to

the absence of a factor which can give a quantitative idea of the degree of

cementation.

The limiting unit skin friction values (2.9kPa to 28.7kPa) shown in Table 4-4 are

similar to the results from pile loading tests (1kPa to 33.1kPa) shown in Table 2-3.

This apparent agreement may actually be a causal effect rather than a confirmatory

analysis.

Other Authors proposed specific analysis and design methods but most qualified the

results to pertain to a particular site. Beringen et al. (1982) recommended specific

tests such as CPT, while others (Datta et al., 1980, Dutt and Moore, 1985) focused

attention on particular soil properties such as cementation or compressibility

(Nauroy and LeTirant, 1983, 1985). Datta et al. (1980) proposed that more

emphasis be placed on the particle nature and suggested that the behavior of

carbonates with few intraparticle voids, which are therefore less crushable, approach

the behavior of non-carbonate soils.

Many of the recommendations for current design practice in carbonate cohesionless

soils are based on the carbonate content and on the compressibility of the soil.

Nauroy et al. (1996) demonstrated that limiting values of unit skin friction depend

on the compressibility of the material and the type of pile (open-ended or closed-

ended, unplugged or plugged) and the compressibility index which can be obtained

from the results of an odometer test. The odometer test is ideally performed on

intact undisturbed material which requires good quality samples at the site

investigation stage. Tirant et al. (1983) presented typical ranges of compressibility

indices for siliceous sands and for detrital and bioclastic carbonate sands and

recommended limiting values as a function of the limit compressibility index.



In carbonate clays, it is unclear whether the carbonate content has a beneficial or

adverse impact on the properties of the clay. Agarwal et al. (1977) stated that

“carbonate content in clay appears to have beneficial effects on strength properties”

whilst Nauroy and LeTirant (1985) contends that “the skin friction of driven piles in

fine carbonate soils is probably lower than observed in non-carbonate cohesive soils

of the same undrained shear strength”. For calculating the skin friction of driven

piles in cohesive carbonate soils, Nauroy and LeTirant (1985) recommended that the

lowest of five methods (API RP2A α-method; API RP2A method 2; Semple and

Ridgen method; Randolph and Murphy method; λ method) be used for prediction of

axial capacity of piles in carbonate soils. Based on field and model tests on piles in

carbonate soils, Nauroy and LeTirant (1985) also suggested an inverse correlation

between shaft friction and compressibility index.

In 1999, Alba and Audibert reviewed the research and developments that have taken

place over the last thirty years and acknowledged that susceptibility to crushing and

degree of cementation govern pile soil interaction, but conceded that these

parameters are the most difficult to quantify.

In 1999, Kolk compared laboratory data with results from full-scale pile loading

tests and examined current analytical methods for driven piles in carbonate soils. In

the absence of compressibility data, Kolk (1999) defined the limiting skin friction

and limiting end-bearing values by the carbonate content of the material as follows:

( ) ( )4log20

log80,,,lim,

⎟⎠⎞

⎜⎝⎛

×−−=

CC

ffff ssississ Equation 4-1

( ) ( )4log20

log80lim

⎟⎠⎞

⎜⎝⎛

×−−=

CC

qqqq sisi Equation 4-2

Where: q80 = 3MPa is the limit taken for a carbonate content of 80% or higher

qsi = limit taken for a silica sand with a carbonate content of 20% or lower as specified in API (2000)

fs,80 = 15kPa is the limit taken for a carbonate content of 80% or greater

fs,si = Limit for a silica sands with CC <=20%

CC = Carbonate content



Clearly, there is no consensus regarding the acceptable reduction in those limiting

parameters. Nevertheless, there is an agreement that the following parameters affect

the limiting values for skin friction and bearing:

• Carbonate content,

• Degree of cementation,

• Relative density, and

• Cone penetration resistance.

Given the wide difference in opinions and lack of consensus for the limiting

parameters, Lacasse and Goulois (1989) polled the opinion of experts in

Geotechnical practice by sending a questionnaire that adopted API recommended

practice as a reference and concentrated on medium to very dense sands. Each

expert was asked to estimate limiting values for the engineering parameters for

different soil types of carbonate sand.

Analysis of the responses to the questionnaires is shown in Table 4-5. The experts

pointed to insufficient data on carbonate sands and the urgent need for in situ tests.

However, there was agreement on the appropriateness of considering the product of

two parameters, K.tanδ, as a single variable in addition to being the significant

variable for determination of skin friction in sand.

This research used the mean values of the expert opinions. A review of several

confidential engineering reports in the Arabian Gulf indicated presence of a weak

degree of cementation in the Arabian Gulf soils and these were employed in the

calibration. A summary of the employed parameters is shown in Table 4-6.

4.4.2. INPUT DATA TO APIPILE

Prediction of the static capacity of piled foundations in this research required the use

of several engineering parameters which were derived from soil borehole details and

soil investigation reports. The database collated in this research included a borehole

at or close to each pile. Derivation of the soil parameters such as soil shear strength

from these boreholes is beyond the scope of this research and is extensively covered

in the literature (Bowles, 1988; Tomlinson, 1980). The engineering parameters

were extracted from various Geotechnical reports for each layer and at each soil

borehole for each pile.



The extraction of parameters to predict capacity from the soil boring required

considerable effort and judgment due to the extreme soil variability from one layer

to the other. Even within the same layer, the distribution and occurrence of the

predominantly carbonate materials in the Arabian Gulf tend to be laterally and

vertically variable. Occasionally, this lateral variability can occur over relatively

short distances with attendant significance for offshore foundation capacity

prediction. Hence, it was also necessary to account for these variations in selecting

soil parameters by carefully inspecting and plotting all available soil data at a given

site to select the most likely value for each parameter.

Table 4-7 shows input data from one pile in the database. The pile is 91m deep with

nine layers at this particular site. The upper layer mainly comprised of sand mixed

with clay layers. The soil profile in any layer was classified to either sand (S) or

clay (C), and the corresponding nominal engineering parameters were derived from

the soil report associated with the borehole at the location of the platform. The sand

layers extend from the mudline to approximately 32m. The first layer was

essentially clay (down to 62m) which is laid over a sand layer (down to 91m). The

limiting values shown in the spreadsheet printout were obtained from Table 4-6.

4.4.3. OUTPUT FROM APIPILE

Calculation of the pile capacity was performed by APIPILE in accordance with API

RP2A-LRFD (1993) methodology, which is described in Appendix D, but applying

the limiting engineering parameters shown in Table 4-6.

The spreadsheet calculates friction and bearing values along the shaft and at the pile

tip as shown in Table 4-8 for one pile. The output provides the capacity of the pile

at the bottom of each layer. The calculations were stopped at the tipping depth. In

this particular case, the predicted capacity was found to be 17326kN as indicated at

the bottom most right of Table 4-8. The complete set of calculations for all piles is

provided in Appendix D.

4.5. ACTUAL AXIAL PILE CAPACITY

As discussed in Section 4.2, derivation of the bias factors requires evaluation of the

analytical and actual capacity of each pile in the database. Section 4.4 presented the

methodology used to derive the analytical capacity of each pile. This section



describes the methodology used to derive the ‘actual’ pile capacity for each pile in

the database.

4.5.1. METHODOLOGY

The derivation of actual capacity of piles was carried out in this research using

Wave Equation Analysis method and with the aid of the software coded GRLWEAP

described in Section 3.9.3.

The input data was obtained from the pile installation records described in Section

4.3. Input parameters to GRLWEAP were employed to construct a mathematical

model of the pile/ soil interaction. The computed capacity from GRLWEAP

represented short term capacity of piles. Setup factors were utilized to compute long

term capacity of each pile.

Validation of the approach used in this research to calculate long term pile capacity

was conducted using field measurement data of pile capacity from actual pile

installation in the Arabian Gulf.

4.5.2. INPUT DATA FOR WAVE EQUATION ANALYSIS

To perform back-analysis of pile capacities, this research employed Bearing Graph

module in GRLWEAP. The “Bearing Graph” module was considered by the

Author to be the most direct approach to back-calculate the pile capacity.

The bearing graph analysis required realistic inputs for the hammer, pile and soil

parameters in addition to the penetration resistance (blows per foot). The input data

for GRLWEAP were obtained from pile drawings and pile driving records described

in Section 4.3.

4.5.2.1. Hammer Parameters

The hammer used to drive each pile can be described by five parameters, namely:

• The rated hammer energy,

• The efficiency of the hammer,

• The weight of the ram,

• The cushion stiffness, and

• The coefficients of restitution for the ram hitting the cushion.



The rated energy and weight of the ram are hammer-specific. Cushion properties

are based on average values of field data obtained by dynamic measurements (GRL

and Associates, 1995).

Hammer efficiency is more complicated due to the ever increasing number of

hammer systems, makes and models, starting from the simple cat-and-rope driven

drop hammer of an SPT rig to sophisticated, hydraulically powered hammers with

pneumatic accelerators and electronic controls. Naturally, the unknowns vary

widely for these hammers and efficiency values cannot possibly be assigned based

on hammer model evaluations or a few measurements at the time of a hammer

model's introduction into construction practice. This is because field performance of

a hammer will depend on a variety of operational factors such as its state of

maintenance, fuel or power supply. Yet, an estimate of hammer efficiency is

required for the solution using WEA.

In GRLWEAP, the performance of a given pile driving system is evaluated by

comparison of its energy transfer efficiency (ETR) to the statistical results of similar

hammer-pile systems compiled from numerous projects. The ETR results were

obtained from actual dynamic pile measurements (GRL and Associates, 1995). It

includes losses occurring during impact, in particular:

• Cushion compression, which is non-axial;

• Plastic pile top deformation or other energy losses occurring between hammer

and pile, and

• Ram impact energy losses such as a stroke less than maximum, friction, or an

inaccurate timing of motive fluid injection (pre-admission, pre-ignition).

GRLWEAP includes a default efficiency value for each hammer and these were

used in deriving the “actual” pile capacity for every pile in the database.

4.5.2.2. Pile Parameters

Pile parameters consist of a diameter, wall thickness schedule, modulus of elasticity

of the pile material, unit weight of the pile material, freestanding length of pile and

penetration below the seafloor. These parameters are presented in Appendix B for

each pile and were derived from the pile drawings that form part of the database

collated during this research.

Dashpots were used in GRLWEAP to model the difference between static and



dynamic behavior in the pile material. Dashpots are inserted between masses and in

parallel with springs to represent the pile cushion, which transfers some of the

dynamic load by absorbing more energy when the pile is suddenly loaded. The pile

cushion is a relatively soft element, usually located underneath the helmet and

immediately above the pile top. In the Arabian Gulf, the pile cushion material is

usually wood, most frequently plywood. The primary function of the pile cushion is

to protect the pile both against high average stress levels and high contact or

bending stresses. Often, pile cushion properties change during driving. For

example, plywood may compress to only ½ of its initial thickness and its elastic

modulus may increase.

In addition, coefficients of restitution (COR) were specified to model energy losses

in cushion material and in all segments that can separate from their neighboring

segments by a certain slack distance. The COR ranges from one for a perfectly

elastic collision (which preserves all energy) to zero for a perfectly plastic condition

(which loses all deformation energy). Partial elastic collisions were modeled with

an intermediate COR value. The soil resistance along the embedded portion of the

pile and at the pile toe was represented by both static and dynamic components. The

static soil resistance forces were modeled by elasto-plastic springs and the dynamic

soil resistance by linear viscous dashpots.

4.5.2.3. Soil Parameters

The soil data include static soil resistance at driving profile, quake input and soil

damping input. The original soil damping model proposed by Smith (1960) was

followed by other models such as non-dimensional “case damping” approach

developed by Gibson and Coyle (1968) and Goble and Rausche (1976). The use of

Smith model was considered most appropriate for this research as it is a well-

established model and has been subjected to verifications by various Authors (GRL

and Associates, 1995).

In the Smith damping model, the dynamic soil resistance is proportional to a

damping factor multiplied by the pile velocity times the assigned static soil

resistance.

The dynamic soil resistance can be described by four basic parameters, namely:

• Shaft quake,



• Toe quake,

• Shaft damping and

• Toe damping.

Quake is the elastic rebound of the soil and represents the amount the pile will

rebound once the impact force of the hammer has dissipated after each blow. It is

the displacement at which the soil changes from elastic to plastic behavior.

Damping is the dissipation of energy by the soil that reduces the effective energy for

driving the pile. These parameters are not derived from fundamental soil properties

or from in situ soil testing techniques but could be evaluated from dynamic pile

monitoring. However, dynamic monitoring is rarely available and various estimates

were recommended in the literature as shown in Table 4-9.

Roussel (1979) values shown in Table 4-9 were determined from comprehensive

correlation research performed for large diameter offshore piles in which the driving

records of 58 piles at 15 offshore sites in the Gulf of Mexico were analyzed.

GRL and Associates (1995) indicated that it was rarely necessary to vary quakes

along the shaft, and that a value of 2.5mm could be used for unplugged “coring”

driving. For plugged conditions, GRL and Associates (1995) recommended a value

of D/120 for the quake, where D is the nominal diameter of the pile in mm, and

indicated that input of one average value for shaft damping and one value for toe

damping using the standard Smith damping approach yielded sufficient accuracy.

Survey of the literature revealed that researchers made no distinction between Smith

soil parameters for carbonate and non-carbonate soils. Hence, this research adopted

default Smith parameters in GRLWEAP as shown in Table 4-9, as these were found

to compare well with measurements in Arabian Gulf reported by Tagaya (1979).

4.5.2.4. Soil Resistance to Driving (SRD)

SRD is one of the more sensitive inputs to wave equation analysis. The effect of

installing the pile causes each layer to be extensively remoulded, intermingled with

adjacent layers and either compacting or loosening depending on the method of

installation and soil type.

In general, SRD can be achieved by reducing skin friction capacity in clay by some

factor to account for remoulding during driving. However, nearly the full static

capacity for skin friction in granular soils is used. The static end bearing for both



clay and granular soils is generally utilized.

Toolan and Fox (1977) described one method to calculate SRD, in which unit skin

friction at time of driving is equated to a remoulded shear strength value in cohesive

deposits and to the static friction capacity in cohesionless deposits. The remoulded

shear strength value can represent a combination of effects including an adhesion

factor between the soil and the pile. It can also incorporate reduction in the unit

friction using driving compared with the long term static value that can be obtained

after pore pressures have dissipated and setup has occurred. The unit friction during

driving can be equated to the undisturbed undrained shear strength divided by the

clay sensitivity for use in pile driveability analysis. For a range of carbonate soils

from offshore India, Agarwal et al. (1977) quoted average sensitivities of between 4

and 5. Other research reports covered the calculation of SRD (Semple and

Gemeinhardt, 1981; Stevens et al., 1982; Tagaya et al., 1979). The method of

Semple and Gemeinhardt (1981) was based on case studies evaluating pile

driveability in clay, relating clay unit friction to the clay stress history in terms of

over-consolidation ratio (OCR).

Stevens et al. (1982) developed a procedure for cohesionless soils, which is based

on case studies evaluating pile driveability for hard clay, very dense sand and rock.

According to this method, granular material was treated as silt, sandy-silt, silty-sand

or sand and particular soil parameters are used. Further, the friction angle for

carbonate material is reduced by 5 degrees to take account of potentially lower soil

resistance due to particle crushing.

Tagaya et al. (1979) estimated that the pile capacity immediately after pile driving is

½ to ¼ of the ultimate pile capacity calculated by API RP2A and presented data

showing that the longer the remoulding time, the less the unconfined compressive

strength becomes. Tagaya (1979) showed that the strength of the disturbed soil is ½

to 1/3 that of the undisturbed soil. This method showed good agreement with

instrumented actual pile driving installation in the Arabian Gulf when the measured

and calculated driving force and pile stresses are compared as shown in Figure 4-2.

The method proposed by Tagaya et al. (1979) was considered most relevant to the

research and was adopted to derive SRD for every pile. The Tagaya et al. (1979)

method was programmed in APIPILE spreadsheet to compute SRD and the

percentage of shaft resistance to the overall resistance.



4.5.3. SHORT TERM AXIAL PILE CAPACITY

The input parameters described in Section 4.5.2 were used to derive short term axial

capacity of one pile in the database. The procedure was repeated for every one of

the 138 piles and the results are documented in Appendix E. The pile driving record

data for this demonstration pile was used to derive the pile capacity using the

computer software GRLWEAP.

A 48 inch (1219mm) diameter pile was selected to demonstrate the methodology.

The pile soil interaction model shown in Figure 4-3 represents GRLWEAP input

screen. The pile was driven to 79.9m into the soil using MENCK MRBS 4600

hammer and projected around 25m above mudline.

A pile wall thickness of 31.8mm was used for the full length except that 50mm was

used for the pile shoe. The percentage of shaft resistance shown in Figure 4-3

represents a fixed percentage of the skin friction to total pile resistance and the

remainder to end bearing. In this research, the percentage of shaft resistance and the

SRD were developed as part of APIPILE spreadsheet. Analysis results of the

complete database are shown in Appendix D. Generally, the percentage was found

to be in the range 93% to 97%.

The influence of pile damping is small in steel and was ignored in the computations.

The soil profile was segmented into one meter segments and the required soil

parameters were provided for every soil segment using data from soil reports

collated in this research. A hammer damping input of 2 was used in this research.

According to GRL and Associates (1995), the use of zero hammer damping

indicates some high frequency vibrations which had not been observed in

measurements while a value exceeding two results in an over-dampened response.

The GRLWEAP default efficiency factor for the Menck hammer of 0.67 was used in

this analysis.

The pile in question was driven in the specified soil stratum using Menck 3000/150

hammer as shown in Figure 4-4. As the soil resistance increased, it was necessary

to switch to a larger hammer, and the driving continued using Menck 4600/150

hammer to the tipping depth of 79.9m. At penetration depth, the PDR shows 33

blow counts per foot as depicted graphically in Figure 4-4.

Using the pile, soil and hammer parameters, the results of the bearing Graph (BG)



analysis are shown in Figure 4-5 which describes the relationship between blow

count and ultimate capacity

The BG was entered with the blow count at penetration depth (33 blows per foot)

and the short term capacity from the Bearing Graph was read and found to be

around 14000kN.

4.5.4. TIME EFFECT - COMPUTING SETUP FACTORS

Full understanding of the basis of the API RP2A approach as well as the output

from WEA is essential for computing the bias factors and calibrating deterministic

parameters for reassessment of piles. In this research, pile capacities were predicted

using API RP2A formulation, which computes “long term” static pile capacity since

it utilizes soil parameters that represent natural conditions unaffected by the pile

driving process. On the other hand, conventional wave equation applications

generally assume that the resistance to pile penetration during driving represents the

static resistance during (or immediately after) driving plus a superimposed viscous

resistance component. For consistency, it was necessary to obtain the long term

actual capacity so as to be compatible with the capacity derived using API RP2A-

LRFD (1993) method. This was carried out by multiplying the short term capacity

derived using GRLWEAP by a setup factor.

The setup factor only describes the loss of shaft resistance and does not affect the

end bearing. Setup factors are calculated as total capacity from restrike tests divided

by the total pile capacity at the end of driving.

capacitydrivingofEndcapacityrestrikeofBeginningFactorSetup = Equation 4-3

Axial resistance at the end of driving (EOD) and axial resistance on resumption of

pile driving (also termed beginning of restrike or BOR) were obtained from the plots

of wave equation analyses by reading the capacity values corresponding to the

number of blow counts at end of driving (EOD) and beginning of restrike (BOR)

respectively.

4.5.4.1. Mechanics of Setup

Setup was first mentioned in the literature in 1900 by Wendel (Long et al., 1999),

and was documented for virtually all types of driven piles. It was reported to occur



in organic silt, inorganic saturated clay, loose to medium dense silt, sandy silt, silty

sand and fine sand in fine-grained soils in most parts of the world (Soderberg,

1961).

Many researchers (Davie and Bell, 1991; Fellenius et al., 1989; Randolph et al.,

1979; Rice and Cody, 1992; Svinkin, 1996; Tavenas and Audy, 1972; Thompson

and Thompson, 1985; Tomlinson, 1971; Wardle et al., 1992; York et al., 1994)

studied the phenomenon of time-dependent strength changes in soils during pile

driving.

When saturated cohesive soils are compressed and disturbed due to pile driving,

large excess pore pressures develop. These excess pore pressures are generated

partly from the shearing and remoulding of the soil and partly from radial

compression as the pile displaces the soil. The excess pore pressures cause a

reduction in the effective stresses acting on the pile and thus a reduction in the soil

shear strength. This results in a reduced pile capacity during and for a period after

driving. After driving, the excess pore pressure will dissipate primarily through

radial flow of the pore water away from the pile. With the dissipation of excess

pore pressures, the soil reconsolidates and increases in shear strength. This

phenomenon is widely referred to as soil setup in which the change in soil strength

over time results in a higher capacity during restrike testing. It is predominately

associated with an increase in shaft resistance (Bullock, 1999; Chow et al., 1998;

Fellenius et al., 2000; Lukas and Bushell, 1989).

Most pile capacity tests used to define the empirical relationships incorporated into

pile capacity analyses are executed somewhere between a few hours and several

days after the pile is driven. Some loading test indicated substantial increases in the

pile capacity as the pile was allowed to ‘setup’. These ‘aging’ increases were still

evident after 18 months of driving the piles (Bea and Audibert, 1979; Bea, 1980;

1983). Extraction of the piles after loading tests indicated development of

electrochemical process, which had effectively bonded the cohesionless soil to the

faces of the steel piles. Titi et al. (1999) demonstrated that setup accounts for

capacity increases of up to 12 times the initial capacity at end of driving condition.

The rate and magnitude of setup is a function of a number of factors (Samson and

Authier, 1986), the interrelationship of which is not well understood but a

qualitative description is available.



In cohesive soils, setup was found to be a function of soil and pile properties (Camp

and Parmar, 1999; Finno et al., 1989; Long et al., 1999). The shear strength of the

remoulded soil is higher than the soil’s undisturbed shear strength (Randolph et al.,

1979; Seed and Reese, 1955). In fine-grained granular soils, setup is related to soil

and pile properties in addition to creep-induced breakdown of driving induced

arching mechanisms. Since setup is related to dissipation of excess pore-water

pressures, the more permeable the soil the faster setup develops. Setup rate

decreases as pile size increases (Long et al., 1999; Wang and Reese, 1989).

Set-up effects calculated in this research represents short term effects (300 hours) as

opposed to longer term effects (20+ years). Such approach is consistent with API

RP2A pile capacity equations, which include the former effect but not the latter.

Setup time is known even less than the setup factors. In some geologic areas such

as in Louisiana (USA), setup materializes very slowly probably because of very

slow draining of pore water pressures in the fine-grained soils. Indeed, static

loading tests in Louisiana usually indicate unrealistically low capacities if the

waiting time between pile installation and static loading test is less than 6 weeks. In

other areas and particularly in coarse-grained soils, setup may occur much quicker.

A rough estimate would be that sands set up within one hour, fine sands or silts

within 1 day and clays within 7 days.

However, an estimate of setup time is only required for driving interruptions to

select appropriate hammer size during design stage. Hence, this is immaterial for

reassessment as long term capacity would have already been materialized for

existing platforms subject to reassessment.

4.5.4.2. Assessment of Setup in the Literature

Assessment of setup can be performed using measurements or empirical formulae.

Measurements can be done by installing Piezometers within an area of three

diameters of the pile to monitor pore pressure dissipation with time. Alternatively, a

number of empirical relationships were proposed to estimate or predict setup and

have demonstrated reasonable accuracy in a number of studies (Skov and Denver,

1998; Svinkin, 1996; Guang-Yu, 1988; Huang, 1988; Svinkin and Skov, 2000).

However, established relationships are difficult to generalize due to the combined

(shaft and toe) resistance determinations, inter-dependence of back-calculated or



assumed variables in addition to the complexity of the mechanisms contributing to

setup.

Nauroy and LeTirant (1983) presented data from model and full-scale pile loading

tests which indicated that, for highly compressible carbonate sands with calcium

carbonate content in excess of 80 percent, unit values are not affected by setup.

Also, using data from the Philippines, Noorany (1985) found no setup after 34 days

of driving piles when the calcium carbonate content was in excess of 90 percent. If

this is a true characteristic feature of carbonate sands/ silts, which are high in

carbonate content with high compressibility, then any measurement of soil

resistance during driving should be a close approximation of the actual pile capacity.

However, results of pile loading tests that were carried out offshore India (Agarwal

et al., 1977) did not support such findings. Nauroy and Le Tirant (1983) showed

that in material with lower calcium carbonate content and lower compressibility, the

unit skin friction was five times higher than the original value in only a week of

setup.

In 1993, Ping et al. presented the results of an installation monitoring and

performance assessment program for offshore platform foundations in Campos

Basin on the Brazilian continental shelf. One of the main objectives of the program

was to evaluate the setup effects of soils at the site. The re-drive tests were

performed between 26 and 71 days after initial driving so as to provide information

on the gain of resistance with time. Ping et al. (1993) reported increases in skin

friction representing setup of 3 to 4 for the soils at Enchova and Namorado sites in

Brazil. Thus, the effect of setup on the long term capacity remains highly site-

specific.

4.5.4.3. Setup in this Research

Time effect was evident from inspecting the pile driving records in this research. In

the short term, and during pile installation, driving delays were caused either by the

need to weld an additional section of the pile or to change a hammer, and varied in

duration between 5 minutes and 10 days. The estimate of setup was based on actual

blow count at stoppage and at start-up. For each back analysis performed, an

assessment was made of the increase in time in soil resistance to driving (SRD) due

to these delays.



The procedure for calculating the setup factor was repeated for each stoppage

penetration and for a number of piles to derive long term pile capacity. The setup

factors were plotted against the time period lapsed between End-of-Driving (EOD)

and Beginning-of-Restrike (BOR) as shown in Figure 4-6. Inspection of the trend in

the chart indicates that a setup factor of 2 fairly represented long term effect of

setup.

Large scatter in the data generally occurred at the shorter time delays and at shallow

penetration, which may suggest higher setup factors. This was partly due to the fact

that at shallow penetrations, the setup factor was based on a small difference divided

by a small number and was therefore very sensitive to the small changes in values.

The computed long term setup factor of 2 was compared with reported setup factors

in the literature. Tagaya et al. (1979) estimated the long term capacity of piles

driven in similar soils in the Arabian Gulf using a setup factors range from 1.5 to 4

with a final recommendation of 1.5 for that site. The choice of 1.5 is the lowest

value obtained through a literature review. Therefore, the value of setup factor of 2

was considered to be a fair representation which fell within the range of setup

factors reported in the literature. Rather than using a value of 2, the use of the

individual computed value for each pile was contemplated. However, the computed

setup factors for each pile represent short term setup which tends to underestimate

the long term capacity of the pile. Since the computed capacity using API RP2A

represents long term capacity, and in order to have consistent bias factors, the

representative value of 2 was used in this research.

4.5.5. BACK-ANALYSIS PROCEDURE

The “actual” axial pile capacity was derived in this research by multiplying the short

term axial pile capacity, which was described in Section 4.5.3, by a setup factor of 2,

as described in Section 4.5.4. The process of deriving “actual” pile capacity is

termed back-analysis procedure in this research.

Validation of the back-analysis procedure revolved around two issues. Firstly, there

was a need to validate the accuracy of GRLWEAP to predict the short term axial

pile capacity. Secondly, it was important to validate that the use of the back-

analysis procedure correctly predicted axial pile capacity.



4.5.5.1. Validation of GRLWEAP Results

A survey of the literature showed that the level of reliability of GRLWEAP

realistically predicts the capacity of piled foundations. Zhou (1999) confirmed that

GRLWEAP accurately predicts axial pile capacity for the Beginning of Restrike

(BOR) condition when only soil profile, hammer and pile data is available.

Additionally, using the database presented by Likins et al. (1996), Thendean et al.

(1996) conducted a correlation research using 99 cases for which static pile loading

test was available in a variety of soil and pile types. It is noted that the predictions

are automated within GRLWEAP and are thus operator independent. In the

correlation of that research, the ratio of predicted static capacity in failure to actual

capacity was evaluated by the Davisson offset method for end of driving (EOD) and

beginning of restrike (BOR) tests. Thendean et al. (1996) showed the general

tendency of GRLWEAP EOD results to underpredict the load test capacities. On

the other hand, BOR results tend to be overpredicted as shown in Figure 4-7.

In the initial stages of this research, such trend made it hard to justify the use of

GRLWEAP given its tendency to underpredict the EOD and overpredict BOR

capacities. However, a closer look at the analysis in light of the interpretation of test

results provided a different view and supported the use of GRLWEAP. Fellenius

(1975, 1980) presented nine different definitions of pile capacity evaluated from

load-movement records of static loading test. Five of these were of particular

interest, namely, the Davisson Offset Limit, the DeBeer Yield Limit, the Brinch-

Hansen Ultimate Load, the Chin Kondner Extrapolation and the Luciano Decourt

methods.

The Davisson Offset Limit Load is probably the best known and widely used

method. The limit load was proposed by Davisson (1972) as the load corresponding

to the movement that exceeds the elastic compression of the pile (taken as a free

standing column) by a value of 0.15 inch (4mm) plus a factor equal to the diameter

of the pile divided by 120. The Offset Limit Load is not necessarily the ultimate

load.

The method proposed by DeBeer (1968) is based on plotting the load movement

data in a double-logarithmic diagram. If the ultimate load was reached in the test,

two line approximations will appear; one before and one after the ultimate load

(provided the number of points allow the linear trend to develop). The slopes are



meaningless, but the intersection of the lines is useful, as it indicates where a change

occurs in the response of the piles to the applied load. DeBeer called the

intersection the Yield Load.

The Hansen 80% criterion was developed by J. Brinch Hansen (1963), who

proposed a definition for pile capacity as the load that gives four times the

movement of the pile head as obtained for 80 % of that load.

The Chin (1970; 1971) method can be applied by dividing each pile movement by

its corresponding load and plotting the resulting value against the movement. After

some initial variation, the plotted values will fall on straight line. The inverse slope

of this line is the Chin-Kondner Extrapolation of the ultimate load. As an

approximate rule, the Chin-Kondner Extrapolation load is about 20 % to 40 %

greater than the Davisson limit.

Decourt (1999) proposed a method which can be applied by dividing each load by

its corresponding movement and plotting the resulting value against the applied

load. A linear regression determines the line. The Decourt extrapolation load limit

is the value of load at the intersection.

Fellenious (1980) interpreted results of a pile loading test using the various methods

described above as shown in Table 4-12.

Inspection of the result shows that the Davisson Offset Limit Load method, which

was used to correlate GRLWEAP, is conservative while the Hansen Criterion is

more “liberal”. Maximum values of pile capacity can be estimated with the Chin

method from which results are about 20 % to 40 % greater than from the Davisson

limit.

Considering that API RP2A-LRFD (1993) pile load test results were interpreted

using Davisson Offset Limit, GRLWEAP results were considered to provide pile

capacity that is consistent with test results used to calibrate API RP2A-LRFD

(1993). Consequently, GRLWEAP pile capacity prediction was considered to

represent “actual” pile loading tests. Further, the automated parameters within

GRLWEAP were shown to represent the most likely conditions of pile driving

especially for BOR conditions.

4.5.5.2. Validation of the “Actual” Capacity

Validation of the back-analysis procedure was carried out in this research by



comparing the predicted axial pile capacity described in Section 4.5.3 to the

dynamic measurement results of that pile.

The dynamic measurements were obtained from actual offshore installation in the

Arabian Gulf. The dynamic pile measurements were carried by a specialist

contractor in the Arabian Gulf during an offshore installation campaign of a

platform in 2003. Figure 4-8 shows pile driving records of the four piles supporting

that platform.

To validate the results of the back-analysis method, the piles were modeled using

GRLWEAP. The input data screen in Figure 4-9 shows the ultimate capacity range

that was specified to start from 2400kN and stepped up in equal steps to reach

24600kN. The selection of this range ensured that the bearing graph captured all

possible blow count range.

The input data sheet shown in Figure 4-9 describes a MENCK MHU 600 hammer to

drive the pile to the tipping depth. The assumed hammer efficiency was 67% which

was taken as the default from GRLWEAP for the Menck MHU600 hammer. The

GRLWEAP default parameters were used to represent soil parameters (quake and

damping). The pile is 90m long and penetrates 56m into the soil stratum, with a

sectional area of 1193cm2. The shaft resistance percentage used in the calculations

is 90%.

The output data in Figure 4-10 shows the relationship between the blow count and

the ultimate capacity in the GRLWEAP bearing graph. The ultimate capacity values

are defined at the bottom of Figure 4-10.

Figure 4-10 shows a residual skin friction component of 65% of pile capacity, which

is lower than 90% component shown in Figure 4-9 and calculated using API RP2A

methodology. The 90% skin friction component is based on bearing capacity

theory, which tends to over predict the true toe resistance of long piles. Typically,

the residual (post-peak) capacity has been shown to be 10% to 20% lower than the

peak capacity (Mirza, 1997).

The residual stress effect has been attributed to the flexibility of the pile and

differences between toe and shaft quakes. This is because the pile’s shaft resistance

tends to restrict the pile’s rebound at the end of the impact event. When the next

blow occurs, pile and soil are prestressed and therefore less energy is needed to



compress and move the pile through the soil. GRLWEAP performs residual stress

analysis, which is more rational than the standard blow count calculation method

originally proposed by Smith, particularly for long or flexible piles.

To compute the ultimate capacity for each pile, Figure 4-10 was entered with the

blow count of the pile, which was read from the pile driving records. Using linear

interpolation, ultimate capacity for each pile was calculated to represent the short

term capacity as previously described. Applying a setup factor of 2 as discussed in

Section 4.5.4, the long term ultimate capacity was computed. The results are

summarized in Table 4-14.

The computed capacity at the target penetration was compared to the dynamic field

measurements (CAPWAP) in Table 4-14. Comparison of the calculated capacity

using back-analysis procedure to the CAPWAP results was very encouraging (less

than 5% difference).

Initial attempt to check the validity was not so encouraging but was found to be due

to inaccurate modeling of the pile. This was driven by the desire to shortcut

modeling of the pile wall thicknesses due to the large amount of data entry that was

required for that pile.

The make-up of the pile in question is shown in Figure 4-11. To model the pile

cross sections shown in Figure 4-11 for GRLWEAP analysis, a large number of

sections were required for modeling the pile accurately, which made the data entry

quite tedious.

The input required a change in the calculated properties not only at each wall

thickness but also at each change in the soil layer within that thickness as shown in

Figure 4-12. In this example, the soil boring identified eleven (11) soil layers up to

the tipping depth, and the structural drawings identified 10 pile sections. When the

pile sections were imposed on the soil layers, it was necessary to subdivide the pile

into twenty one (21) sections to model the changes in soil profile within each pile

section. This required considerable effort to model each pile in the database

containing 138 piles. A shortcut was contemplated to reduce the complexity of the

input data in GRLWEAP by using the average pile wall thickness instead of

modeling the various pile sections.

Using an average wall thickness, instead of the actual pile wall thicknesses, the



computed short term pile capacity was 15.6MN as shown in Figure 4-13. The

computed pile capacity (15.6MN) at 25 blows per foot was factored by applying a

setup factor of 2. The computed capacity (31.2MN) is almost double the

corresponding capacity (15.9 MN at BOR) from dynamic monitoring which is

shown in Table 4-14.

In order to understand the reason for the large discrepancy in results when average

pile wall thickness was used, full understanding of wave propagation was necessary.

This section presents an overview of the mechanics of driving a pile.

When a hammer first strikes a pile, it is only compressed at the ram pile interface as

shown in Figure 4-14. This compressed zone, or force pulse, expands into the pile

toward the pile toe at a constant wave speed which depends on the pile’s elastic

modulus and mass density. When the force pulse reaches the embedded portion of

the pile, its amplitude is reduced by the action of static and dynamic soil resistance.

Depending on the magnitude of the soil resistance along the pile shaft and at the pile

toe, the force pulse will generate either a tensile or a compressive force pulse, which

travels back to the pile head. Both incident and reflected force pulses will cause a

pile toe motion and produce a permanent pile set if their combined energy and force

are sufficient to overcome the static and dynamic resistance effects of the soil.

During an elastic loading process, the pile penetrates into the ground and the

activated soil resistance increases until it reaches, and then exceeds, the maximum

(i.e., ultimate) values. The excess energy then works on advancing the pile and

securing permanent penetration. Typically, piles are installed with permanent set of

30 to 3mm/blow (i.e. driving blow count of 10 to 100 blows per foot). Pile refusal

criterion (sometimes defined as penetration resistance exceeding 250 blows per foot)

occurs if the driving system is incapable of producing sufficient pile displacement

beyond the elastic and into the plastic soil deformation ranges. Under such

conditions, dynamic pile testing can only measure the mobilized portion of the

ultimate pile capacity activated by the limited pile movement. Dynamic testing

during restrike is performed under a limited number of hammer blows. The same

original pile driving hammer or another one may be used to restrike the pile. The

pile should experience permanent penetration under the restrike hammer blow if the

ultimate pile capacity is to be reached and measured.

Hence, soil resistance to driving depends on the transferred energy which in turn



depends on the pile modulus. As the pile modulus is a function of the wall

thickness, using inaccurate wall thickness in the model results in computing an

incorrect value for the transferred energy and hence the wrong capacity prediction.

This explains the reason for the inaccurate prediction when the incorrect wall

thickness was used in the analysis.

This was an interesting observation as it demonstrated the importance of pile wall

thickness in the static prediction of ultimate pile capacity. However, API RP2A-

LRFD (1993) formulation does not account for this effect. Hence, the observation

in this research could lead to a very useful future research requirement to refine the

API RP2A prediction and reduce the bias and error reported by Tang (1988) in the

application of the method to predict axial pile capacity.

As a result of the above, the use of the above back-analysis procedure with

GRLWEAP one-way equation analysis to derive pile capacity was considered

representative of loading test results. Furthermore, the model for every pile in the

entire pile database (138 piles) accounted for variations in the wall thicknesses of

each pile to ensure accurate results.

4.5.6. SENSITIVITY ANALYSIS OF THE COMPUTED ACTUAL

PILE CAPACITY

The use of GRLWEAP to perform back analysis requires a number of input

parameters, some of which are related to the physical characteristics but others are

default values within GRLWEAP. The parameters associated with the physical

characteristics include the pile diameter, penetration length and the pile wall

thickness. The sensitivity of the default parameters used to calculate the “actual”

capacity was examined and is reported in this section.

4.5.6.1. Hammer Efficiency

The sensitivity of using various hammer efficiencies was examined by executing a

number of Bearing Graph analyses for various hammer efficiency values. The

analysis was performed on the pile shown in Section 4.5.3, which used a MENCK

MRBS 4600 hammer. For this hammer, the default efficiency value in GRLWEAP

was 0.67. The analysis investigated various hammer efficiencies, ranging from

0.40 to 0.8. The computations resulted in curves for the ultimate capacity against



blow count (blows per foot) and these were plotted in Figure 4-15.

The plot indicates that the choice of hammer efficiency value is relatively

insensitive for up to 100 blow count (blows per ft). This is the range experienced in

most offshore pile driving in the Arabian Gulf as revealed from examining pile

driving records in the database. Hence, the use of default values for the hammer

efficiency in GRLWEAP was shown to produce acceptable results when the number

of blow counts is less than 100 blows per foot.

4.5.6.2. Segment Length

In this research, each pile was divided into one-meter segment lengths, and each

segment was modeled as a weight and a spring. The sensitivity of the assumed

segment length was checked by executing the analysis with various segment lengths.

The analysis was executed for segment lengths of 0.5 m, 1.0m and 5m.

Figure 4-16 demonstrates that the selection of segment length has almost no effect

on the computed capacities (around 17,000kN) experienced in the Arabian Gulf.

Hence, the use of one meter segment length produced reliable results and was


4.5.6.3. Cushion Effects

Cushion type is not usually specified in pile driving records and the selection of

cushion type is user-defined in GRLWEAP. Several Bearing Graph analyses were

executed to examine the effect of changing cushion type and thickness. For the

demonstration pile, the default cushion type for the Menck MRBS 4600 hammer

was a Bongossi wood model 42 (1065)-72 (1830). To investigate the sensitivity of

using a different cushion type, two other cushion types were analyzed. The first one

was a Mitsubishi MH 80B Micarta and the second was Delmag 42(1070)-48(1220)

Offshore 43%alim+57% conbest.

As can be seen from Figure 4-17, the computed capacity is highly insensitive to the

selected cushion type or manufacturer. This is because soil resistance, rather than

pile characteristics, dominates the axial capacity.

4.6. STATISTICAL PARAMETERS OF BIAS FACTORS

This section presents the statistical parameters of the bias factors. For each pile in



the database, the axial pile capacity was predicted using API RP2A-LRFD (1993)

procedure as described in Section 4.4. A basic assumption in this research was that

the statistics of the bias factors represented all sources of errors such as SPT testing,

pile loading tests and the static pile capacity prediction models.

The short term axial capacity for each pile was also derived using WEA as described

in Section 4.5.3 and time effects were incorporated as described in Section 4.5.4.

The bias factor for each pile was calculated using the following relationship:

CapacityCalculatedARPAPIFactorSetupCapacityGRLWEAPFactorBias

2×

==λ Equation 4-4

A bias factor greater than unity indicates that the limiting soil parameters shown in

Table 4-6 underpredicts the capacity and vice versa.

The statistics of the bias factors were calculated in this research for the following

subgroups:

• Database subgrouped by installation method,

• Database subgrouped by soil profile, and

• Database subgrouped by degree of optimization in design.

The statistical parameters were also calculated for the complete database. Further

grouping of the data was considered impractical as it would have resulted in too

many groups, which would make it impractical for engineering applications.

Further, the size of each group would have also been reduced to a level possibly

leading to gross inaccuracies in the calculations of the statistical parameters. In

addition, the use of three groups has already extended the analysis beyond the use of

a single group used in the calibration of API RP2A-LRFD (1993) and was therefore

considered sufficient refinement to the API RP2A-LRFD (1993) yet still within

practical bounds.

4.6.1. STATISTICS OF THE COMPLETE DATABASE

Figure 4-18 presents a scatter plot of the computed bias factors versus the

penetration ratio for the complete database. The penetration ratio is defined as the

ratio of the pile length over its diameter. Inspection of the trend in Figure 4-18

shows an almost equal distribution around a bias factor of 1.0. This implies no bias

in the overall database when predicting capacity using API RP2A-LRFD (1993)



formulation with the limiting engineering parameters defined in Table 4-6.

The statistical parameters of the bias factors were derived using the method

described in Section 3.4 and with the aid of the @RISK software. The histogram of

the data was plotted and several distributions were examined to fit the data. The

normal probability plot generated the best fit shown in Figure 4-19. The associated

statistical parameters were calculated as 0.93 and 0.36 for the mean and coefficient

of variation, respectively.

To confirm the selected distribution, probability plots of the bias factors were

generated for several competing distributions to arrive at the best fit. Figure 4-20

represents a probability plot for an assumed normal distribution using the parametric

method described in Section 3.4.2. The correlation coefficient associated with the

linear fit to the data in the probability plot provided a measure of the goodness of the

fit. The calculated correlation coefficient for a normal distribution produced the

highest correlation coefficient (0.98) and therefore confirmed the assumption of

normal distribution. The calculated statistical parameters (mean = 0.93, coefficient

of variation = 0.36) of the data were found to be identical to the values using non-

parametric method.

4.6.2. GROUPING BY INSTALLATION METHOD

Availability of installation records for existing platforms reduces uncertainty

associated with predicting axial pile capacity when reassessment of a platform is

required. On the other hand, installation data are unavailable during design, which

makes it allow higher margin of safety to deal with uncertainties associated with

installation circumstances. Hence, it was prudent to subgroup the database by

installation method in order to represent the effect of various installation

circumstances on the predictive method.

Inspection of the pile driving records revealed that the method used to install the pile

could be broadly divided into two groups. The first group includes piles that were

driven without supplementary methods and the second group includes piles that

were installed using supplementary installation methods such as pre-drilling/

grouting or jetting. Out of the 138 piles in the database of this research, 20 piles

were installed with jetting, and the balance was installed without the need for

supplementary methods.



The statistical analysis results indicated that API RP2A-LRFD (1993) significantly

(40% to 80%) overestimate the capacity as shown in Figure 4-21 when piles are

installed using supplementary methods. The computed capacities were therefore on

the unsafe side.

McClelland et al. (1969) reported similar (50% to 85%) decrease in the shaft

resistance for piles installed using supplementary methods compared to those driven

without the need for supplementary methods. Further, Poulos and Davis (1980)

recommended a reduction of 50% for the ultimate shaft resistance from the

originally calculated capacity in the jetted zone for jetted piles driven to the final

penetration. The reduction depends on the size of the predrilled hole.

The excessive (80%) reduction in capacity for some piles was disturbing and

required an explanation. One explanation relates to the hammer type used for those

piles. Mosher (1987, 1990) summarized the results from five sites where piles were

installed by both impact and vibratory hammers. The conclusion was that, for a

significant majority of the cases, piles installed in sand with a vibratory hammer had

a lower ultimate capacity than impact driven piles at the same site. Interestingly,

piles with low bias factor of 0.20 were found to have been driven with a VULCAN

530, which is a vibratory hammer. Hence, data could be further broken according to

the hammer type (impact or vibratory), but this was not adopted in this research.

Mosher (1987) also concluded that time-dependent soil strengths occurred equally

for both installation methods but that, with time, impact driving increased the

capacity of the installed pile as compared to a vibratory driven pile. Inspection of

pile driving records in this research revealed that piles were driven using the same

hammer type (vibratory or impact) for the full penetration depth. Hence, for the

database collated in this research, no increase in pile capacity would be materialized

as there was no change in hammer type during driving.

To derive the statistical parameters, histogram of the data was generated using

@RISK and a number of probability distributions were fitted. The normal

distribution provided the best fit for this set of data as shown in Figure 4-22. For the

normal distribution, the mean (λ = 0.65) and the coefficient of variation (COV =

0.40) corresponding to a normal quantile of 0 and 1 respectively were calculated for

piles that were installed using supplementary methods.

Due to the small number of data points, the parametric method described in Section



3.4.2 was employed. The normal probability plot for this set of data generated a

straight line as shown in Figure 4-23 and confirmed that that the normal distribution

type and statistical parameters (λ = 0.65 and COV = 0.40) provided good

representation of the data.

4.6.3. GROUPING BY SOIL TYPE

Further subgrouping of the data was inspired by an approach adopted by Tomlinson

(1971) using a large number of loading tests. This subgrouping accounts for the

high variability in soil formations in the vertical as well as in the horizontal

direction. In this research, two types were identified. The first type represents piles

in soils dominated by cohesionless soils underlain by cohesive soils (Type CS) and

the second type represents piles in soils that are dominated by cohesive soils

underlain by cohesionless soils (Type SC).

The bias factor distribution for the first soil type SC is shown in Figure 4-24, while

Figure 4-25 shows the distribution for the soil type CS.

Inspection of Figure 4-24 revealed that the use of API RP2A-LRFD (1993) with the

limiting parameters shown in Table 4-6 tends to overpredict pile capacities for SC

soil type, which is unsafe. On the other hand, when piled foundations are installed

on soils dominated by cohesionless soils over cohesive soils, the trend has not been

as conclusive as can be seen in Figure 4-25 but appears to underpredict the capacity.

Interestingly, the trend of overpredicting the capacity for soil type SC and

underprediction for soil type CS is consistent with the pile loading test results

reported by Tomlinson (1971). Tomlinson showed that the adhesion factors for

piles driven through granular materials into stiff or very stiff clays were

considerably higher than those piles driven through soft clays into the stiffer soils.

Similar results were reported if piles were driven into stiff to very stiff clays without

other overlain strata.

Tomlinson (1971) provided an explanation of such a trend by examining the soil

movement in the upper layer and the separation of the pile surface from the

surrounding undisturbed soil. During driving, the upper sand layers in soil type CS

are carried down to a limited depth forming a skin of compacted sand or sand/ clay

mixture around the shaft. This skin has a high friction value such that piles driven

to penetrations of less than 20 diameters into the cohesive soils can have an ultimate



skin frictional resistance exceeding 1.25 times the undrained shear strength of the

soil. On the other hand, when piles are driven through soft clays - as the case with

type SC soils - a soft skin is carried down, which has a considerable weakening

effect on the frictional resistance of the shaft.

Development of the statistical parameters for the bias factors for both soil types was

performed using @RISK and using the method described in Section 3.4.2. For the

SC soils, the bias and standard deviation were calculated as 0.77 and 0.26,

respectively as shown in Figure 4-26. The computed COV is 0.34.

The development of the statistical parameters for the bias factors for both soil types

was confirmed using probability plots. For the SC soils, the bias and coefficient of

variation were calculated as 0.77 and 0.34, respectively, as shown in Figure 4-27.

4.6.4. GROUPING BY OPTIMIZED DESIGN

The bias factor distribution for piles driven in soils CS (shown in Figure 4-25)

indicates, though not conclusively, that the use of API RP2A-LRFD (1993) with the

limiting parameters shown in Table 4-6 underpredicts pile capacity. To explain the

inconsistent trend of bias factors in Figure 4-25, a number of deterministic analyses

from historical designs were reviewed and an interesting trend was detected. Some

piles appeared to be grossly over-designed as was evident from the large factor of

safety identified in the deterministic analyses using API RP2A-WSD (2000). Under

operating conditions, API RP2A-WSD (2000) requires a minimum factor of safety

of 2.0 for pile design. The deterministic analyses of the database that constituted the

data in Figure 4-25 broadly identified two groups according to the calculated factor

of safety. The first group is termed “Optimized” as shown in Figure 4-28 and

represents piles with factor of safety around 2.0 under operating conditions. The

other group, termed “overconservative” represents piles with factor of safety around

4.0 or more and is shown in Figure 4-29.

Inspection of Figure 4-29 reveals that the application of API RP2A-LRFD (1993)

formulation with the limiting parameters defined in Table 4-6 tends to overpredict

the capacity (bias < 1.0) for overconservative designs (FOS > 2.0) when the

penetration ratio exceeds 80. However, for lower penetration ratios (up to 50), the

API RP2A formulation tends to underpredict the capacity (bias > 1.0). The bias

factor pattern is generally opposite to the pattern shown in Figure 4-28 for optimum



designs.

To obtain the statistical parameters of the overconservative group, the non-

parametric approach described in Section 3.4.2 was applied using @RISK. The

statistical parameters (mean = 0.97, COV = 0.34) of the bias factors for the

conservative designs were computed as shown in Figure 4-30.

Using the parametric approach described in Section 3.4.2, the probability plot shown

in Figure 4-31 confirmed the results of the non-parametric analysis.

For the optimum design of type CS soil, an analysis was performed using @RISK.

The statistical parameters were computed using non-parametric approach. The bias

factor for the optimum design was computed as 1.12 with COV of 0.26 as shown in

Figure 4-32.

Using the parametric approach described in Section 3.4.2, the statistical parameters

for the optimum design (λ = 1.12, standard deviation = 0.29, COV = 0.26) were

computed as shown in Figure 4-33.

4.6.5. GROUPING BY PENETRATION RATIO

Penetration ratio is defined as the ratio of the pile length (L) over its diameter (D).

The effect of penetration ratio was investigated by grouping the complete database

into four groups as shown in Table 4-15 to represent penetration ratios less than 50,

between 50 and 75, between 75 and 100 and larger than 100.

Table 4-15 reveals little change in the mean of the penetration ratio but indicates

larger variability in the coefficient of variation. The coefficient of variation is

relatively high (more than 0.40) for low and high penetration ratios. For

intermediate penetration ratios, the coefficient of variation is lower (less than 0.35)

and is consistent with the mean of the complete database.

4.7. BENCHMARKING STATISTICS OF BIAS FACTOR

In order to tie the work back to the API RP2A-LRFD (1993), the statistical

parameters of the complete database derived in this research were compared to those

used in the calibration of API RP2A-LRFD (1993).



4.7.1. BIAS FACTORS IN API RP2A-LRFD (1993)

The statistical parameters used in the calibration of API RP2A-LRFD (1993) are not

explicitly stated in API RP2A-LRFD (1993) but were traced to a research project

titled “Offshore Axial Pile Design Reliability”. In that research project, Tang

(1988) used 44 load tests to evaluate the statistical parameters and found substantial

bias (mean = 0.78) and error (COV = 0.68) in the predicted model. On further

examination of the database, Tang (1988) excluded piles driven in stratum

consisting of carbonate sand containing shells and/ or signs of carbonate

cementation which reduced the database to 33 piles only. Exclusion of piles in

carbonate sand improved the capacity prediction and yielded mean bias of 1.01 and

error of 0.46.

Hence, the bias and error computed for carbonate soils in this research (0.93, 0.36)

were essentially similar to those reported by Tang (1988) for the non-carbonate soils

(1.01, 0.46). This implies that the large scatter (0.78, 0.68) reported by Tang (1988)

was mainly due to combining carbonate soils and non-carbonate soils in one

database which points to the large difference in soil behavior between carbonate

soils and other soil types.

Prior to the publication of API RP2A-LRFD (1993), Hamilton and Murff (1992)

presented a set of bias factors and coefficients of variation of loads and resistance

which could be used in calibration work of piled foundations and these are shown in

Table 4-18. However, those proposed values were not adopted in the calibration of

API RP2A-LRFD (1993).

For piles in normally consolidated or soft clays, Hamilton and Murff (1992)

considered that there was considerable evidence to support a capacity bias of around

1.3. Such bias was considered to be mainly due to rapid loading rate effect, which

would apply to the extreme loading condition. Likewise, piles primarily supporting

gravity loads were assigned a bias of 0.9 because of the very slow loading rate.

Piles in sand are not as sensitive to loading rate. Lacasse (1988) demonstrated

consensus among industry experts that API RP2A-WSD guidelines are conservative

for dense sand. Accordingly, and based on results reported by Tang (1988),

Hamilton and Murff (1992) used bias of 1.2 for axial capacity of piles in sand.

Hamilton and Murff (1992) reported coefficients of variations of 30% and 40% for



piles in sand and clay respectively on the basis of the work reported by Tang and the

survey of industry experts by Lacasse (1988). Hamilton and Murff (1992)

supported the contention that axial pile capacity in sand has a higher degree of

uncertainty than axial pile capacity in clay.

The discussion above implies that piles in clay have a higher reliability than piles in

sand due to the difference in bias and uncertainty. However, current specifications

(WSD and LRFD) used in industry practice do not address the apparent discrepancy

between piles in different soil types.

4.7.2. BIAS IN THE STATISTICS OF BIAS FACTORS

The bias factors computed in this research (0.93) and those (1.01) reported by Tang

(1988) were found to be considerably lower than the bias value of three (3) reported

by Bea (1983) and others.

The identification and characterization of “biases” associated with API RP2A-

LRFD (1993) formulation was of particular importance to clarify the discrepancy.

These biases represented explicit and implicit conservatism that were intentionally

and unintentionally integrated into the current API RP2A-LRFD (1993) guidelines.

To illustrate the magnitude of bias that can be present in a traditional design, Bea

(1983) studied the failure of a platform located at South Pass Block 70, Mississippi

River Delta. The platform was designed according to API RP2A guidelines to

withstand a 58 ft high (17.7m) design wave. The most heavily loaded pile (i.e.

critical pile) was designed for a factor of safety of 1.5 against a combination of

dead, live and extreme wave loads. The ultimate shaft capacity for the critical pile

was calculated using the API RP2A method and found to be 3100 kips (13790kN).

However, based on a detailed case research after a sea-floor slide at the site, the

actual capacity of the critical pile was estimated to be 10,000 kips (44484kN). This

represented a bias factor of more than 3.

In 1984, Bea considered the results of an older pile loading test in silty sand and

reported large difference (bias = 2.0) between the pile axial capacities based on API

static capacity guidelines and the pile test capacity. Bea (1984) explained that the

large bias are accumulated from extrapolations and interpretations involved in all

phases of the data gathering and design process methods used in the ‘crude’ but

industry practice (Bea, 1983; Wu et al., 1989; Tang, 1988; Tang and Gilbert, 1990;



1992) as well as the pile capacity model including:

• Performing the soil boring,

• Accomplishing the sampling,

• Performing the testing (in situ and laboratory),

• Characterizing the soil strength and stiffness characteristics,

• Defining the pile loading (dead, live, environmental),

• Analyzing the individual pile and pile system performance, and

• Defining the age of the pile at the time that the capacity was to be evaluated.

Bea (1983) quantified the factors contributing to this large bias and suggested that a

bias of 2.7 could easily result from a mere 10% on each of the elements presented in

Table 4-17. Even if the lower bound of all elements is considered, there is still bias

of 2.5.

Similar analysis was carried out by Tang (1988) who evaluated the overall bias and

error associated with the API recommended procedures for the design of offshore

piles driven in clay. Tang (1988) identified significant parameters affecting the

reliability of axial pile capacity through a complete uncertainty analysis. The

analysis considered the following:

• Capacity model error,

• Systematic bias in undrained shear strength as a result of using driven samples.

The use of driven sample method is more common in practice than the pushed

sample method which formed the basis of the design method in RP2A. The use

of pushed sample generally reduces the scatter of error and is more reliable but

driven samples were mostly used for older platforms to define strength

parameters,

• Systematic bias through the use of nominal or “interpreted” strength instead of

using the average of the measured undrained shear strength,

• Systematic bias due to insufficient soil samples at the site,

• Spatial variability of soil properties,

• Reconsolidation and time effect,

• Rate of loading effect,

• Compressibility effect, and

• Error in jacking and determination of ultimate load.



A high bias is particularly applicable to older platforms due to the effect of older

soil boring techniques. There has been a dramatic improvement in the methods used

to perform borings and obtain, preserve, transport and test samples as a function of

time (Dover, et al., 1981; McClelland, Ehlers, 1986; Young, et al., 1983; Wu, et al.,

1989). Hence, soils could be envisioned as being near their lower limit of strength

in these conditions and characterizations. For example, it is not uncommon for the

soil boring of an existing platform to have had been carried out using rudimentary

methods with no heave compensation, wire line sampling method, unconfined

compression testing and lower bound characterization of available data. In such

cases, the bias could be expected to be in the range of 2 or more (Emrich, 1971,

Young, et al., 1983, Quiros, et al., 1983, Dover, et al., 1981, McClelland and Ehlers,

1986).

To understand the effect of sampling, Tang (1988) investigated the undrained shear

strength (su) as it is the most predominant parameter defining foundation capacity in

clay. The undrained shear strength values depend on sampling techniques (driven or

pushed), type of tests performed in the field or in the laboratory and the degree of

disturbance of the soil sample. Soil strength sampling/ test method are generally

performed using any one of the following methods:

• Unconfined compression strength on pushed sample (UCP),

• Unconsolidated undrained strength on pushed sample (UUP),

• Unconsolidated undrained strength on driven sample (UUD),

• Miniature vane strength on driven sample (MVD),

• Miniature vane strength on pushed sample (MVD), and

• Unconfined compression strength on driven sample (UCD)

Tang (1988) reported a bias in the calculated pile capacity ranging from 1.3 to 3.7

and an error varying from 28 to 53%. The unconfined compression (UC) tests of

driven samples were generally found to produce low shear strength values as they

are significantly affected by soil disturbance. Tang (1988) calibrated the API pile

test database to the UCP samples and reported an average bias in undrained shear

strength with a mean of 1.18 and COV 27% due to the use of UCP samples.

Thus, older borings could be expected to have inherently greater biases and the use

of these values without any adjustments may be overly conservative. The boreholes

used in this research were taken between 1965 and 2001, and UC tests on driven



samples were performed for a majority of cases.

To account for the high bias inherent in API RP2A-LRFD (1993) as described

above, the Author considered the use of a bias factor (2.5), or higher, instead of the

calculated (0.93) bias factors to calibrate the required resistance factors. However,

this would have deviated from the statistical parameters adopted in the calibration of

API RP2A-LRFD (1993) and was therefore not adopted in this research.

The mean bias of 0.93 is used for calibration purposes only for consistency with the

approach adopted in calibrating API RP2A LRFD. The true bias in piled

foundations may be higher as evident by lack of any clear foundation failure during

hurricanes Katrina, Ivan and Rita.

4.8. BAYESIAN UPDATE OF BIAS FACTOR STATISTICS

Calibration of the resistance factors requires sufficiently large number of

observations. However, partitioning of the database reduced the size of each

sample. To supplement the limited size of the partitioned database and provide a

more robust estimate of bias factors, Bayesian updating was employed to enable

inference. In statistics, inference refers to the process of drawing conclusions or

making predictions on the basis of limited information.

4.8.1. “PRIOR” DISTRIBUTION

Due to paucity of the data related to the Arabian Gulf, the ‘prior’ distribution of the

overall bias in pile axial capacity was represented by statistics in the literature.

A survey of the literature to identify suitable statistical parameters revealed

disagreement between researchers on the topic. Olsen (1984) suggested a bias of

0.79 and a range of COV between 20% to 40%, but Moses and Larrabee (1988)

reported that the commonly quoted database was a bias of 1.0 and the COV of 20%.

Moses (1986) explained that piles in platforms are stronger than that of Olsen’s

research (1984) because “pile capacity predictors” presented in Olsen’s research did

not behave the same as “pile capacity consultants” even though they might be the

same individuals.

Using a database of 44 load tests (out of the original databank that comprised 1004

load tests), Tang (1988) estimated a range of overall bias in the 16th Edition of the



API RP2A between 1.3 and 3.7 and a range for the overall error (COV) ranging

from 32% to 53%. The range was affected by the methodology used to determine

the undrained shear strength at the given site. For the 17th Edition of API RP2A,

Tang (1988) estimated a range between 1.6 and 2.9 for the overall bias, while the

overall error (COV) ranged between 0.3 and 0.4.

In this research, a mean value of 1.3 with COV of 0.3 was used for the ‘prior’ of the

bias factor. The mean value of 1.3 was considered to provide a reasonable balance

to represent expert opinions.

4.8.2. “LIKELIHOOD” DISTRIBUTION

The ‘likelihood’ distribution of the bias factor distribution in this research simulated

the results of the back analyses presented in Section 4.6 and summarized in Table

4-16.

4.8.3. “POSTERIOR” DISTRIBUTION

Using the approach described in Section 3.5.2, the updated statistics of the bias

factors were computed and are summarized in Table 4-19.

Inspection of the statistical parameters reveals that the mean ranges from 0.85 to

1.18. The coefficient of variation ranges from 0.20 to 0.26, which is lower than the

value (0.46) employed in calibrating API RP2A-LRFD (1993). Hence, grouping the

data in the manner described above reduced the error in the data, which enabled a

more refined resistance factors compared to those in API RP2A-LRFD (1993).

4.9. TARGET RELIABILITY INDEX

Section 2.3.3 identified limitations in the reliability-based method and revealed that

the selection of a specific target level, to reflect the many variables that should be

considered, has not been resolved.

For the purpose of this research, the Author considered the use of target reliability

level β that was employed in the calibration of API RP2A-LRFD (1993). However,

using a single target reliability index implies similar risk to different types of

facilities and may result in uneconomical or non-optimized solutions. For

reassessment applications, the Author did not justify adopting a single target



reliability level, especially in view of recent advances in risk and reliability analyses

and the need to develop a rational cost effective yet safe approach. Hence, a range

of target values was selected for this research to reflect the many variables.

In view of the variety of circumstances for a single pile, target reliability index

values of 1.5, 2.0, 2.11, 2.5, 3.0 and 3.5 were selected for the calibration of the

resistance factors. The selection of a target reliability level of 2.11 in this research

maintains consistency with the target level selected by Moses (1980) in the

calibration of API RP2A-LRFD (1993). The use of this target reliability level ties

the work back to API RP2A-LRFD (1993).

Application of the calibrated resistance factors in reassessment of existing platforms

requires a method to identify the appropriate target reliability level for that platform.

Table 4-20 provides a qualitative approach to assess an appropriate target reliability

level. To nominate an appropriate target reliability level, the likelihood of adverse

events and consequence of those events are required.

Depending on the nature and the specifics of the platform, likelihood of occurrences

can be expressed as a probability that an event can happen, or a chance that the pile

existing status will change and will require mitigation steps to take place. The

consequence reflects the perceived or actual impact that may occur if the given risk

materializes. The Engineer should consider the description of the consequence

outlined below and ask the question: “if the event occurs, how would it impact the

integrity of the pile in question?”

Combination of these two values (likelihood of occurrence and consequence) will

enable the determination of the relative risk level for a given pile. Risk items that

plot in the upper left-hand corner of the risk matrix represent the greatest risk to a

pile and require assigning a high target reliability level value, while those on the

lower right-hand side are of lesser concern and qualify for a low target reliability

level.

4.10. CALIBRATION OF RESISTANCE FACTORS

Using the calibration equations shown in Section 3.7, calibration of the resistance

factor was carried out for the partitioned groups. The results are shown in Table

4-21. The resistance factors were calculated for the total (toe + skin) capacity, since

the one-way WEA does not separate the skin and toe capacities.



The calibrated resistance factors are graphically presented in Figure 4-34. The

relationship demonstrated that driven piles require higher resistance factors than

those installed using supplementary methods. Meanwhile, piled foundations in soil

type CS require higher resistance factor than type SC soil.

The outcome of the calibration provided valuable insight into implicit assumptions

and limitations associated with the current empirical method included in the API

RP2A-LRFD (1993). The variation in resistance factors shown in Table 4-21

demonstrates that a single value for the resistance factor, which was used by Moses

(1980) to calibrate API RP2A-LRFD (1993), does not account for the various

parameters affecting the reliability of piled foundations. Those parameters are

identified and discussed in this section.

4.10.1. TARGET RELIABILITY LEVELS

Target reliability indices used in this research represents various consequences and

risk levels for the platform. For example, an unmanned wellhead platform jacket

that does not support storage or processing facilities and includes safety features

such as Emergency Shutdown Valves (ESDV) would represent relatively low

consequence of failure from a life and environmental point of view as its likelihood

of an adverse event and/or the consequence of failure is low. Hence, lower bounds

of the target levels may be considered for such a platform.

On the other hand, a manned compression platform with processing facilities for

sour gas service and storage facilities represents the other extreme and qualifies for

high target reliability levels. The use of a higher target reliability level for such a

platform is logical as it results in lower resistance factors. The use of lower

resistance factors limits any additional demand on an existing platform to produce

the desirable lower probability of failure. This approach resolves one of the

drawbacks of the deterministic method.

4.10.2. SOIL TYPES

Current static prediction methods including API RP2A formulation consider soil

type (cohesive or cohesionless) but do not consider that soils are usually made up of

combinations of cohesive and cohesionless soils, rather than one soil type. The

calibration studies conducted in this research highlighted the difference in behavior



between two types. The calibrated resistance factors for SC soils are 20%-25%

lower than those factors for CS piles shown in Table 4-21.

4.10.3. INSTALLATION METHODS

This research showed that the use of supplementary installation methods has a

dramatic effect on the capacity of piled foundations in carbonate soils. Back-

analysis of drilled piles revealed that that the capacity of piles installed using

supplementary methods are much lower than those installed without the need for

supplementary methods. This finding agrees with many scholars recommending

reductions in the resistance factors for conditions such as jetting and drilling.

This research also showed that the reduction in capacity of piles installed by drilling

varies from site to site and depends on the degree of disturbance to the surrounding

soils. The level of disturbance is in turn a function of the jetting or drilling

procedure, which varies widely from project to project and depends on the

individual contractor’s operation. Currently, there are no specific guidelines in the

API RP2A-LRFD (1993) or other codes for acceptable supplemental installation

procedures, which is due to the fact that API RP2A-LRFD (1993) addresses design

rather than reassessment. During design, piles are assumed to reach penetration

depth without the need for drilling or grouting. During installation, the presence of

harder soil strata or a boulder may make it necessary to use supplementary methods.

4.10.4. PENETRATION RATIO

Calibration of resistance factors for various penetration ratios employed the

statistical parameters defined in Table 4-15 as “likelihood” parameters and the

statistical parameters defined in Section 4.8.1 as “prior" parameters. The posterior

parameters for the various penetration ratios are shown in Table 4-22.

Resistance factors were calibrated for the various penetration ratios using target

reliability index (βΤ) of 2. The calibrated resistance factors are reported in Table

4-22. Inspection of the results indicates that the calibrated resistance factors are

insensitive to changes in the penetration ratios.

The calculations were repeated using a higher reliability index value (βΤ = 2.7) to

examine the sensitivity of various reliability index values on the computed



resistance factors. The results reported in Figure 4-35 reveal that resistance factors

are insensitive to penetration ratios but sensitive to target reliability index levels.

An increase of the target level from 2 to 2.7 (35% increase) resulted in up to 25%

reduction in the calibrated resistance factors.

4.11. SUMMARY

Despite the extensive knowledge gained from researching the behavior of piled

foundations driven in carbonate soils, international codes and standards such as API

RP2A-LRFD (1993) provides no guidance for reassessment of piled foundations in

those soils.

This Chapter calibrated resistance factors for axial capacity of piles in the carbonate

soils of the Arabian Gulf.

A database from actual pile installations in the Arabian Gulf was collated in this

research to derive bias factors for the piles. The bias factors were then used to

calibrate resistance factors that can be used with API RP2A-LRFD (1993)

formulation.

The bias factor for each pile was calculated by dividing the “actual” capacity by the

predicted capacity. The “actual” capacity was derived by multiplying the short term

pile capacity, which was obtained using the bearing graph module of GRLWEAP

software, by a setup factor of two.

Derivation of the short term capacity employed default values in GRLWEAP.

Sensitivity studies were conducted to investigate the effect of using those default

values on the computed short term capacity. It was shown that, for the relatively

low blow counts experienced in the Arabian Gulf, the computed ultimate capacities

are relatively insensitive to the changes in the hammer efficiency, pile segment

length or the cushion type. Hence, it was considered appropriate to utilize the

default values in GRLWEAP when computing the “actual” pile capacities.

The resulting statistics of the bias factor (mean and coefficient of variation) for the

complete database (138 piles) were 0.93 and 0.36, respectively. The calculated bias

factors were then partitioned into four groups in order to reduce the error in the bias

factor statistics. The resulting groups represented various physical situations that

can be expected in practice. The grouping covered installation method (driven or



drilled/ jetted), soil type (cohesionless underlain by cohesive soils and vice versa),

level of optimization in design and the penetration ratio.

However, partitioning the data reduced the size of the sample. To supplement the

limited size of the partitioned database and provide a more robust estimate of bias

factors, Bayesian updating was employed. The application of Bayesian update

required prior and likelihood distributions to derive the posterior distribution which

can be used in the calibration of resistance factors. The prior distribution was

evaluated through a literature survey. The likelihood distribution was derived using

the database collated in this research.

The mean values of the bias factors for the various groups were found to range from

0.65 to 1.12, and their error ranged from 0.26 to 0.40.

Using the statistics of the bias factors for each group, calibration of axial resistance

factors was conducted in this research using first order reliability method (FORM)

for the operating condition load case.

The methodology described in this research is valid for use in industry practice.

However, the bias factors and calibrated resistance factors should be viewed as

initial and only relevant to conditions in the Arabian Gulf where weakly cemented

carbonate soils are dominant. Further, these factors can be considered by

international committees such as ISO and the regulatory bodies in their further

development of guidelines and criteria for platform reassessment in the Arabian

Gulf. The use of the developed values in a different context can lead to significant

errors and incorrect prediction of capacities.



Table 4-1: The first sheet of the pile driving record (PDR) for a demonstration pile is shown. The PDR is in tabulated form showing the blow count at each penetration, hammer type (3000/150) used at the start of the driving operation and time of starting the driving operation (10th March 1978) at 5:00pm



Table 4-2: An intermediate (pen-ultimate) sheet of the PDR for the demonstration pile is shown. This sheet provides part of the history during the driving operation and shows that the operation was stopped when the penetration reached 201 ft at 5:04am and restarted on the 22nd March 1978 at 11:53 pm. This sheet also indicates a change in the hammer type (4600/150) from the initial hammer (3000/150)



Table 4-3: The last sheet of the pile driving record for the demonstration pile is shown. The PDR sheet indicates that the penetration depth was 262 ft (79.9m) and was achieved with 25 blow count per foot. This sheet also shows that the target penetration was achieved at 6:12pm on the 23rd March 1978 which means that the driving operation for this pile took around 2 weeks to complete (from 10th March 1978 to 23rd March 1978 as shown on the pen-ultimate PDR sheet)



Table 4-4: Recommended limiting skin friction and end bearing values by various researchers for design of piled foundations in carbonate soils

Author Year Limiting Skin Friction (kPa)

Limiting End Bearing (MPa)

McClelland 1974 20.0 5.0

Angemeer 1975 19.2 -

Agarwal 1977 28.7 6.7

Datta 1980 14.4 2.9

Abbs 1988 7.7 - 17.2 -

Nauroy & LeTirant

1983 2.9 -

Poulos 1988 5.7-9.6 -

LeTirant 1994 4.8 - 9.6 3.9 – 7.7



Table 4-5: Summary of replies of a questionnaire was sent to various Geotechnical experts to recommend limiting skin friction and end bearing values for piled foundations in carbonate sands (Lacasse and Goulois, 1989)

Parameter Degree of Cementation

Expected Value

`Mean Value

Uncemented 2 – 8 4.1

Weakly cemented 3.5 - 8.1 5.1 Limiting unit end bearing (MPa)

Well cemented 6 - 12 7.6

Uncemented 5 - 50 22

Weakly cemented 5 - 30 22 Limiting unit skin friction (kPa)

Well cemented 7 - 76 24

Uncemented 20 - 30 24

Weakly cemented 32 - 50 42 Bearing Capacity Factor

Well cemented - -

Uncemented - -

Weakly cemented 10 - 40 23 Soil Pile Friction Angle (degree)

Well cemented 5 – 40 26

Uncemented - -

Weakly cemented 0.4 – 0.6 0.47 Coefficient of lateral earth pressure

Well cemented 0.2 – 0.8 0.46



Table 4-6: Using the results of the questionnaire shown in Table 4-15, this table presents the limiting soil parameters selected for this research. Inspection of soil reports in the Arabian Gulf revealed weak degree of cementation

Parameter Units Mean Value used in Research

Limiting unit end bearing MPa 5.1

Limiting unit skin friction kPa 22

Bearing Capacity Factor 42

Soil Pile Friction Angle degree 23

Coefficient of lateral earth pressure 0.47



Table 4-7: This table presents a printout of the APIPILE spreadsheet showing input data of one pile in the database. The printout shows all required parameters in red while the black color shows the calculations performed in the spreadsheet APIPILE. The input data reflects the limiting soil parameters identified in Table 4-6

CHAPTER 4: PILE RESISTANCE FACTORS


Table 4-8: Extract from APIPILE Excel spreadsheet developed in this research to predict axial pile capacity in accordance with API RP2A-LRFD (1993). The spreadsheet output shows the calculated friction capacity along the pile shaft and the bearing value at the pile tip at the bottom of each layer

CHATER 4: PILE RESISTANCE FACTORS 153


Table 4-9: Compilation of basic Smith soil model parameters by various Authors and the selected parameters that were used in this research

Damping Constant Quake

Reference Soil Type Skin Friction Js, s/m

End Bearing Jp s/m

Side mm

End1 mm

Clay 0.65 0.01-1.0 2.5 2.5

Sand 0.15 0.33-0.65 2.5 2.5 Tomlinson, 1995

Silt 0.33-0.50 0.33-1.5 2.5 2.5

Clay 0.098 0.492 2.54 2.54

Sand 0.262 0.492 2.54 2.54 Stevens et al., 1982

Silt - - - -

Clay 0.656 0.01 2.54 2.54

Sand 0.164 0.49 2.54 2.54 Tagaya, 1979

Silt - - - -

Soft Clay 0.26 0.66 5.08 5.08

Firm Clay 0.23 0.50 3.81 3.81

Stiff Clay 0.20 0.50 2.54 2.54

V. Stiff Clay 0.16 0.50 2.54 2.54

Hard Clay 0.10 0.50 2.54 2.54

Roussel, H.J., 1979

Sand 0.26 0.50 2.54 2.54

Clay 0.65 0.50 2.50

Silts 0.16 0.50 2.50 D/60 (2.5)

Sand 0.16 0.50 2.50 D/120 (2.5)

GRLWEAP (this research)

Rock 0.16 0.50 2.50

1 Values in brackets represent unplugged conditions



Table 4-10: Extract from the spreadsheet APIPILE developed in this research showing calculation used to predict the Soil Resistance to Driving (SRD) at the top and bottom of each layer



Table 4-11: Summary of reported soil setup factors for various soil types in various sites around the world (Rausche et al., 1996)

Predominant Soil Type along Pile Shaft

Range of reported

Setup Factors

Recommended Setup Factors

Number of Sites (% of database)

Clay 1.2-5.5 2.0 7 (15%)

Silt-Clay 1.0-2.0 1.0 10 (22%)

Silt 1.5-5.0 1.5 2 (4%)

Sand-Clay 1.0-6.0 1.5 13 (28%)

Sand-Silt 1.2-2.0 1.2 8 (18%)

Fine Sand 1.2-2.0 1.2 2 (4%)

Sand 0.8-2.0 1.0 3 (7%)

Sand-Gravel 1.2-2.0 1.0 1 (2%)



Table 4-12: A list of the various methods commonly used to interpret pile-loading tests and an example application of these methods to interpret static loading test (Fellenious, 1980)

Method Development Year

Load Limit (kips)

Davisson Offset Limit Load 1972 375

Dr Beer Yield Load 1968 360

Brinch-Hansen 80% Criteria 1963 418

Chin-Kondner Extrapolation 1971 475

Decourt Method 1999 474

Average 420



Table 4-13: Results of dynamic pile monitoring results obtained from a confidential report of a real life offshore installation

Penetration Actual SRD Setup Increase Pile ID

m Remarks

MN MN

A1 55.75 EOD 10.3

EOD 11.5 A2 56.05

BOR 15.9 15.9-11.5 = 4.4

EOD 9.5 B1 56.00

BOR 15.6 15.6-9.5 = 6.1

B2 56.00 EOD 10.0



Table 4-14: Back analysis results from GRLWEAP and a comparison with CAPWAP results. The results demonstrate that the accuracy of the developed back-analysis method is within 5% of the accuracy using CAPWAP

PILE A1 A2 B1 B2

Blow count (blows/ft) 23 25 20 23

Short term Capacity (MN) 7.7 7.99 7.42 7.70

Setup Factor 2 2 2 2

Long term Capacity (MN) 15.4 15.9 14.8 15.4

Capacity from CAPWAP (MN) 15.5 15.9 15.6 15.5

Percentage of Error 0.3% 0.5% 4.9% 0.3%



Table 4-15: The computed bias factors were subgrouped to represent various penetration ratios (<50, 50-75, 75-100, >100). Statistical parameters were calculated for each penetration ratios. The computed statistical parameters were considered as likelihood values when Bayesian updating was applied

L/D <50 50-75 75-100 >100 ALL

λ = mean 0.95 0.93 0.93 0.89 0.93

COV 0.42 0.34 0.32 0.47 0.36



Table 4-16: Summary of the statistical parameters of the bias factors computed in this research. The bias factors shown in this table were considered as likelihood values when Bayesian updating was applied

Installation Method Soil Type Mean COV

Complete Database All 0.93 0.36

SC 0.77 0.34

CS – Effective Design 1.12 0.26 Driven

CS – Conservative Design 0.97 0.34

Drilled All 0.65 0.40



Table 4-17: Major sources of bias and variability for a single pile (Bea, 1983)

Source Bias COV

Construction – pile penetrations 0.9-1.0 0.05-0.10

Soil sampling and testing (wire line) 1.5-2.0 0.10-0.20

Rate of cycling effects 1.3-2.0 0.20-0.30

Cyclic (one-way) loading effects 0.9-1.0 0.10-0.20

Pile compressibility and strain softening effects 0.9-1.0 0.10-0.20

Template pile system capacity versus most heavily loaded pile capacity

1.8-3.0 0.10-0.20

Engineering interpretation of shear strength data 1.1-2.5 0.10-0.30

Static capacity prediction (API) 1.1-1.3 0.30-0.50

Soil/ pile variability across site 1.0 0.10-0.35

Maximum loading annual 0.3 0.50-0.80

(lifetime) (0.8) (0.3-0.45)



Table 4-18: Hamilton and Murff (1992) proposed the statistical parameters shown in this table. These parameters were published prior to the introduction of API RP2A-LRFD (1993) but were not used in the calibration of API RP2A-LRFD (Hamilton and Murff, 1992)

Function Parameter Clay Sand

Bias (Operating) 0.90 1.20

Bias (Extreme) 1.30 1.20 Pile Capacity

COV 0.30 0.40



Table 4-19: Bayesian updating was employed in this research to arrive at the “posterior” statistical parameters for the resistance. To apply Bayesian updating in this research, “prior” and “likelihood” statistical parameters were required. The “prior” statistical parameters are based on analysis of data reported in the literature and described in Section 4.8.1. The “likelihood” parameters were determined on the basis of analysis of the database collated in this research and reported in Table 4-16

Prior Likelihood (Table 4-16)

Posterior Installation Method

Soil Type/ Condition

Mean COV Mean COV Mean COV

SC 1.30 0.30 0.77 0.34 0.93 0.23

CS Effective Design 1.30 0.30 1.12 0.26 1.18 0.20 Driven

CS Overconservative Design 1.30 0.30 0.97 0.34 1.11 0.23

Drilled All Soils 1.30 0.30 0.65 0.40 0.85 0.26



Table 4-20: A proposed method to determine appropriate target reliability level for reassessment of an existing platform. The risk matrix shown in this table can be used for a qualitative assessment to establish the relative risk level for a given pile. To assess the target reliability level for a certain application, there is a need to answer two questions. The first question identifies the likelihood of an adverse event and the second question defines the consequences if this event takes places and its impact on the integrity of the pile in question. For example, if an event is almost certain to occur but its effect on the platform pile is insignificant; the table is entered with those criteria to define a target level of 2.5. This target level can then be used with Table 4-21 to define the appropriate resistance factor for reassessment of a pile

Consequence

Catastrophic damage in human life, environment and financial goals

Major threatening goals and objectives – requires close management

Severe and would require significant adjustment to function

Minor and would threaten element of a function

Insignificant and routine procedures are sufficient to deal with consequences

Lik

elih

ood

Manned Nonevacuated sour gas

Manned

nonevacuated

Manned

Evacuated

Unmanned

Nonevacuated

Unmanned

Evacuated

Almost Certain (>90%)

3.5 3.5 3.0 2.5 2.5

Likely

(65%-90%) 3.5 3.0 2.5 2.5 2.0

Moderate

(<65%) 3.0 3.0 2.5 2.0 1.5

Unlikely

(10%-35%) 3.0 2.5 2.0 1.5 1.5

Rare

(<10%) 2.5 2.5 2.0 1.5 1.5



Table 4-21: Resistance factors were calibrated in this research using the “posterior” statistical parameters defined in Table 4-19 and employing various target reliability levels. Inspection of the results reveals that the calibrated resistance factors are sensitive to the target reliability levels. This finding highlights the importance of defining a target level when reassessment is conducted. By comparison, API RP2A-LRFD (1993) nominates a single value for the resistance factor, which is based on the use of a single target reliability level (β = 2.11). Further, this research also concluded that resistance factors are a function of other parameters such as soil type and installation method, whereas API RP2A – LRFD (1993) provides a single value for the resistance factor regardless of the driving method or the soil type

Calibrated Resistance Factor for βT

Installation Method Soil Type Mean COV

1.5 2.0 2.11 2.5 3.0 3.5

SC 0.93 0.23 0.80 0.69 0.64 0.60 0.52 0.45

CS Optimum 1.18 0.20 1.05 0.92 0.88 0.81 0.71 0.62 Driven

CS conservative

1.11 0.23 0.95 0.82 0.76 0.71 0.62 0.54

Drilled All 0.85 0.26 0.70 0.60 0.54 0.51 0.44 0.38



Table 4-22: Calculated “posterior” statistical parameters for various penetration ratios using Bayesian approach. The “prior” statistical parameters are based on analysis of data reported in the literature and described in Section 4.8.1. The “likelihood” parameters were determined on the basis of analysis of the database collated in this research and reported in Table 4-15. The calibrated resistance factors were based on the use of a target reliability index level of 2 and were found to be insensitive to changes in the penetration ratio

L/D <50 50-75 75-100 >100 driven drilled ALL

Rλ 1.30 1.30 1.30 1.30 1.30 1.30 1.30

Prio

r

COVR 0.30 0.30 0.30 0.30 0.30 0.30 0.30

Rλ 0.95 0.93 0.93 0.89 0.96 0.76 0.93

Like

lihoo

d

COVR 0.42 0.34 0.32 0.47 0.34 0.43 0.35

Rλ 1.13 1.08 1.07 1.11 1.10 0.98 1.08

Post

erio

r

COVR 0.25 0.23 0.32 0.26 0.23 0.25 0.23

φ 0.82 0.81 0.81 0.79 0.83 0.71 0.81



Figure 4-1: Diagrammatic representation of the soil/ pile database showing a pile that was driven using various hammers in soil strata. The graph shows the relationship between the resistance of the pile and penetration depth. The diagram shows that various hammers (Menck 4600/150, 3000/150) were used to drive the pile to the desired penetration depth. Firstly, Menck 3000/150 hammer was used to drive the pile from the seabed to the desired penetration depth of 98m



Figure 4-2: A comparison of the calculated driving force (left) and stresses (right) against measured values as suggested by Tagaya et al. (1979) method. The measured response was taken from an actual offshore installation in the Arabian Gulf, which was reported by Tagaya et al. (1979)



Figure 4-3: Input parameters used for a demonstration pile are shown here. This 1219mm diameter steel pile is 105m long and penetrates 79.9m into the soil. The pile was driven by MENCK MRBS4600 hammer with an assumed efficiency of 67% and 1.5m stroke. The water depth measured from the water surface to the mudline is around 25m



Figure 4-4: This chart shows the complete pile driving record for a demonstration pile. The soil profile and the blow count at the pile tipping depth are of interest for the back-analysis calculations



Figure 4-5: This chart shows the results of the GRLWEAP Bearing Graph analysis. The output screen shows all input parameters that were assumed in the design such as the assumed efficiency in addition to stresses in the pile and the relationship between blow count and predicted capacity



0.0

0.5

1.0

1.5

2.0

2.5

0 50 100 150 200 250 300

Elapsed Delay During Driving (Hours)

SRD

Fric

tion

ratio

Figure 4-6: Estimate of setup factor was made using various start/stop data of the pile driving record. The graph shows the relationship between SRD to elapsed time during driving. Inspection of the trend in the chart indicated that a setup factor of 2 fairly represented long term effect of setup



Figure 4-7: Scatter plot of the ratio of GRLWEAP capacity to static loading test capacity at the Beginning of Restrike (BOR) and End of Driving EOD) conditions (Thendean et al., 1996)



Figure 4-8: Blow count versus depth diagrams for four piles which were plotted by an installation contractor in during actual pile driving installation in the Arabian Gulf. The pile driving records were collated in this research. The pile driving records show that all piles were driven to around 56m with blow count of approximately 25 blows per foot



Figure 4-9: GRLWEAP input data screen for the analysis of a pile to validate the back-analysis results. The pile has a cross section of 1193cm2 and penetrates 56m into the soil stratum. The pile was driven by a Menck MHU 600 hammer. The soil parameters used in this analysis were selected from Table 4-9. The computed pile capacity from GRLWEAP represented short term capacity. The long term capacity was computed by allowing for a setup factor of 2 and compared to the output from the dynamic monitoring results for the pile in question



Figure 4-10: Predicted axial capacity using GRLWEAP of the pile that had been subject to dynamic monitoring. This pile was used to validate the back-analysis procedure



Figure 4-11: Pile makeup of the dynamically monitored pile. The pile makeup consists of 9 sections with a uniform outside diameter of 1219mm. The minimum wall thickness used in the pile makeup was 20mm and the maximum wall thickness was 44mm at the mudline



Figure 4-12: Modeling of the pile-soil interaction in GRLWEAP requires a breakdown of the system at each layer and at each change in pile section



Figure 4-13: Results of GRLWEAP analysis when average pile wall thickness across the whole pile length was assumed for the pile instead of using the actual wall thickness for every pile section



Figure 4-14: Mechanics of wave propagation in a pile (Cheney and Chassie, 1993). The mechanics of driving a pile was used to explain the reason behind the divergence in results when an average pile wall thickness - instead of actual variable thickness - was used when modeling pile wall thicknesses in GRLWEAP



0

200

400

600

800

2460 4920 7380 9840 12300 14760 17220 19680

Ultimate Capacity (kN)

Blo

wco

unt (

Blo

ws/

ft)

Figure 4-15: Sensitivity analysis of the computed ultimate capacity in GRLWEAP as a result of changing hammer efficiency. The curves show that the computed capacity is relatively insensitive to the assumed efficiency for low blow count such as those experienced in the Arabian Gulf



Figure 4-16: Influence of changing segment length on the computed pile capacity showing that pile capacity is insensitive to the segment length for the range of pile capacities experienced in the Arabian Gulf

0

250

500

750

1000

1250

1500

2460 4920 7380 9840 12300 14760 17220 19680


Blo

wco

unt (

Blo

ws/

ft) 5m segment

2m segment

1m segment



0

200

400

600

800

1000

2460 4920 7380 9840 12300 14760 17220 19680


Blo

wco

unt (

Blo

ws/

ft)

Figure 4-17: Sensitivity of using various cushion types and manufacturers on the computed ultimate capacity. The plot shows that pile capacity is relatively insensitive to the selected cushion type as soil resistance, rather than pile characteristics, dominates the axial capacity



Figure 4-18: Bias factors for the complete set of data comprising 138 piles. The trend shows similar number of piles with bias factor above and below 1.0. Hence, the trend implies no bias in the overall database



( , )

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0

0.5

1.0

1.5

2.0

2.5

< >5.0% 5.0%90.0%0.381 1.474

@RISK Trial VersionFor Evaluation Purposes Only

Figure 4-19: Statistical analysis of the complete set of data shows that a normal distribution is most appropriate. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in carbonate soils



y = 2.9599x - 2.7457R2 = 0.9802

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4

Bias Factors

Red

uced

Var

iate

Figure 4-20: Statistical analysis of the complete set of data using the parametric method shows that a normal distribution is most appropriate as described in Section 3.4.2. The statistical analysis produced bias and coefficient of variation, λ = 0.93, COV = 0.36 for axial capacity of piled foundations in carbonate soils



0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100 110

Pile Tip Penetration Ratio (L/D)

Bia

s Fa

ctor

Figure 4-21: Scatter plot showing bias factors for piles installed using supplementary installation methods. The plot shows that, in situations when piles are installed using supplementary methods, the use of API RP2A to predict pile capacity significantly (40% to 80%) overestimated the capacity and therefore is on the unsafe side



0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

< >5.0% 5.0%90.0%0.219 1.296


Figure 4-22: This chart shows a histogram of all piles that were installed using supplementary installation methods. An assumption of normal probability distribution provided the best fit to the data as shown above. The statistical parameters of the drilled/ grouted piles were computed as λ = 0.65 and COV = 0.40



y = 0.324x + 0.7576R2 = 0.9209

0.0

0.4

0.8

1.2

1.6

-3 -2 -1 0 1 2 3

Normal Quantile

Bia

s Fa

ctor

Figure 4-23: An assumption of normal probability distribution for piles installed using supplementary methods resulted in linear probability plot. The statistical parameters of the drilled/ grouted piled foundations were computed as λ = 0.65 and COV = 0.40



0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100 110


Bia

s Fa

ctor

Figure 4-24: Scatter plot for soil Type SC which describes predominant cohesive soil profile underlain by cohesionless soil



0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100 110


Bia

s Fa

ctor

Figure 4-25: Scatter plot for soil type CS which describes predominant cohesionless soil profile underlain by cohesive soil



0.0

0.5

1.0

1.5

2.0

2.5

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

< >5.0%90.0%0.345 1.199


Freq

uenc

y (x

10-3

)

Figure 4-26: Probability plot of soil type SC showing that a normal distribution fits the data with mean = 0.77, standard deviation = 0.26, COV = 0.34



y = 0.2575x + 0.7719R2 = 0.9459

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

-3 -2 -1 0 1 2 3

Normal Quantile

Bia

s Fa

ctor

Figure 4-27: Probability plot of soil type SC confirming that the assumed fitted normal distribution is appropriate with mean = 0.77 and COV = 0.34. This is in line with the non-parametric analysis using @RISK which indicated similar statistical parameters



0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100 110


Bia

s Fa

ctor

Figure 4-28: Statistical parameters of the bias factors for piles driven in soil type CS was subgrouped further to piles with optimum design against those with overconservative design. This plot shows statistical parameters of bias factors for piles which are optimally designed



0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

30 40 50 60 70 80 90 100 110


Bia

s Fa

ctor

Figure 4-29: Statistical parameters of the bias factors for piles driven in CS soils. This plot shows the scatter diagram of the bias factor for those piles with an overconservative design. The definition of overconservative design in this research describes piles with a factor of safety of 4 or more according to API RP2A-WSD (2000). The API RP2A-WSD (2000) only requires piles to be designed for a factor of safety of 2 under operating conditions



( , )

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

< >5.0% 5.0%90.0%0.425 1.518


Figure 4-30: Probability plot for soil type CS with conservative design, The analysis showed that a Normal distribution provided the best fit to the data with statistical parameters λ = 0.97, COV = 0.34



y = 0.3234x + 0.9713R2 = 0.9153

0.0

0.4

0.8

1.2

1.6

2.0

2.4

-3 -2 -1 0 1 2 3

Normal Quantile

Bia

s Fa

ctor

Figure 4-31: probability plot for soil type CS with conservative design, λ = 0.97, COV = 0.34. The results of this non-parametric statistical analysis are consistent with the parametric analysis described in Figure 4-30



Figure 4-32: Probability plot of soil type CS with optimum design. The probability plot shows that a normal distribution provides the best fit to the data with statistical parameters λ = 1.12, COV = 0.26.



y = 0.2798x + 1.1192R2 = 0.9067

0.0

0.4

0.8

1.2

1.6

2.0

-3 -2 -1 0 1 2 3

Normal Quantile

Bia

s Fa

ctor

Figure 4-33: Probability plot of soil type CS with optimum design, λ = 1.12, COV = 0.26



Figure 4-34: Calibrated resistance factors for the various subgroups identified in this research. The chart shows that, for a certain target reliability level, the resistance factor for a pile driven using supplementary methods should be lower than that for a pile driven without the need for drilling or jetting. The plot also shows that API RP2A-LRFD (1993) recommends a single value for the resistance factor and does not address the various conditions that affect the value of the resistance factor

0.2

0.4

0.6

0.8

1.0

1.2

1.5 2 2.5 3 3.5

Target Reliability Index

Res

ista

nce

Fact

or

Drilled - All Soil Types

Driven - Soil Type SC

Drilled - Soil Type CS Optimum

Drilled - Soil Type CS Conservative

Target reliability index (βT = 2.11) used in calibrating API-RP2A-LRFD (1993) and its corresponding resistance factor (φ = 0.7)



Figure 4-35: A plot showing the effect of penetration ratio on the calibrated resistance factors. The analysis shows that the calibrated resistance factors are insensitive to various penetration ratios

0.60

0.70

0.80

0.90

<50 50-75 75-100 >100

Penetartion Ratio

Res

ista

nce

Fact

or

β = 2.7

β = 2.0


Chapter 5.

CALIBRATION OF OPEN AREA LIVE LOADS

5.1. BACKGROUND

Section 2.8.1 revealed that open area live load (OALL) on offshore platforms has

not been explored, quantified or addressed in international codes and standards.

Hence, there was a need to develop OALL that can be used in reassessment of

existing platforms.

Open areas on offshore platforms provide areas where equipment can be laid during

normal operation and during shutdown. Open areas have the following benefits:

• Permit access for normal operation and maintenance,

• Locate wells, production and pipeline facilities to reduce the risk from potential

events,

• Permit access for operators to perform necessary emergency shutdown actions in

an emergency situation,

• Facilitate personnel escape, evacuation and rescue procedure in the event of an

emergency,

• Permit access for fire fighting or other emergency response,

• Protect critical facilities from damage during normal operations or emergency

situations,

• Segregate toxic or highly reactive materials and high risk facilities,

• Separate continuous ignition sources from probable points of release of

flammable materials,

• Separate air intakes (building pressurization, combustion air to turbine or

heater),

• Separate equipment to minimize involvement of or escalation to adjacent

facilities in a fire or explosion, and

• Provide site security

CHAPTER 5: OPEN AREA LIVE LOADS 203


Calibration of OALL required a database that models the nature of offshore

platforms. However, the literature survey conducted in this research identified lack

of a suitable database to assess live loads on offshore platforms. Available live load

databases only covered buildings and structures other than offshore platforms as

discussed in Section 2.8.2. Hence, there was a need for a relevant database for the

purpose of this research.

In addition to the requirement for an appropriate database, the discussions in Section

2.8.3 revealed that the probability model used to derive live loads for buildings in

codes and standards was not suitable to derive OALL for offshore platforms and an

alternative probability model was required.

This Chapter identifies the nature of the database required to develop OALL,

describes the data collection efforts in this research and defines the probabilistic

model used in this research to derive OALL effect on piles of offshore platforms.

5.2. DEFINITIONS USED IN DEVELOPMENT OF OALL

The OALL concept adopted in this research is consistent with the uniformly

distributed live loads used in design codes such as ASCE Standard 7-05 for

buildings. As such, OALL developed in this research can be used with the API

RP2A-LRFD (1993) code provisions without the need to conduct additional

sophisticated numerical analyses.

The process of developing OALL in this research was to calculate the mean lifetime

maximum live load on a pile given the arbitrary point-in-time survey data.

The mean lifetime maximum live load effect considers the variables involved in the

live load process and is distinct from the data obtained in surveys, which represent

arbitrary point-in-time. Whereas the loads measured by live load surveys give an

instantaneous picture of the loading on a floor, the mean lifetime maximum load

effects account for spatial and temporal variations in the load components.

Using the survey data collated in this research, statistical parameters of OALL were

developed and can be divided into 2 sets:

• Statistical parameters associated with arbitrary point-in-time survey data, and

• Statistical parameters of the lifetime maximum live load on a pile, which is

obtained using influence coefficient and extreme value analysis (Ang & Tang).



5.3. CALIBRATION MECHANICS OF OALL

The derivation of OALL started with identifying the nature of live loads on offshore

platforms, which was then used to conduct arbitrary point-in-time surveys. The

survey results produced a database that was used to derive OALL on offshore

platforms. The database consisted of instantaneous loads from survey data, which

are termed arbitrary point in time values, since it was not feasible to conduct load

surveys lasting several years. Consequently, it became necessary to employ a

stochastic process to these load values.

The arbitrary point-in-time survey data points were subjected to a statistical analysis

to identify the most suitable distribution and obtain their statistical parameters. This

step was required because the statistical parameters heavily rely on the distribution

type as described in Section 3.4. To obtain the statistical parameters, the data was

plotted using a scatter plot to check for linearity, presence of outliers and unusual

points.

Using the arbitrary point-in-time statistical parameters, an appropriate stochastic

process was employed to treat the arbitrary-point-in-time data and derive the

maximum load effects.

Using the statistical parameters of the database and the maximum load effect,

extreme value analysis was employed to produce the mean lifetime maximum value

for a single “realization” using the methodology described in Section 3.8.2. The

mean lifetime maximum load effect on a pile was developed using the asymptotic

distribution of the extreme values from the single “realization” case.

The OALL effect on a pile was converted to equivalent uniformly distributed load

using tributary area. The derived OALL covers loads on open deck areas around

equipment supported by floor grating and plating. This approach is compatible with

that used in calibrating EUDL in ASCE Standard 7-05.

5.4. NATURE OF LIVE LOADS ON OFFSHORE PLATFORMS

Unlike office and residential building surveys, which target furniture and persons to

represent live loads on the floors of building structures, the nature of live loads on

offshore platform topsides was not easily identifiable.

The Author conducted a number of surveys while on job assignments on offshore



platform sites to define the nature of live loads on offshore platforms. The Author’s

surveys represent the arbitrary point-in-time data, which is similar in nature to the

furniture surveys used by ANSI to derive load data for buildings as described in

Section 2.8.2. The surveys revealed that open areas on offshore platforms are

usually loaded with equipment or parts of equipment or tools used in maintenance

such as scaffolding. The Author recorded all items such as scaffolding and minor

pieces of equipment that were found on various platform open areas on platform

deck structures.

On examining the magnitude of the surveyed data against the platform deck areas, it

was evident that a probabilistic analysis would result in relatively minor OALL

design values. Figure 5-1 shows an example of live loads on an offshore platform

during normal operating conditions. The small OALL magnitude would be due to

the large open deck areas in addition to the relatively small surveyed weights.

Several discussions with offshore maintenance and operations personnel in the

Arabian Gulf were conducted to identify if other equipment would normally be

located on open areas. The discussions were conducted with a number of senior

level personnel with a combined experience of over 100 years in maintenance and

operations.

The discussions revealed that during operation, minor items would be expected on

open areas to comply with house-keeping rules on offshore platforms. House-

keeping rules for operation and maintenance of offshore platforms address safety

issues which prevents items on open areas during normal operating conditions.

However, the maintenance and operation personnel agreed that this would not be the

case during shutdown. In such case, various activities could take place

simultaneously which would make it possible for the open areas to be completely

occupied by various pieces of equipment. Further, the discussions revealed that

floor live loads on offshore platforms should account for operation and maintenance

of equipment and possible modifications or changes in use during shutdown.

During installation or maintenance, portions of equipment may be laid down at

various locations on the floor.

The discussions also indicated that the characteristic of those pieces of equipment

could generally be considered similar to the population of equipment on offshore

platform decks but need not be correlated to each other. For example, the



replacement or maintenance of a motor, which is part of the compressor skid,

similar to that shown in Figure 5-2 could be scheduled during a shutdown. The

motor weight is related to the weight of the turbine itself, but other equipment pieces

that may be present on the open topside deck have no correlation to the motor

weight or the turbine weight or to each other’s weight. Other equipment in the same

area may include blasting equipment, scaffolding packs and welding machines or

other tools which are not correlated to the weight of the rotor or to each other.

The discussions also revealed that very large pieces of equipment or those with

heavy weights are usually not maintained offshore due to space or lifting

restrictions. Typically, a platform would have a crane with a limited lifting

capacity. The capacity of those cranes is usually limited to around 10 tonnes on the

main deck and would therefore be insufficient to lift equipment pieces heavier than

10 tonnes. When large pieces of equipment require maintenance offshore, they are

disassembled in their fixed location and their components are placed on the open

area. It is generally true that the total weight of the large and heavy equipment

occurs only in its fixed location on the floor and can only be considered in the

structural design as dead loads.

In summary, open area live loads on offshore platforms can be described by

equipment pieces with weights less than the crane capacity on the platforms.

5.5. EQUIPMENT LOAD DATABASE

Using the characterization of the live load on offshore platform decks, a survey of

actual pieces of equipment was conducted. The survey covered over 400 pieces of

equipment supported on over 35,000m2 of combined topside deck area on offshore

platforms in the Arabian Gulf.

The database of all equipment is presented in Appendix A and includes footprints

and weights for over 400 pieces of equipment from 60 platforms. Each point in this

database defines the equipment name, its weight, footprint and location on the

topside deck. The data was grouped to different deck usage (main, mezzanine and

cellar decks).

The equipment data were extracted from actual offshore visits, layout drawings,

vendor drawings, and equipment data sheets. Inspection of the records showed that

the surveyed equipment pieces were installed from the 1960s until 2003. Hence, the



survey reflects the time-dependent nature of the live loads that could be present on a

platform deck.

Figure 5-3 presents a scatter plot of equipment weights against the respective

footprint and shows that the majority of the surveyed equipment weighs less than 20

tonnes (200kN).

5.6. SUBGROUPING THE EQUIPMENT DATABASE

Inspection of Table 2-4 reveals that surveys which were used to derive live loads in

the development of building codes such as ANSI A58 were only grouped to building

functions. For example, the surveyed areas for residences were 204,000 ft2 but no

further subgrouping was considered to determine the statistical parameters of

various room usages (open space such as living rooms against closed rooms such as

bedrooms). The approach used in building codes was examined for use with

offshore platforms but with the following two questions. The first question

addressed the subgrouping of the data to cover various platform functions and the

second question covered the treatment of the data within that function.

5.6.1. SUBGROUPING BY PLATFORM FUNCTION

In this research, it was not possible to divide the database in accordance with

platform function due to the large number of functions that can be assigned to

platforms. Table 3-1 presented seven functions associated with the database (e.g.

compression, wellhead, glycol, living quarters, riser and water disposal) but other

functions (e.g. utility, observation) could also be envisaged.

Due to the difficulty of obtaining data for offshore platforms, it was not possible to

obtain sufficient data for every conceivable platform function. It is difficult to

obtain real data for offshore platforms when compared to building structures due to

confidentiality issues. Owners and operators allow limited personnel to visit their

facilities. Hence, the amount of data that can be obtained is limited. Subdividing a

limited amount of data with platform functions would result in a small sample for

each group which would affect the calculated statistical parameters. Consequently,

it was considered impractical to divide the database of this research in a similar

manner to that used in building codes and a single database was contemplated

without further grouping the data.



5.6.2. SUBGROUPING BY LOCATION ON PLATFORM

The Author’s experience with industry rules and best practices related to layout of

equipment on offshore platforms revealed that heavy and large pieces of equipment

are usually placed on upper decks. Such layout is commonly adopted to facilitate

maintenance and removal of those heavy equipment by independent cranes in the

event of any need to overhaul or replace those equipment pieces Hence, it was

considered appropriate to subgroup the equipment database in accordance with their

location on the platform deck, which reflected the philosophy usually adopted

during layout of equipment on offshore platforms. Equipment pieces on lower

decks are handled by monorails with relatively smaller safe working loads (SWL),

while larger equipment on the upper deck are usually handled by jib cranes with

larger SWL.

Figure 5-4 shows an elevation view of an actual platform in the Arabian Gulf.

Inspection of the layout reveals that larger pieces of equipment are located on the

upper deck while smaller size equipment pieces tend to be located on lower decks.

Hence, grouping the database into two groups reflects layout practice and is also

easy to comprehend when performing deterministic reassessment.

5.6.3. SUBGROUPING BY CRANE AND MONORAIL SWL

Handling equipment pieces on a platform requires use of cranes and monorails. On

each platform, there is usually a number of monorail beams and one or two cranes.

Determination of the lifting crane capacity is usually carried out in a “materials

handling” study during platform original design and crane capacity is usually

optimized to reduce capital expenditure (CAPEX) by placing heavier equipment on

the upper deck.

Inspection of the data revealed that crane Safe Working Load (SWL) ranges from 5

to 10 tonnes on upper decks, while SWL of monorails is usually limited to 5 tonnes

and can be as low as one tonne on lower decks.

In this research, a limitation was imposed on the database to reflect industry practice

and common crane and monorail SWL on various decks. The database was limited

to 10 tonnes and 5 tonnes for equipment on upper and lower decks, respectively.



5.7. STATISTICAL PARAMETERS OF EQUIPMENT

WEIGHTS

Statistical analysis was carried out for two sets of equipment weights. The first

dataset is described in Section 5.7.1 and covered equipment weights on lower decks.

The second dataset covered equipment weights on the upper deck and is described

in Section 5.7.2.

A definition of the probability distribution of the data was required to establish the

statistical parameters of equipment weights for the upper deck and lower decks.

Several probability models such as Normal, Gumbell, Exponential and one-sided

Normal distribution for the equipment data were examined and the validity of each

distribution model was tested by performing a co-linearity test of the probability

paper graph. The selected distribution was confirmed by visual inspection.

5.7.1. ON LOWER DECKS

As previously discussed in Section 5.6.3, the complete database includes equipment

weights which can not be lifted during shutdown operations because of the

limitations on materials handling equipment (monorails) on lower decks of

platforms. As the interest in this research was focused on equipment weights that

can physically be placed on the open areas, the database only considered those

weights that can physically be lifted by a monorail hoist limited to SWL of 5 tonnes.

The statistical parameters of equipment on lower decks are shown in Figure 5-5 with

mean of 36.1kN and standard deviation of 18.4kN. The calculated COV for the

arbitrary point-in-time equipment weights is 0.51.

For comparison, the overall dataset on the lower deck was also examined to identify

the statistical parameters if the effect of monorail capacity was disregarded. Figure

5-6 shows a histogram of the complete dataset of the equipment on lower decks. An

assumed distribution was fitted to the data. The fitted distribution shows an

Exponential distribution and its parameters. Using the Exponential distribution, the

mean and standard deviation of the equipment weights on lower decks were

computed as 135.4kN and 134.8kN, respectively.



5.7.2. ON UPPER DECK

When the equipment weights on the upper deck were analyzed to reflect a crane

SWL of 10 tonnes, the distribution shown in Figure 5-7 was representative of the

data with a mean of 81.5kN and a standard deviation of 30.6kN.

When the effect of cranes is disregarded, Figure 5-8 provides a histogram and a

fitted distribution for equipment on the upper deck distribution using @RISK and

the non-parametric approach described in Section 3.4.2. The analysis reveals that an

Exponential distribution provided the best fit to the equipment data on upper deck

with a mean of 627kN and a standard deviation of 638kN.

5.7.3. CONCLUSION

A comparison of the results above indicates the significant effect of including the

crane/ monorail capacity in determining the statistical parameters of the equipment

weights. Further, as the statistical parameters directly affect the calculated OALL, it

was clear that the effect of the crane/ monorail capacity would be dominant on the

outcome of this research and that disregarding the effect of crane would lead to

overconservative results.

Consequently, it was necessary to consider the effect of crane/ monorail effect on

the computed statistical parameters in this research.

5.8. CALCULATION OF MEAN LIFETIME MAXIMUM PILE

LOAD

The discussions with operation and maintenance personnel proved to be very

valuable to guide the efforts for collection of appropriate survey data and enable the

selection of an appropriate probabilistic model.

Using the statistical parameters for equipment weights on the upper and lower decks

developed in Section 5.7, this section applies the influence surface method to

calculate the maximum live load on piles. The influence surface method was

described in Section 3.8.1.

The total axial live load supported by one pile due to a random variable is given by

the following formula (Ang and Tang, 1984):



∑ =

==

mi

i iiWCF1

Equation 5-1

Where F = Axial live loading on one pile due to the load on ¼ of the influence area

C = Influence coefficient

W = Random variable of equipment weights, assumed to be statistically independent with the same mean & standard deviation

m = Number of sectors

Discussions with maintenance and operation personnel revealed that loads applied

on platform decks during shutdown events were not necessarily similar to the

loading applied during another shutdown event. The discussions also revealed that

pieces of equipment on an open area were not necessarily related to the neighboring

equipment. Hence, pile axial load was assumed to vary (statistically) independently

between shutdown events. In such case, the mean value and standard deviation of

the maximum axial live load on one pile F are given by (Ang and Tang, 1984):

( ) ( )∑ =

==

mi

i ii WECFE1

Equation 5-2

Wmi

i iF C σσ ×= ∑ =

=12 Equation 5-3

Where E(W) = Mean of the equipment weight

σW = Standard deviation of the equipment weight

The mean and standard deviation of the axial live load on the pile are the load effect

statistics for one “realization” of a fully loaded deck. During the platform lifetime,

there will be a number of such realizations. Each realization represents one

shutdown event. Discussions with maintenance and operation personnel revealed

that it would be reasonably conservative to assume that other realizations, with

independent (different) weight combinations, will occur once every year during the

platform lifetime.

The interest is in the statistics of the lifetime maximum load effect. For this

purpose, the asymptotic distribution of the extreme values was used (Ang and Tang,

1984). To derive the lifetime maximum load effect, the distribution type for the

load effect was required. Ang and Tang (1984) described the derivation of the

lifetime maximum load effect using two distribution types, namely normal and

lognormal. Both distribution types were examined in this research.



Assuming that the Central Limit Theorem is valid, the axial live load loading on one

pile (F) will approach a normal distribution. The Central Limit Theorem indicates

that any variable that can be physically modeled as a summation of several effects is

likely to be described by the normal distribution. Since the total load on a structural

sector arises from the combined additive effect of individual personnel and pieces of

equipment, the normal distribution is a logical model. As the size of the sector

increases, the number of individual contributions usually increases and the normal

model can be expected to provide a better fit.

However, the number of sectors in a platform deck is likely to be small. Since only

a small number of random variables are in the computation of the axial load and the

contribution of the closest sectors would tend to dominate, the distribution would be

somewhere between a normal and lognormal distribution. In this research, both

normal distribution and lognormal distributions were used and a comparison of their

respective extreme values was made.

To allow for the weight of personnel working on the deck, ten persons on the

platform deck during shutdown was assumed. An average weight of 667 N per

person (Ellingwood and Culver, 1977) added about 30N/m2 to the OALL from

analysis of the sustained loads. Thus, the effect of personnel is relatively

insignificant. This assumption is also likely to be conservative for large open areas

offshore but the variance in normal personnel load contributes negligibly to the

variance in the unit load (Wen, 1979).

5.8.1. EXTREME AXIAL PILE LOAD USING NORMAL

DISTRIBUTION

It can be shown that the extreme values of F (termed Fn in this research) approaches

Type 1 asymptotic distribution (Ang and Tang, 1984). Thus, using Cramer’s

approximation to the Type 1 asymptotic solution, it was possible to compute the

statistical parameters of Fn for a number of realizations (n) as n approaches infinity.

For a normal variable F with mean value of μF and a standard deviation of σF, the

extreme value Fn has a characteristic largest value (mode) of μn and a dispersion

parameter αn given by Ang and Tang (1984) as follows:



FFn nnn μσπμ +⎟⎟

⎠

⎞⎜⎜⎝

⎛ +−=

ln224lnlnlnln2 Equation 5-4

Fn

nσ

α ln2= Equation 5-5

The mean and standard deviation of the extreme value Fn can then be calculated as

follows (Ang and Tang, 1984):

( )n

nn uFEαγ

+= Equation 5-6

nF α

πσ6

= Equation 5-7

Where γ = Euler number = 0.577216

By varying the values of n, the relationship between E(Fn) and σFn and the number

of shutdown events (n) could be established for an assumed number of sectors. The

relationship plot usually shows a unique second order polynomial converging to an

asymptotic value. The point at which this graph converges to an asymptotic value

can be regarded as the natural saturation point for that specific floor deck.

5.8.2. EXTREME AXIAL PILE LOAD USING LOGNORMAL

DISTRIBUTION

The lognormal distribution was also investigated because the normal model, which

is based on the Central Limit Theorem, may not be justified due to the relatively

small number of sectors in a typical platform deck and dominance of several

equipment weights on the load effect.

Having determined the values of μF and σF, the values of the lognormal distribution

parameters ζ and λ are determined from the following equation:

⎟⎟⎠

⎞⎜⎜⎝

⎛+= 2

22 1ln

μσζ Equation 5-8

where n = Sample size of the number of times (realizations) for which the deck is fully loaded

μF = Mean value of a normal variable F

σF = Standard deviation of a normal variable F



2

21ln ζμλ −= Equation 5-9

From the relationship between a normal variable and a lognormal variable, the

variable F’=ln(F) is normal and its extreme values will converge to Type 1

asymptotic distribution assuming a sample size of n:

λζπζ ++

−=n

nnun ln224lnlnlnln2' Equation 5-10

ζα n

nln2' = Equation 5-11

According to the above logarithmic transformation, Fn will converge to the Type 2

asymptotic distribution with the parameters (Ang and Tang, 1984):

Un e=ν Equation 5-12

'nk α= Equation 5-13

Where νn = Characteristic largest value of the initial variate F

k = Shape parameter with 1/ k being a measure of dispersion

Hence, the mean and standard deviation of Fn was computed using the following

equations:

( ) ⎟⎠⎞

⎜⎝⎛ −Γ=

kFE nn

11ν Equation 5-14

21

2 1121 ⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ −Γ−⎟

⎠⎞

⎜⎝⎛ −Γ=

kknF νσ Equation 5-15

Where Γ = Represents the gamma function whose values can be obtained from a standard table (Ang and Tang, 1984) or as shown below

The logarithm of the gamma function is sometimes treated as a special function to

avoid additional ‘branch-cut” structures that are introduced by the logarithm

function (http://mathworld.wolfram.com/LogGammaFunction.html).

The log-gamma function can be defined by:



( ) ∑∞

= ⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ +−+−−=Γ

11lnlnln

i kz

kzzzz γ Equation 5-16

Stirling series (in its normal form) also provides a solution, given by a simple

analytical expression:

( ) ( ) 53 12601

3601

121ln

212ln

21ln

zzzzzzz +−+−⎟

⎠⎞

⎜⎝⎛ −+=Γ π Equation 5-17

By varying the values of n, a plot of E(Fn) versus the sample size can be obtained

and the maximum lifetime live load on a pile can be established.

5.8.3. MINIMUM SEPARATION DISTANCE

In this research, the minimum distance between equipment pieces was of interest to

determine the required number of sectors on a platform deck, which is then used to

calculate influence coefficient. This enables the application of influence surface

method described in Section 3.8.1.

The minimum separation distance depends on the chemical and mechanical process

requirements. Processes differ from each other because of their inherent hazards.

During equipment layout stage at Front End Engineering Design (FEED), processes

and operations are usually grouped on a platform depending on their fire and

explosion hazard which can be classified into moderate, intermediate and high.

The moderate category includes processes, operations or materials having a limited

explosion hazard and a moderate fire hazard. This class generally involves

endothermic reactions and non-reactive operations such as distillation, absorption,

mixing and blending of flammable liquids. Exothermic reactions with no flammable

liquids or gasses also fit into this hazard group. Typical examples include acetic

anhydride and formaldehyde.

The intermediate category includes processes, operations or materials having an

appreciable explosion hazard and a moderate fire hazard. This class involves mildly

exothermic reactions such as alkylation in refinery and cyclohexane.

The high category includes processes, operations or materials having a high

explosion and moderate to heavy fire hazard. This class involves highly exothermic

or potential runaway reactions and high hazard products handling such as Ethylene

and Polyethelene.



This classification is used to determine minimum spacing requirements but

additional factors and judgments can still affect the class to which the process is

assigned, including pressure vessel size, flammable liquid holdup, gas versus liquid

phase, loss history, interdependency, and lead time to rebuild. For example, loss

history shows that fires or explosions in congested areas of oil and chemical plants

can result in extensive losses.

Wherever explosion or fire hazard exists, proper plant layout and adequate spacing

between hazards are essential to loss prevention and control. Layout relates to the

relative position of equipment or units within a given site. Spacing pertains to

minimum distances between units or equipment. Further, vapor cloud calculations

could indicate greater separation distance between some units is needed because of

higher than normal explosion damage potential and business interruptions.

The plant layout and spacing necessary to limit loss size is determined on the basis

of worst case scenarios for water cloud, vessel and building explosions and for fires.

The analysis involves the calculation of overpressure circles and is typically carried

out by loss prevention engineers.

A good layout and sufficient spacing between hazards, equipment and units will

have the following benefits:

• Less explosion damage: Overpressures created by an explosion decrease rapidly

as the distance from the center of the explosion increases,

• Less fire exposure: Radiation intensity from a fire decreases as the square of the

separation distance,

• Higher dilution of gas clouds or plumes: Gas concentration decreases as the

distance from the emission source increases,

• Easier access to equipment for maintenance, inspection and fire fighting

purposes, and

• Easier spill and spill fire control in open areas.

Loss prevention engineers typically establish a probable maximum loss (PML) and

maximum foreseeable loss (MFL) estimates based upon a vapor cloud explosion

where such a hazard exists. An adequate spacing between explosion hazard areas

will lower the PML and MFL.

On the other hand, extensive spacing increases the initial investment costs required



to build a given platform. The larger platform deck requires additional supporting

structural weight, larger foundations, more piping, cabling and larger drainage

systems. Additional or larger pumps or compressors may also be required as

friction loss increases with the piping length, which will in turn increases operating

costs.

Industrial Risk Insurers (1978) produced recommendations and guidelines for loss

prevention and protection. Those guidelines are based on experiences gained from

loss cases documented by the insurance underwriters. The guidelines cover onshore

and offshore plants that process oil and gas with an objective of keeping plants as

safe as possible from fire and explosion. For offshore facilities, the Industrial Risk

Insurers guidelines reproduced spacing charts for offshore facilities that require a

minimum distance of 3m.

Greater distances may be required where facilities are of very large size or contain

high pressures or toxic materials. Where spacing is significantly below the

minimum distances of 3m recommended by Industrial Risk Insurers, it is usually

necessary to compensate for the increased degree of risk. This can often be done by

providing more extensive safety features such as fire proofing, fixed water sprays,

additional fire fighting equipment and training, fire and/ or blast walls. However,

reduction below 3m is not usually permitted in industry practice.

In conclusion, a minimum separation distance between equipment of 3m was used

in this research, which reflects the recommendations of Industrial Risk Insurers

(1978) recommendation and industry practice.

5.9. APPLICATION

This section applies the procedure described in Section 5.7.3 to compute the mean

lifetime maximum load effect on a pile. For this purpose, an offshore platform with

a square plan dimension of 15m was considered to demonstrate the procedure and

sensitivity analyses were then performed on the parameters of this platform to derive

a generic solution for OALL. The plan of the platform is shown in Figure 5-9.

The number of sectors on a floor deck was established on the basis of two criteria.

First, the minimum distance between equipment was established at 3m as discussed

in Section 5.8.3. Hence, the size of each sector had to be 3m or more in plan.

Second, for equipment weighing less than or equal 10 tonnes, the average equipment



footprint was found to be approximately 10m2 as shown in Figure 5-3, which also

translates to an approximate minimum plan dimension of 3m.

Hence, the plan dimension of the platform was divided into sectors as shown in

Figure 5-9. The plan dimension of 15m*15m was divided into 16 sectors, which

satisfies both conditions of minimum separation distance and average equipment

footprint.

The influence surface concept described in Section 3.8.1 was applied to determine

the axial load on piles for a random load located anywhere on the deck. To derive

the mean lifetime maximum load effect on a pile, the procedure described in Section

5.7.3 was employed. The calculations were conducted for 50 shutdown events and

are presented in Table 5-2. The number of shutdown selected for their application is

based on one shutdown per year for a lifetime of 50 years.

By varying the number of shutdowns n (also termed realizations or occurrences in

this thesis), the mean lifetime maximum load effect on piles against the number of

shutdowns is plotted in Figure 5-10 and Figure 5-12 for the lower and upper decks,

respectively.

Inspection of Figure 5-10 reveals that, at large number of occurrences, E(Fn)

displays an asymptotic distribution. Similar conclusion is also drawn from Figure

5-12.

A plot of the relationship of the number of occurrence (or realizations) against the

standard deviation of Fn is shown in Figure 5-11 and Figure 5-13 for the upper and

lower decks, respectively. It is clear that the assumed distribution has a marked

effect on the computed standard deviation.

The calculated mean lifetime maximum live load effect on piles for each deck was

divided by the tributary area of the deck to provide OALL as shown in Table 5-3.

The derived OALL is equivalent to EUDL nominated in design codes and standards.

Conversely, using EUDL stipulated in design codes multiplied by the tributary area

produces the mean of the lifetime maximum live load on a pile.

Inspection of Table 5-3 reveals that the range of OALL used in industry practice is

subjective and is hence not representative of the various parameters affecting live

loads. For example, the current specifications of one operator in the Arabian Gulf

apply 2.5kPa to all floors when calculating live loads but an older specification of



the same operator applied 17kPa to calculate live load effects. An investigation of

the specifications of other operators in the Arabian Gulf revealed that the use of

5kPa for OALL is more common. The Author’s experience in South East Asia with

a major American contractor and review of calculations performed by major

Japanese contractors revealed that using 5kPa for all decks is not uncommon.

5.10. BENCHMARKING THE STATISTICS OF OALL

This section evaluates the results of this research with an objective of ensuring that

its findings are consistent with values used to develop API RP2A-LRFD (1993).

The computed coefficient of variation (10% - 20%) shown in Figure 5-11 and

Figure 5-13 for the maximum lifetime live load effect was compared to that used in

the development of API RP2A-LRFD (1993). Moses (1980) defined live loads on

offshore platforms as facility loads associated with the platform operation during

some portion of its lifetime. According to Moses (1980), live loads covered

applications such as floor loading, drill rig, drilling facilities and supplies, crane and

derrick hook loads and production facilities and supplies.

In deriving the statistical parameters for live loads, Moses (1980) considered that it

would seem reasonable to use no bias for live loads on the basis that live loads may

be overestimated or underestimated. Hence, this research adopted a bias factor of

1.0 for live loads. To derive COV for live loads, Moses (1980) argued that live load

placement and analysis would carry more uncertainty than dead loads, hence leading

to larger variability. Moses assumed 10% weight variability and a further 10%

analysis uncertainty leading to an overall value of about 14% for live load COV.

Hence, this research adopted bias factor of 1.0 and COV of 15% for OALL. The

use of no bias for live loads is consistent with the value used by Moses (1980) as

discussed above. The use of 15% for the COV is based on the results of this

research, which is also consistent with the value employed by Moses (1980) to

calibrate API RP2A.

The mean lifetime maximum live load effect developed in Section 5.9 was derived

from nominal arbitrary point-in-time statistics defined in Section 5.7. This section

investigates the relationship between the statistics of the lifetime maximum load

effect and the statistics of the arbitrary point in time values in view of available

knowledge in the field.



A review of the literature revealed no specific relationship between the arbitrary-

point-in-time and the nominal load data statistics. However, ANSI/AISC 360-05

stipulates that the “mean value of arbitrary-point-in-time live load is in the order of

0.24 to 0.40 times the mean maximum lifetime live load” but that “its dispersion is

far greater”.

The ratio (0.24 to 0.40) nominated in ANSI/AISC 360-05 was found to be similar to

the ratios calculated in this research. For the lower deck, Table 5-3 provides a value

of 4.4kPa for OALL. Hence, the mean maximum lifetime live load on a pile is

4.4kPa*15*15/4 = 247kN. The mean value of 36.1kN for the arbitrary point-in-time

live loads was defined in Section 5.7. Hence, the ratio (0.15) computed in this

research for lower decks is similar to the lower bound of the ratio (0.24) stipulated

in ANSI/AISC 360-05.

The dispersion of the arbitrary point-in-time on lower and upper decks was defined

in Section 5.7 to be 0.51 and 0.38, respectively. These dispersion values are greater

than the ratio (0.15) between the mean lifetime maximum load effect and mean

value of arbitrary point-in-time.

Hence, the ratio of the statistics of the arbitrary point-in-time live load and the

maximum lifetime live load computed in this research is consistent with values

defined in ANSI/ AISC 360-05.

5.11. SENSITIVITY ANALYSIS OF OALL PARAMETERS

Section 5.9 presented an application of the methodology using the statistical

parameters developed in this research. The application considered one platform

deck size, a specific SWL and a specific number of sectors. The application resulted

in a nominated value for OALL. In order to derive generic values for OALL, there

was a need to investigate the effect of considering other deck sizes and study the

effect of varying the SWL and the number of sectors.

5.11.1. VARYING THE DECK AREA

The application described in Section 5.9 considered a floor plan of 15m*15m to

derive OALL values. This section identifies OALL values for other deck sizes

ranging from 100m2 to 1000m2. For every assumed floor area, the number of

sectors was determined based on the minimum separation distance of 3m defined in



Section 5.8.3.

The relationship between the floor size and OALL is shown in Figure 5-14. The

analysis shows that OALL reduces with increasing floor areas. However, the

reduction is not significant. For example, an increase in the floor area from 200m2

to 400m2 (100% increase) results in 11% reduction in OALL.

5.11.2. MINIMUM SEPARATION DISTANCE

Varying the minimum separation distance between equipment changes the number

of sectors, which results in a change in the computed influence coefficients as

shown in Table 5-4. The calculations were repeated using a number of realizations

(n) of 50.

The sensitivity of changing the number of sectors on the computed OALL for lower

floors is depicted in Figure 5-15. Inspection of the trend indicates that, for a floor

area of 15m*15m, increasing the separation distance results in a smaller number of

sectors, leading to a reduction in the calculated OALL.

This analysis implicitly assumed that the deck is fully occupied with OALL.

However, it is unlikely that a platform deck is fully occupied with live load only due

to the presence of fixed equipment on the deck. The fixed equipment can be

considered as dead loads, so open areas only cover the spaces between fixed

equipment. The effect of fixed equipment can be modeled by eliminating some

sectors when calculating influence coefficients. For example, sectors 5 and 8 in

Figure 5-9 could be occupied by fixed equipment so the calculations for influence

coefficient would discount those sectors in the calculations.

In this research, the effect of fixed equipment on the computed influence

coefficients was not considered. This is a conservative assumption leading to an

upper bound to the computed OALL.

Inspection of the results showed that the computed OALL is sensitive to the

separation distance between equipment and to small (less than 3m) separation

distance in particular. However, as previously discussed in 5.8.3, fire and safety

considerations and regulations preclude the use of separation distances less than 3m.

For separation distances of 3m or larger, the computed OALL is not as sensitive to

changes in the minimum separation distances. The relationship between the



separation distance and OALL was established using regression analysis as follows:

OALL (kPa) = 42.7*(separation distance in m)-1.8 Equation 5-18

5.11.3. VARYING THE CRANE CAPACITY

The statistical parameters defined in Section 5.7 are based on the use of 10 tonne

SWL for cranes on the upper deck and 5 tonne SWL for monorails on lower decks.

This section investigates the effect of changing the assumed crane capacity on the

computed OALL. The analysis was conducted for lower levels but is equally

applicable to upper deck.

Figure 5-16 depicts the sensitive relationship between monorails capacities and

OALL. Doubling the monorail SWL resulted in doubling the computed OALL.

Hence, analysis of OALL on any platform needs to include the monorail capacities

on that platform.

OALL (kPa) = 0.08 * Crane SWL (kN) + 0.28 Equation 5-19

5.12. LIVE LOAD FACTORS

Section 5.7.3 identified a procedure to calculate the magnitude of the OALL that can

be used with the strength check described in API RP2A-LRFD (1993). This section

quantifies a corresponding live load factor that can be used with the derived OALL

in the strength check equations.

The derivation of live load factor was performed in light of approaches previously

used to define live load factors in API RP2A-LRFD (1993) and ANSI/AISC 360-05.

However, a review of the literature describing the methodology used to derive live

load factors in API RP2A-LRFD (1993) identified a number of inconsistencies and

pointed to subjectivity in the selection of live load factors in API RP2A-LRFD

(1993). These inconsistencies are described below and a conclusion is drawn to

recommend live load factors for this research.

API RP2A-LRFD (1993) recommends a load factor of 1.5 to the computed action

effect on the structure resulting from the short duration live loads exerted on the

structure. The live load factor of 1.5 is based on a coefficient of variation equal to

14% (Moses, 1980). However, API RP2A-LRFD (1993) Commentary proposed a

simplified method to calculate the load factor in lieu of a detailed reliability analysis



if the statistical parameters are different to those used in deriving load factors in API

RP2A-LRFD (1993).

The simplified procedure is applicable to loads acting alone and requires an estimate

of the coefficient of variation. It employs Table 5-5 to determine a load factorγ .

The load factor is then multiplied by the bias to provide the load factor which is

applicable to the specified coefficient of variation. The bias is defined as the ratio of

the mean value to the nominal or design value.

API RP2A-LRFD (1993) demonstrated the method using the environmental

statistical data for environmental data (mean = 0.7 and coefficient of variation =

0.37) and established a load factor (1.393) which was considered to be close enough

to the environmental load factor (1.35) defined in Section C.3.1.1 of the API RP2A-

LRFD (1993).

On applying the above procedure to the live load statistical parameters (bias = 1 and

coefficient of variation = 0.14) reported by Moses (1980) and used to calibrate API

RP2A-LRFD (1993), the resulting live load factor was 1.306. However, such live

load factor does not match live load factors (1.5) nominated in API RP2A-LRFD

(1993). Therefore, this research concluded that the simplified procedure described

in the commentary of API RP2A-LRFD (1993) is possibly applicable to

environmental loads only.

A comparison of live load factors defined in API RP2A-LRFD (1993) and

ANSI/ASCE 360-05 revealed another inconsistency in the application of the load

factors in reassessment of the various components of existing offshore platforms.

As discussed in Section 2.3.2, API RP2A-LRFD (1993) addresses tubular sections

only and refers to ANSI/ASCE 360-05 for non-tubular members. However, API

RP2A-LRFD (1993) recommends the use of 1.5 for live load factor whereas

ANSI/ASCE 360-05 recommends the use of 1.6. API RP2A-LRFD (1993)

acknowledged the inconsistency but justified this inconsistency using a simple dead

plus live load combination to conclude that the error introduced by such decision is

small (less than 3.4%).

Further, a single value for live load factor (1.6) was nominated in ANSI/ ASCE 360-

05 despite the wide range of coefficient of variation (23% to 100%) shown in Table

5-6 for the arbitrary point-in-time live loads. However, the discussion in Section



5.10 identified a relationship between the statistics of the arbitrary point-in-time

loads and those of the maximum lifetime load effects. Further, Section C.1-1 of the

API RP2A-LRFD (1993) Commentary provides a relationship between the

coefficient of variation of live load effects and live load factor. Hence, a

relationship exists between the coefficient of variation of the arbitrary point-in-time

loads and live load factors. Such a relationship would result in a set of live load

factors that correspond to the various coefficients of variation identified in Table

5-6. However, as discussed above, ANSI/ AISC 360-05 nominates a single live load

factor. Consequently, the development of live load factors in API RP2A-LRFD

(1993) and ANSI/AISC 360-05 appears to disregard the variation in the statistical

parameters of the arbitrary point-in-time live loads.

Computation of live loads in other research projects without attending to changing

the load factors is not uncommon. For example, a similar approach was adopted in

the bridge live load research. Miao and Chan (2002) obtained extreme daily

moments and shears using 10 year weigh-in-motion (WIM) data as compared to the

traditional normal probability paper approach used in bridge codes and standards

such as AASHTO. In their study, Miao and Chan (2002) analyzed the WIM data to

understand the real traffic status for comparison with bridge design live load models

in use in Hong Kong and did not consider load factors while investigating the design

load.

Hence, there is justification to maintain a live load factor of 1.5 for offshore

platforms in the Arabian Gulf, especially given the similar coefficient of variation

(10-20%) derived in this research as described in Section 5.8.3 compared to that

(14%) used in the calibration of API RP2A-LRFD (1993).

5.13. SUMMARY

In the absence of specific guidance for open area live loads in international codes

and standards, industry practice uses subjective value for open area live loads when

performing reassessment of existing offshore structures. The use of an arbitrary and

subjective value for the live loads in geographic regions, such as the Gulf of Mexico

and the North Sea, may not to be critical for the outcome because extreme storm

conditions dominate the failure mechanism in the those geographical locations.

The Author’s industry experience pointed to the dominant effect of OALL on the



results of deterministic reassessment of offshore platforms in the Arabian Gulf. API

RP2A-LRFD (1993) in general and Section ‘R’ in particular do not cover OALL.

For a complete set of specifications, it was important to rationally determine OALL

values that can be used in reassessment of existing platforms in the Arabian Gulf.

This Chapter presented a rational procedure to establish the mean of lifetime

maximum load effect on piles, which can be used to derive OALL. Application of

the procedure required a set of statistical parameters for equipment weights, which

would be equivalent to the arbitrary point-in-time of furniture used in deriving live

loads in ANSI A58. The statistical parameters of equipment weights on upper and

lower decks are identified in Table 5-1.

The derived OALL was compared to industry practice from the Author’s experience

working for contractors and operators around the world. It was found that the

calculated OALL falls within the range used in industry practice, but that industry

practice values for OALL is too broad and is likely to result in either unsafe or

uneconomical reassessment outcome.

Further, OALL values used in industry practice do not consider that OALL is

function of a number of parameters and is therefore not a unique number as the case

with live loads on building structures. Some of the parameters affecting the

magnitude of OALL include the location of the load on the platform (lower decks or

upper deck), deck area, SWL of material handling equipment on the floor and the

separation distance between equipment.



Table 5-1: Statistical parameters of the arbitrary point-in-time equipment weights on upper and lower decks for given crane or monorail SWL. These values were derived using the equipment weight database shown in Appendix A and collated in this research

Crane SWL (tonne)

Mean (kN) Standard Deviation

COV (%)

Lower 5 36.1 18.4 51

Upper 10 81.5 30.6 38



Table 5-2: Derivation of the maximum lifetime live load on a pile for an assumed floor deck of 15m*15m in plan and using a number of realizations (n) = 50 shutdown events

Parameters Units Upper Deck Lower Decks

Mean (Section 5.7) 81.5 36.1

Standard Deviation (Section 5.7) 30.6 18.4

Number of Sectors 16 16

Σ Influence coefficients 4.00 4.00

Σ square of Influence coefficient. 2.21 2.21

E(F) 326 144

Fσ 45.5 27.3

n 50 50

Assuming normal variables

nu kN 442 214

nα kN-1 0.0615 0.102

Euler number 0.577216 0.577216

E(Fn) kN 451 220

σFn kN 21 13

COV 4.6% 5.7%

Assuming lognormal variables 2ζ 0.139 0.188

λ 5.78 4.95

υ’n 6.07 5.35

α’n 20.164 14.911

nv 432.1 210.4

k 20.16 14.91

E(Fn) kN 446 220

σFn kN 28.7 19.7

COV 6.4% 9%



Table 5-3: Statistical parameters and magnitudes of OALL. The statistics are based on normal distribution for the mean lifetime maximum load effect. The statistical parameters are compared to the range of OALL used in industry practice. The range used in the industry is based on the Author’s experience while working in various parts of the world

OALL (kPa) Deck Reference Parameter This

Research Industry Practice

Figure 5-10 E(Fn) 250 4.4 2.5 to 17 Lower deck Figure 5-11 σ (Fn) 9% 9% 14%2

Figure 5-12 E(Fn) 500 8.9 2.5 to 17 Upper decks Figure 5-13 σ (Fn) 15% 15% 14%

2 Implicit in API RP2A-LRFD (1993) and is based on the value used by Moses (1980) to calibrate API RP2A-LRFD (1993)



Table 5-4: Sensitivity analysis showing the effect of changing minimum distance between equipment on OALL

# Sectors used in the analysis 4 9 16 25 36

Distance between equipment 15/2 15/3 15/4 15/5 15/6

Σci 0.66 2.25 4.00 6.25 9.00

Σci2 0.20 1.24 2.21 3.45 4.97

E(Fn) 55 158 245 351 474

OALL (kPa) 0.98 2.81 4.50 6.24 8.43



Table 5-5: Recommended load factors for load statistics not covered in API RP2A specifications (reference: API RP2A-LRFD Table Comm. C.1-1, 1993)

COVQ % 5 10 15 20 25 30 40

γ 1.20 1.25 1.32 1.43 1.60 1.75 2.10

Where: QCOV = Coefficient of variation of the load effect

γ = Load factor assuming mean load is used



Table 5-6: Instantaneous sustained load parameters for occupational groups for various occupancies and usage and the corresponding design live load value from ASCE Standard 7-05 (Reference: Chalk and Corotis, 1980)

Arbitrary Point-in-Time

Live Load Mean

Standard Deviation for 200ft2

COV Occupancy or use

kPa kPa kPa %

Hospitals: Operating rooms 2.9 0.68 0.39 57

Hospitals: Private rooms 1.9 0.35 0.31 89

Libraries 7.2 1.66 0.52 31

Stores: Retail first floor 4.8 0.86 0.24 28

Stores: Retail upper floors 3.6 0.57 0.46 81

Manufacturing: Light 6.0 0.91 0.91 100

Manufacturing: Heavy 12.0 2.88 1.63 57

Office buildings: Lobbies 4.8 0.52 0.28 54

Office buildings: Offices 2.4 0.22 0.16 72

Hotels: Private rooms and corridors

1.9 0.22 0.06 27

School Classrooms 1.9 0.57 0.13 23

Storage warehouses: Heavy 12.0 3.42 2.78 81



Figure 5-1: An example of an unloaded open area during normal operations of an offshore platform and at times other than shutdown. The photo shows that there is usually minor loads and personnel on open areas of offshore platforms



Figure 5-2: An example of a skid frame supporting a compressor skid unit which includes a compressor driven by a turbine. In this example, a motor could be scheduled for maintenance during a shutdown, which would require various components of the skid to be disassembled on the open area of the platform



Footprint = 0.0501 * Equipment Weight + 7.706

0

30

60

90

120

150

0 300 600 900 1200 1500 1800 2100 2400

Equipment Weight (kN)

Foot

prin

t (m

2 )

Figure 5-3: Scatter plot showing equipment weight and their corresponding footprint for every piece of equipment in the database that was used in this research. The scatter plot shows that the majority of the surveyed equipment weighs less than 20 tonnes (200kN) with an approximate linear relationship between an equipment weight and its footprint



Figure 5-4: Schematic of a platform elevation showing that equipment with larger size (and weight) tends to be located on the upper decks to facilitate removal and maintenance. This is usually preferred by operation and maintenance personnel to facilitate and optimize operations and maintenance costs



0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30 35 40 45 50 55


Figure 5-5: This chart shows histogram of the truncated equipment weights on lower decks. The statistical parameters were calculated as mean = 36.1kN and standard deviation = 18.4kN



p ( )

Val

ues

x 10

^-3

Values in Thousands

0

1

2

3

4

5

6

7

8

-0.5 0.0

0.5

1.0

1.5

2.0

2.5


Figure 5-6: Histogram of the equipment weight on lower decks and an assumed fitted Exponential distribution. Using the assumed distribution, the statistical parameters were calculated as mean = 135.4kN and standard deviation = 134.8kN



0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.350 10 20 30 40 50 60 70 80 90 100

110


Figure 5-7: This chart shows a histogram and fitted distribution of the truncated database for equipment weights on the upper deck. The fitted distribution is lognormal with mean = 81.5kN and a standard deviation = 30.6kN



p ( )

Val

ues

x 10

^-3

Values in Thousands

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

-0.5 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


Figure 5-8: This chart shows histogram of the equipment weight on the upper deck fitted an Exponential distribution with mean = 627kN and standard deviation = 638kN



Figure 5-9: Plan view of the model platform decks used to demonstrate the application of the influence surface concept for a 15m square floor deck



Figure 5-10: The relationship between the maximum lifetime live load on a pile and the number of shutdown events for lower decks. The plot shows an asymptotic relationship with a maximum lifetime live load on the pile of 275kN for a lognormal distribution and 250kN for a normal distribution

200

225

250

275

300

50 400 800 1200 1600 2000 2400

Number of Occurences

Mea

n Li

fetim

e M

ax L

LE(

F n) (

kN)

Normal Distribution

Lognormal Distribution



Figure 5-11: Relationship between number of shutdown and the standard deviation of the mean lifetime live load for lower decks showing that the standard deviation ranges between 10%-20%. An average of 15% is used in this research

0

5

10

15

20

25

50 400 800 1200 1600 2000 2400


Std

Dev

of M

ean

Life

time

Max

LL

σ E

(Fn)

(kN

)

Normal Distribution




Figure 5-12: The relationship between the maximum lifetime live load on a pile and the number of shutdown events for the upper deck shows an asymptotic relationship

400

425

450

475

500

525

550

50 400 800 1200 1600 2000 2400


Mea

n Li

fetim

e M

ax L

LE(

Fn) (

kN)

Normal Distribution




Figure 5-13: The relationship between the number of shutdowns and the standard deviation of the mean lifetime live load effect for the upper deck showing that the standard deviation ranges between 15%-22% and depends on the distribution type and the number of occurrences

0

5

10

15

20

25

30

50 400 800 1200 1600 2000 2400


Stan

dard

Dev

iatio

n of

Mea

n Li

fetim

e M

ax L

Lσ

E(F

n) (k

N)

Normal Distribution




OALL= -0.0029 A + 5.7159

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0 100 200 300 400 500 600 700 800 900

Floor Area (m2)

OA

LL (k

Pa)

Figure 5-14: Sensitivity analysis results showing the effect of varying the deck area on the number of sectors for lower decks. The analysis revealed that OALL is not sensitive to varying deck areas. Doubling the floor area from 200m2 to 400m2 results in 11% reduction in OALL



OALL (kPa) = 42.7 * Separation Distance in m-1.8

0.0

4.0

8.0

12.0

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Minimum Separation Distance (m)

OA

LL (k

Pa)

Figure 5-15: Relationship between minimum separation distance and OALL for n = 50 on lower floors. The chart shows that the calculated OALL is sensitive to the selected minimum distance when the minimum distance is 3m or less as discussed in Section 5.8.3.



OALL = 0.08 * Crane Capacity (kN) + 0.28

1.0

2.0

3.0

4.0

5.0

6.0

7.0

20 25 30 35 40 45 50 55 60 65 70 75

Crane or Monorail Safe Working Load (kN)

OA

LL (k

Pa)

Figure 5-16: Relationship between crane capacity and OALL on lower decks showing that OALL is sensitive to the SWL of the crane used on the deck


Chapter 6.

DOMINANT FAILURE MECHANISM

6.1. OBJECTIVE

Calibration of axial pile resistance factors and open area live loads presented in

Chapters 4 and 5 covered operating overload conditions only. To address the

problem statement identified in Section 1.2, there was a need to examine the effect

of extreme storm conditions on the reliability of existing platforms in the Arabian

Gulf.

This Chapter presents the results of a reliability analysis on a platform to evaluate

the dominant failure mechanism in the Arabian Gulf. The platform was selected to

identify the need for considering extreme storm conditions in conjunction with

operating overload conditions for the required reassessment specifications.

Including extreme storm conditions in the specifications would only be required if

the probability of failure under extreme storm conditions is of the same order of

magnitude or higher than the corresponding probability of failure under operating

overload conditions as illustrated in Figure 6-1.

On the other hand, if the probability of failure in the extreme storm condition is

much higher than the probability of failure in the operating overload, extreme storm

conditions would dominate the failure mode. In such case, parameters would only

need to be calibrated for the extreme storm conditions.

If the reliability analysis results show that the probability of failure under extreme

storm conditions is close to the probability of failure under operating overload, the

dominant failure mechanism would result from the interaction of both conditions

and parameters would be needed for both conditions.

CHAPTER 6: DOMINANT FAILURE MECHANISM 249


6.2. PERFORMANCE MODEL

The loading on an offshore structure may be resolved into a horizontal load, PH, and

a vertical load, PV. The horizontal load results from a combination of wave, current

and wind whereas the vertical loads include gravity (dead and live) loads. A

simplified schematic of the loads and moments applied to the pile system is shown

in Figure 6-2.

For a given direction of loading, the horizontal loading, PH, is applied to the

structure at distance above the sea floor to develop an overturning moment, which is

resolved to shear and axial forces in the foundation system. The vertical loading PV

develops mainly axial loads on the pile system. In real life situations, a dominant PH

represents an extreme storm condition and PV effect is relatively small, while a

dominant PV represents an operating overload condition in which the PH effect is

relatively small.

6.3. APPROACH

Assessment of the probability of failure under extreme storm conditions was carried

out using results of a pushover analysis. First Order Reliability Method (FORM)

was then employed using the results of the pushover analysis to estimate the

probability of failure of the piled foundation system.

The statistical parameters for the resistance (axial pile capacities) and for the loading

(open area live loads) derived in this research were employed in the reliability

analysis to determine the probability of failure under operating overload conditions.

To estimate the probability of failure under operating overload, a consistent

approach to that used for the extreme storm condition was required. To achieve

such consistency, a novel approach was employed in this research and is based on

incrementing the vertical loads.

Due to its novelty, the outcome of applying FORM to the results of the pushover

analysis in the vertical direction was verified by comparing its results with another

established method to validate the computed probability of failure.

6.4. LOGIC OF PLATFORM SELECTION

Reliability analysis was conducted on a selected platform in the Arabian Gulf. The



platform was selected such that the outcome of the reliability analysis could be

considered as generic and applicable to other platforms in the Arabian Gulf.

A survey of available platform data identified a four-legged unmanned wellhead

jacket, which was considered to meet the objective of this research. The selected

platform was designed in 1972 according to API RP2A-WSD. It is located in 100m

water depth with leg spacing of 15m in plan between the main gridlines. The

platform is supported by one pile at each corner of the jacket base, which coincides

with the main gridlines. The jacket was fabricated using mild steel to support nine

(9) conductors. The topside supports flow meters, low pressure (LP) and high

pressure (HP) manifolds, piping and control panels, pig trap, instrument air system,

risers, emergency shutdown (ESD) valves and fire and gas detection systems. These

facilities are typical for a wellhead platform in the Arabian Gulf and are shown in

Figure 6-3.

The choice of a four-legged platform served the purpose of this research. Since the

objective was to compare failure mechanisms, it was reasonable to use a

configuration that was likely to provide an upper bound to the failure probability for

extreme storm conditions, such that if extreme storm conditions were found not to

dominate the failure mechanism, similar conclusions could be drawn for six or eight

legged offshore platforms.

Under extreme storm conditions, the probability of failure associated with the use of

six or eight-legged platform structures would be lower due to the inherent reserve

strength and redundancy in the system. Tang and Gilbert (1993) studied offshore

pile system reliability considering a four-legged platform and an eight-legged

platform and found that the latter provided more than twice the redundancy of the

former when similar failure mechanisms were compared. Hence, the use of a four-

legged platform would neutralize the high reserve strength effect on the system, and

was therefore adopted as an appropriate choice for the purpose of this research.

The selection of a platform in 100m water depth also served the objective of this

research. This water depth is the deepest in the Arabian Gulf as shown in Figure

2-3. Since the objective was to confirm the premise that the operating overload

condition dominated the failure mechanism, the use of the deepest water maximized

the hydrodynamic loads on the platform, resulting in an upper bound probability of

failure under extreme storm conditions.



The choice of a wellhead platform rather than a compression platform or a

processing platform was also based on the objective of this research. Wellhead

platforms in the Arabian Gulf are generally associated with larger open areas on

topside decks, which are usually required for drilling reasons as shown in Figure

6-4. Large open areas result in an upper bound for the probability of failure under

operating overload conditions.

The above discussion is illustrated in Figure 6-5. The graph maps out the expected

probability of failure under operating overload and extreme storm conditions. The

probability of failure for the selected platform is positioned on the map.

Therefore, the reliability analysis of the selected platform would produce an upper

bound probability of failure for the operating overload condition and extreme storm

conditions. Consequently, the conclusion from this research would be applicable to

other platforms in the Arabian Gulf which would be either in shallower water or

with smaller open deck areas.

6.5. MATHEMATICAL MODEL

The nonlinear computer model used for the static pushover analysis of the 4-legged

steel jacket platform is shown in Figure 6-7. The model consists of a fully coupled

nonlinear jacket foundation system. Figure 6-7 also depicts the force-deformation

relationship used to model each of the primary members. The piles were modeled

using the Pile Soil Interaction (PSI) module in SACS. Nonlinear p-y (lateral), t-z

(axial skin friction) and q-z (axial end bearing) springs were attached to pile nodes

to model pile/ soil behavior.

Some of the important items to determine the estimates of platform capacity,

together with various factors that influence shear strength and estimates of lateral

and axial soil capacities are identified in this section.

6.5.1. STRUCTURAL MODELING

The structure was modeled with one finite element per physical member and

employed DNV guidelines (1999). The mathematical model is shown in Figure 6-6.

The geometry of the space frame was modeled in three dimensions. The nodes of

the model of the framework were selected as the intersection points of the



centerlines of the legs, diagonal members and plan braces. The offsets between the

intersection points of brace member/ chord centerlines at the joints which are less

than ¼ of the CAN diameter and the corresponding member end eccentricities were

not included in the structural model. This approach is in line with API RP2A-LRFD

(1993) recommendations.

The ultimate strength model included the primary framework in the vertical and

battered frames of the structure and the secondary framework that provides stiffness

to the plan framing and lateral support to the conductors and other appurtenances.

Often, the secondary framework, such as plan bracing, becomes part of the load

mechanisms as primary members yield and shed load.

The model of the non-linear characteristics of primary members reflected the plastic

performance at the ultimate load limit. An inelastic “beam-column” element was

used to represent the legs, piles and conductors while “strut” elements modeled the

horizontal and diagonal braces.

The stiffness and strength of appurtenances such as launch cradles, mudmats, J-

tubes, risers and skirt pile guides were not included. These elements do not to

contribute significantly to the overall global stiffness and structural strength.

The tension and compression failure of a member was described using a beam

element that includes a lumped plasticity formulation in COLLAPSE. An initial

out-of-straightness was included in the element properties so that the solution

accurately predicts the ultimate compressive strength of the element. An out-of-

straightness of 0.15% of the member length was assumed in modeling the members

of the space frame.

The rigid cross-section of the tubes implies that the cross section remains unchanged

during axial compression. However, at large compressive deformations, significant

local buckling or distortion of the cross-section may occur in the tubular walls. This

local buckling can reduce the maximum load carrying capacity and the post

buckling behavior. The following regions were implemented for the various

slenderness ratios (D/t):

• For D/t < 35, a tubular member will develop the full plastic bending capacity up

to high axial shortening deformations,

• For 35 < D/t < 60, industry experience showed that local buckling appears to



reduce the member’s compressive strength but with an increase in the load

shedding in the post buckling range. However, this usually occurs at such large

axial shortening that the collapse strength of the frame has already been

achieved and only the framework’s post collapse behavior is affected. Thus, a

rigid-cross section for describing buckling behavior was assumed, and

• D/t > 60, local buckling may occur prior to global member buckling, which

limits the member’s compressive strength. Initial local shell buckling also

results in load shedding in the post buckling range more rapidly than when the

cross-section is assumed rigid. In the case of a structure in which the primary

braces involved in the failure modes with D/t > 60, more detailed evaluation

would be required using existing empirical equation (Van Langen 1995) or

detailed non-linear FE analysis. This is not the case in the selected platform or

generally for real offshore platform structures as it is the industry norm to select

low value of D/t.

The ultimate capacity of simple unstiffened and undamaged tubular joints was

established using API RP2A-LRFD (1993) formulae with all resistance factors set to

unity.

The calculations showed that ultimate axial load and ultimate moment capacities

provided by all joints exceeded the axial yield load and plastic moment capacity of

the brace cross-section. As the brace failed prior to the joint failure, there was no

need to include the joints in the failure mechanism. This enabled a more optimum

computer model size because inclusion of the joint in the COLLAPSE model

introduces additional degrees of freedom.

Joint flexibility was not modeled as it does not significantly affect the primary axial

loading in a framed structure. As previously discussed, joints were found to be

stronger than the incoming bracing members. Hence, member failures occurred in

tension or compression.

To ensure that a member’s tensile capacity is effectively mobilized, the model for

member tensile failure included a ductility criterion that identified disconnection in

the member ends after cracking and opening of joint welds. In lieu of a more

advanced analysis, a fracture criterion that limits the tensile strain in the member to

3% was applied. When such limit is exceeded in a member, the member is

disconnected and the analysis is repeated. This did not occur in the analysis and



strains were below the limit of 3%.

The yield strength of the structure was based on survey of literature and industry

experience. A number of authors used the results of routine mill tests as a basis for

research of the variability in yield strength. Usually the results of mill tests carried

out on a large number of specimens show a skewed distribution since material

below the specified strength is generally detected in routine control tests and not

included in the sample. Julian (1957) used test results from 3124 specimens of low

carbon steel (ASTM A7) and reported mean of 20% above the nominal (minimum

specified) value of the yield strength and a coefficient of variation of 7.8%. Tall and

Alpsten (1969), Galambos and Ravindra (1978) and Baker (1972) reported similar

values.

Another aspect was also considered in the evaluation of member strength in this

research. The tension specimen test is normally applied as a routine acceptance test

for structural steels in most countries. It is performed at a specified loading or strain

rate. Usually, the upper yield point is recorded because reported yield point under

relatively high rate of loading may be considerably above the static yield level. In

laboratory tests, the static yields level is measured by including 2 minute stops in the

loading procedure after measuring the upper yield point. This enables the steel to

“relax” to the static yield level. Galambos and Ravindra (1978) reported that the

average static yield strength of steel is 10% lower than the upper yield point which

is recorded during the mill test. Even a “very slow” laboratory strain rate can raise

the apparent yield strength level by as much as 5%. The actual strain rate or loading

rate experienced during failure under extreme environmental loading may be

accounted for by increasing the apparent yield stress level. Galambos and Ravindra

(1978) and Tall and Alpsten (1969) suggested an increase of 10% for 250MPa steel

and 5% for 350MPa steel.

In this research, the strength of fabricated tubular was based on an increase of 10%

over the yield stress to account for the excess (+20%) of the mean value of yield

strength over the specified strength and a reduction to account for strain rate effects

during mill tests which increases the measured stress by 10%. An additional 10%

for 250MPa steel (5% for 350MPa steel) was applied to account for strain rate

effects during extreme environmental loading when members are failing. Hence, the

assumed yield strength was taken as 300MPa for this mathematical model.



The increase in the assumed yield strength was applied to the material strength used

in the COLLAPSE model rather than carrying out any post corrections. The results

of the COLLAPSE analysis then represent the mean ultimate system strength Rmean.

This strength was directly used in the reliability calculations together with an

estimate of the variability in system strength.

The strain hardening property of steel is extremely important in spreading

“plasticity” along a yielding member or in the plastic hinge region and thereby

ensuring ductile response. However, the increased material strength due to strain

hardening is usually not significant in space frame structure. For compression

members strain hardening will only occur in the post-collapse range and reduce the

load shedding. For tensile members the yielding required for strain hardening to

become significant (above 3%) does not usually occur. For joints the strain

hardening, when relevant, is already included in the ultimate capacity criteria.

Influences of residual stresses and dimensional tolerances were identified in this

research and evaluated for consideration in the mathematical model. The models for

component strength, such as member compressive strength and joint strength are

semi-empirical. That is, they have a theoretical basis but are formulated to conform

to experimental data. When the components are assembled to form the framework,

the effects of fabrication stresses, misalignment and other irregularities were not

included due to their relatively minor effect on the collapse strength. An important

corollary of plasticity theory, developed in connection with portal frames, is that the

initial state of stress has no effect on the collapse load provided that global or local

instability is not governing. This is due to the fact that the initial state of stress is

‘by definition’ in self-equilibrium and hence no net work is done by these forces

when the structure deforms. This also applies in a generalized sense to the

evaluation of the ductile collapse strength of space structures. In space frames,

member slenderness is usually such that buckling is elasto-plastic and hence less

sensitive to the initial state of stress.

6.5.2. FOUNDATION MODELING

Survey of the literature identified that the majority of authors ignored soil pile

interaction when performing pushover analysis (Vughts and Edwards, 1992). For

example, Boon et al. (1993) did not consider foundation failure in their investigation



on the basis that foundation bias is very high. Such an approach is consistent with

common practice in the North-Sea and the Gulf of Mexico.

In this research, ignoring the foundation model could not be justified for both

vertical and lateral directions due to lack of published research relevant to the

Arabian Gulf conditions. At the outset, it was not possible to establish a relative

load level that would cause failure in foundations. Hence, it was important to model

the foundation.

Foundation failure mechanism was captured using capacity models for lateral

failure, punch through and pull out. The foundation models included in the analysis

were based on definition of soil shear strength and modeling of soil pile interaction

in the horizontal and lateral directions.

Inspection of the database collated in this research revealed increases in the soil

shear strength of cohesive soils with depth. However, within the same layer, the

interpreted soil shear strength can vary depending on the consultant and on the

testing method. Figure 6-8 shows shear strength for one borehole. The lines

represent different interpretations by different consultants and at different times. At

intermediate soil layer between 24m to 56m below mudline, the shear strength

values ranged between 100 and 250kPa. In this research, the mathematical model

was based on the mean value of the shear strength using an unconfined undrained

(UU) test method in every layer.

Modeling soil elements in the lateral direction was based on the lateral failure

mechanism as a portal frame failure below mudline, with plastic hinges developing

below the jacket at mudline and at the counter curvature point in the piles. Most of

the energy dissipation will be associated with the plastic hinges in the pile steel and

the flexibilities and/ or local stiffness variations in the frame work should not

influence the pile lateral failure mechanism. Thus, modeling lateral foundations

collapse concentrated on the description of portal frame action below mudline and

on an accurate modeling of the ultimate capacity of the p-y curves to determine the

counter curvature point.

API RP2A-LRFD (1993) p-y curves were used to model the soil elements in the

lateral direction. In the derivation of the API RP2A-LRFD p-y curves, Tang, (1990)

did not exclude carbonate soils when deriving the lateral pile capacity. Hence, the

use of API RP2A-LRFD (1993) was considered appropriate for use in this research.



The API RP2A-LRFD (1993) method was used in this research and p-y curve for

every soil layer is presented in Figure 6-9. The springs represent the soil resistance,

and are characterized by nonlinear lateral, axial load transfer and tip load

displacement curves.

The axial resistance model of the soil in API RP2A-LRFD (1993) is provided by a

combination of axial soil-pile adhesion, or load transfer along the side of the pile,

and end bearing resistance at the pile tip. The foundation compression and tension

capacity will usually be significantly less than that of the pile steel cross section

when API RP2A-LRFD (1993) methodology is used. This is the case for non-

carbonate soils due to the bias in the API RP2A formulation described above and as

evident by actual post-mortem analysis (Bea, 1983).

The relationship between mobilized soil-pile shear transfer and local pile deflection

at any depth is described using t-z curve, while the relationship between mobilized

end bearing resistance and axial tip deflection is described using q-z curve.

The t-z curves were generated in SACS for every soil layer using the relationship

outlined in API RP2A-LRFD (1993) and are presented in Figure 6-10, except that

the soil spring stiffnesses were reduced to reflect the reduction in limiting soil

parameters as discussed in Section 4.4.

6.5.3. LOADING MODEL

Loading on the platform can be divided into three load cases, namely dead loads,

live loads and environmental loads. The dead loads include self weight of the

platform in addition to equipment, appurtenances self weight and buoyancy. Live

loads on the platform decks were obtained from the analysis in Chapter 5. The live

loads were applied as uniformly distributed loads in the model.

For drag-dominated structures, the environmental loading arises from the combined

effect of waves, currents and winds. The probability that a certain combination of

these metocean parameters is exceeded is a key aspect affecting the platform

response statistics and hence the overall platform reliability. This problem was

tackled by transforming the metocean variables into a single structure response

variable (global base shear) and examining the long term statistics of this variable.

To perform reliability analysis, pushover analysis was executed using the mean



value of the environmental conditions, which were obtained using the parameters

identified in Table 2-5. The associated wind and current loads should be used with

the maximum wave height but as the associated values were not available from the

metocean reports, this research used 100 year return period for wind and current.

The use of the 100 year return period instead of the associated values is conservative

but provided an upper bound to the probability of failure on the case research

platform. This approach is consistent with the strategy to provide upper bound of

probability of failure under extreme storm conditions as discussed in Section 6.3.

The loading exerted on the space frame structure for the given environmental

conditions was calculated using SACS. The reference design load was used as the

100 year return period load, which is the load with an annual exceedance probability

of 1%. Wave kinematics were obtained from appropriate wave theories and

adjusted (downwards) to account for directional spreading of wave energy. The free

stream current was also adjusted downwards to account for current blockage by the

structure. Force coefficients were selected based on the flow regime and an

assumed marine growth thickness of 100mm.

Wave kinematics on the platform was derived using Morison’s equation as provided

in Figure C.3.2-3 of API RP2A-LRFD (1993), which defines the regions of

applicability of the various functions. The analysis parameters were obtained from

Table 2-5 and entered in the API RP2A-LRFD (1993) graph. Using the above

parameters, two-dimensional regular wave kinematics showed that Stoke 5th order

wave theory was appropriate as shown in Figure 6-11.

6.6. PUSHOVER ANALYSIS

Pushover analysis is an ultimate strength analysis that includes member failure due

to yielding, buckling and soil-pile failure as well as joint failure. Whereas

conventional analysis for design purposes mainly focuses on the first failure of a

structural member, global non-linear pushover analysis accounts for possible

redistribution of forces and subsequent member failures until system collapse.

Pushover analysis follows an event-to-event strategy, tracing first fiber yield,

occurrence of plastic hinges and failure of each member up to and beyond the

maximum capacity of the whole structural system. The introduction of plasticity

reduces the stiffness of the structure and additional loads due to subsequent load



increments will be re-distributed to members adjacent to the member that has gone

plastic. This procedure (progressive collapse of the member) is continued until the

structure as a whole collapsed or pushed over. Further description of the static

pushover method can be found in several references (Puskar et al., 1994, Bea et al.,

1988, Lloyd and Clawson, 1983).

Pushover analysis was executed in this research using the non-linear COLLAPSE

module in SACS. The model considered the behavior of the piled foundations,

fluid-structure interaction, combined structure foundation collapse mechanisms,

stiffness and the mass of the jacket and topside modules.

The COLLAPSE analysis provided the key failure mode (ultimate strength) under

the action of extreme storm and operating overload conditions and identified those

members that participated in each failure mode including parallel and series

subsystems.

The mathematical model was based on the mean (or best estimate) of the properties

and capacity models for structural steel and soil. The following SACS modules

were used to perform elasto-plastic analysis:

• COLLVUE to perform interactive collapse result processing,

• SEASTATE to generate environmental loads, and

• PSI to perform non-linear foundation analysis.

6.6.1. EXTREME STORM CONDITIONS

The static lateral pushover typically consists of a representative “snapshot” of lateral

wave forces acting on the platform structure. To execute pushover analysis under

extreme storm conditions, two types of loading were identified:

• Vertical operating load, and

• Lateral extreme storm load.

The vertical load is transferred from the deck to the jacket and acts as a constant

load. The vertical loads include dead loads, which are made up of self weight plus

equipment weights on the deck, and live loads, which were assessed based on

Section 5.10. The lateral load is the load that would push the structure to its

ultimate capacity.

To execute pushover analysis, vertical loads were first applied. The SEASTATE



case was then executed with the modified basic model file to combine the loads and

convert to basic load cases. The storm loads were incrementally increased above a

reference value (100 year storm) to derive the collapse load. The load vector

includes wave, wind and current forces with a notional return period of 100 years.

COLLAPSE analysis was then executed using the output file of the SEASTATE and

the collapse input file. This implies that the wave height is fixed (at the 100 year

wave height) and the analysis determines how many multiples of this load can be

taken by the structure before collapse. This load application procedure does not

account for changes in the load pattern as the wave height is increased. This

approach provided reliable results as long as no additional horizontal elevations are

submerged (DNV, 1999).

Under extreme storm conditions, collapse could result in either of the two major

mechanisms: (1) shear dominated or (2) overturning dominated. In the former, the

critical piles would be subjected to lateral failure, whereas in the latter, the critical

piles would be subjected to axial failures. Tang and Gilbert (1993) evaluated the

reliability of offshore pile systems, and considered that the collapse mechanism

tended to be shear dominated in relatively shallow waters while overturning

dominated in greater water depths. The collapse strength of space structures was

expressed in this research in terms of global environmental loading base shear. The

platform collapse was defined by the maximum value of the total environmental

load applied to the structure just before collapse.

Figure 6-12 shows a shear dominated mechanism for the platform as the critical

piles failed laterally. This failure mechanism is consistent with observations made

by Tang and Gilbert (1993) for shallow waters. The output from the pushover

analysis produced a Reserve Strength Ratio (RSR), which is defined as the ratio

between the base shear at platform collapse and that of the 100 year wave load. The

calculated RSR is indicated in Figure 6-12 and denoted by Load Factor of 5.5.

6.6.2. OPERATING OVERLOAD CONDITIONS

To the Author’s best knowledge, all reported pushover analyses in the literature

address extreme storm conditions. The use of pushover analysis in the vertical

direction was required in this research such that the calculated probability of failure

under operating overload condition is consistent with that under extreme storm



condition. Although having not been attempted in the past by other researchers, the

use of such an approach was employed. To validate calculated probability of failure

under operating overload conditions, an alternative method was used in this

research.

To model soil pile interaction in the vertical direction, the q-z curves recommended

in API RP2A-LRFD (1993) were scaled down in order to model the effect of

carbonate soils on the pile axial capacity. The reduced capacity represented the

reduction in limiting values between carbonate and non-carbonate soils.

To perform the pushover analysis in the vertical direction, dead loads and

environmental loads were first applied. Dead loads were represented by self weight

of the structure and the own weight of all equipment while environmental loads

were represented by a one year return period to model the environmental loads at

operating conditions. OALL values developed in this research were used in the

analysis.

To execute pushover analysis in the vertical direction, dead loads were first applied.

The SEASTATE case was then combined with the dead loads. The live loads were

incrementally ramped up above the OALL values to derive the collapse load.

Figure 6-13 shows that the platform dominant failure mechanism is in the piles with

a load factor of 6.8. The collapse load equals the computed load factor (6.8)

multiplied by the live load effect. The live loads were calculated in the analysis

using the values shown in Table 5-3. In Figure 6-13, the deformation is magnified

to identify the final step in the analysis before the platform collapsed. The piled

foundation system plunged into the soil. The slight lateral deformation is due to the

P-Delta effect.

6.6.3. ANALYSIS OF THE RESULTS

The high value of the load factors (5.5, 6.8) in both pushover analyses, when

compared to an industry norm of around 2.0, was due to the conservative design of

the platform structure and piles. The conservatism in the original design of the

platform was partly caused by considerations such as the need for extra stiffness due

to pre-service requirements such as transportation and lifting but could also be

attributed to two main reasons. Firstly, the use of conservative open area live loads

during design as opposed to the values derived in this research contributed to the



conservatism in design. Secondly, the use of subjective but conservative limiting

soil parameters during pile design resulted in larger pile sizes than the case if

findings of this research were implemented.

6.7. LOAD AND RESISTANCE STATISTICS

In order to calculate the probability of failure using the pushover analysis results, the

statistical parameters for the resistance and loads established in this research were

employed. This section describes the statistical parameters used to calculate the

probability of failure under extreme storm and operating overload conditions.

6.7.1. RESISTANCE STATISTICS

The statistical parameters considered member and foundation strengths. The main

aspect of member strengths is variability in steel yield strength which has a COV of

approximately 10% (Cox, 1987). The influence of dimensional tolerances, such as

out-of-roundness and out-of-straightness, has smaller coefficients of variation since

they are statistically uncorrelated.

If all member strengths are fully correlated, the system collapse strength will have a

COV of 10% (Boon et al., 1993). If the member strengths are statistically

independent, the COV of the collapse strength will reduce (Tromans and van de

Graaf, 1992). In any case, Boon et al. (1993) found that the influence of structural

uncertainty is of minor importance. In this research, COV for steel member strength

was assumed to be 10%.

The statistical parameters for offshore piled foundations were also required to

determine the platform resistance. However, while the structural steel strength and

the mechanical behavior of steel structures are relatively well researched and can be

rationally quantified, this is significantly less so for the behavior of offshore piled

foundations. Van Langen (1995) indicated far more modeling and parameter

uncertainty in the description of axial pile capacity and lateral pile capacity when

compared to uncertainties in the strength of steel structure. Further, due to the

dominance of model and parameter uncertainties, pile capacities must be assumed to

be correlated and system effects do not reduce coefficient of variation. Even if all

parameters such as pile penetrations and soil strengths are measured accurately, this

modeling uncertainty cannot be reduced. It will affect the predicted pile capacities



of all piles in a structure in the same manner and therefore assuming full correlation

for evaluating uncertainty in system failure of piled foundations appears appropriate.

The resistance has some Type I uncertainty and was modeled using COV of 10%

(Bea, 1990). In addition, modeling uncertainty would also be present. In the

Arabian Gulf, this research showed that the resistance is strongly dependent on the

behavior of the seabed soils. The carbonate sediments, which are highly variable

and imperfectly understood, increased this uncertainty. Hence, considerably higher

uncertainties were calculated (36%) as shown in Figure 4-19.

6.7.2. DEAD LOAD STATISTICS

The COV for dead loads was established in this research by reference to available

literature. Moses (1980) assumed a mean of 1.05 and COV of 8% for dead loads of

building structures. The Author used similar mean (1.05) for dead loads but

considered that a value of 8% for the COV to be high for existing offshore

platforms.

Generally, the weight of an existing platform is known within 3% accuracy which is

the tolerance that can be practically achieved by load cells. The use of load cells is

common practice in adopted similar the fabrication yard before load out and

transportation of any substructure or topside in the Arabian Gulf.

Further, the use of accurate weight reports before transportation and installation is

common practice in the offshore industry to ensure that the capacity of the derrick

barge crane is sufficient for the offshore lift. During operation, weight control is

normally updated to reflect any modification to the platform. Hence, the remainder

of the research considered COV of 5% for the dead load.

6.7.3. LIVE LOAD STATISTICS

The load statistics for OALL are presented in terms of the mean and coefficient of

variation (COV) of the lifetime maximum load effect. These statistics are presented

in Table 5-3.

6.7.4. ENVIRONMENTAL LOAD STATISTICS

Uncertainties in the environmental loads are due to inherent uncertainties in the



environment itself (Type I), limitations in the mathematical models to replicate

measured storm data and extrapolation techniques to predict extreme events (Type

II). To account for Type I uncertainty, long term wave load distribution was

required. The long term wave load distribution is an expression that enables an

estimate of the wave load level for any return period (typically 100 years or higher).

The fit of data from this step was used to extrapolate to long return periods. The

shape of the fit beyond the range of available data may have a significant impact on

the calculated probability of failure.

Derivation of the statistics of wave loads required long term wave height parameters

to be established. This section describes the derivation of the long term wave

heights which was used to develop the statistical parameters of the wave loads.

Computing the long term maximum wave heights and current speeds employed the

metocean data defined in Table 2-5. To identify the most suitable distribution,

several competing models were tested and a Weibull distribution was found to

represent the data for both variables. For such distribution, the relationship between

the natural logarithm of the maximum wave height or current speed and the

logarithm of the natural logarithm of the return period is shown in Table 6-1. The

linear trend depicted in Figure 6-14 for maximum wave heights and in Figure 6-15

for current speed shows that a Weibull distribution is appropriate for the Arabian

Gulf data.

Using regression analysis, the maximum wave heights and current speeds for higher

return periods (1000 year and 10000 years) were extrapolated as shown in Table 6-1

and plotted in Figure 6-14 and Figure 6-15.

Using the return periods and extrapolated long term maximum wave heights and

current speeds, a number of linear static analyses were executed using SACS to

calculate the base shear for every return period. The calculated base shears are

shown in Table 6-2.

To identify a distribution that fit the base shear data points, the approach described

in Section 3.4 was employed and the calculations are shown in Table 6-3. A

Weibull distribution was found to provide the best fit to the data as evident by the

straight line shown in Figure 6-16.

To obtain the Weibull distribution parameters, the method described in Appendix F



was employed. The scale and shape parameters were calculated using data from

Table 6-3. The resulting slope and intercept of the curve is 4.59 and -32.87,

respectively. The corresponding scale (θ) and shape parameters (m) were 1298 and

4.6, respectively.

For the computed Weibull shape and scale parameters, the mean value and standard

deviation of the base shear were found to be 1186kN and 11.7kN, respectively,

resulting in COV of 1.0% for the environmental loads.

The uncertainty discussed above is that due to inherent variability (Type I). Other

sources of uncertainty may also be incorporated into the assessment and these are

termed Type II or modeling uncertainty. This type stems from uncertainty in the

prediction of the extreme storm conditions and the hydrodynamic forces resulting

from the storm conditions.

To account for Type II effects in the reliability model, Efthymiou et al. (1996)

estimated a coefficient of variation value of 8% or less for jackets analyzed using

modern techniques. In this research, the coefficient of variation for the uncertainty

in hydrodynamic load calculation was taken as 6% on the basis of relatively small

Type I uncertainty.

The resultant uncertainty combines Type I and Type II and was required to calculate

the probability of failure. In the Arabian Gulf, the combined load COV of the base

shears was calculated as ( ) ( ) %1.606.001.0 22 =+ .

Full definition of the environmental load statistics requires evaluation of a bias

factor. Bias is defined as the mean of the maximum expected wave base shear

divided by the nominal design base shear. From the analysis above, the mean base

shear was calculated as 1186kN as described above and the nominal (100 year

return period) was 1281kN as shown in Table 6-2 resulting in a bias factor of 0.92.

6.8. PROBABILITY OF FAILURE CALCULATIONS

The annual probability of failure Pf for extreme storm conditions was estimated as

the annual probability of the load exceeding the structural resistance. If the

structural resistance could be denoted by a single value (i.e. deterministic), then the

target reserve strength ratio (RSR) to achieve a specified probability of failure under

environmental overload is read directly off the non-dimensional load versus return



period graph. Conversely, if the failure load is known, the probability of failure of

the platform can be established by constructing a diagram using the relationship of

the normalized base shears for different return periods.

For operating overload conditions, a simple measure of the structural reliability may

be given by superimposing the expected ultimate strength of the system on the load

distribution and considering the region of overlap between the two curves to indicate

the probability of failure. This was performed using an analytical approach which

employed closed form equations to calculate the probability of failure for a complete

system. In this approach, FORM was applied and the input parameters were derived

from pushover analysis. Alternatively, the probability of failure was determined for

a single pile then a method was applied to establish the probability of failure of the

pile group. In FORM, the probability of failure was computed using the statistics of

the loads and resistance which are treated as normal variables.

For consistency with the API RP2A-LRFD (1993), a comparison of the method used

in this research to calculate the probability of failure against that used to calibrate

API RP2A-LRFD (1993) was conducted. The investigation revealed that the API

RP2A PRAC (such as API PRAC 80-22, API PRAC 86-29B and API PRAC 87-29)

reports did not consider system reliability in the calibration. In all API PRAC

documents, only members and components were considered. However, one

objective of this research was to evaluate system reliability so it was necessary to

employ a different approach and the methodology adopted in the industry to

compute the probability of failure is described in this section and was used to

estimate the probability of failure of the system.

6.8.1. USING PUSHOVER ANALYSIS RESULTS

Pushover analysis results were used to calculate the probability of failure of the

entire system by calculating the Reserve Strength Ratio (RSR). The RSR is denoted

by the ratio of expected ultimate strength to the design load. The probability of

failure was then estimated using Level 2 reliability method or FORM.

In calculating the RSR, the ultimate capacity was represented by the base shear at

collapse in the direction that causes failure. For extreme storm overload condition,

the direction of failure is associated with the highest wave height (shamal) as shown

in Figure 6-12. For the operating overload condition, Figure 6-13 shows the



collapse for operating overload in the vertical direction.

Table 6-4 lists the input data used to calculate the probability of failure for the

extreme storm and operating overload conditions. The input data quantified the

loads (dead, live, storm), resistance (pile axial and pile lateral capacity) and the

statistical parameters of those loads and resistances.

The dead loads represent self weight plus equipment weight. The self weight was

generated by the software and the equipment weights were calculated from vendor

datasheets. The open area on the upper and lower decks constituted approximately

50% of the total area. Hence, the total live load was calculated by multiplying the

open area on the upper and lower decks by the values shown in Table 5-3, resulting

in a load effect on the pile equals to 1688kN. The live load values shown in Table

5-3 are applicable to the platform under consideration.

The capacity of piles in the vertical direction is defined as the vertical load causing

collapse and was computed by multiplying the load factor computed in Section 6.6.2

(6.82) by the reference live load (1688kN) and adding the dead load. This approach

is similar to that adopted in calculating the collapse load under lateral loads.

Similarly, pile capacity in the lateral direction was obtained by multiplying the

reference base shear for 100 year return period by the load factor (5.5) calculated in

Section 6.6.1.

The statistical distribution and parameters of the pile capacity in the vertical

direction was determined in Section 4.6.1, while those in the lateral direction

adopted values used by Moses (1980).

The combination of the loads for the case of operating overload excluded

environmental loads. The operating overload condition represents shutdown

conditions, which are usually planned under very mild (Hs<1m) weather conditions

because supply boats, which are required to service such operation, only operate

under mild conditions. Further, the load combination for extreme storm conditions

excluded live loads because operational guidelines on offshore platforms do not

allow live loads on open areas during storm conditions.

Table 6-4 presents the input data used in the calculations of the probability of failure

under operating overload and extreme storm conditions.

The probability of failure under operating conditions was calculated using Equation



3-9 resulting in reliability index of 4.6, which corresponds to a probability of failure

of 2.4*10-6.

The probability of failure under extreme storm conditions employed the relationship

between the long term base shear values and corresponding return periods shown in

Table 6-2 and plotted in Figure 6-17. The probability of failure under extreme

storm condition was computed as the inverse of the return period corresponding to a

collapse load of 7045kN, which was calculated using the pushover analysis results

(Load Factor = 5.5, Base Shear for 100 year RP = 1218kN) as presented in Section

6.6.1. The resulting probability of failure under extreme storm condition was

2.3*10-71.

Results of the analysis demonstrate that the operating overload condition dominates

the failure mechanism due to its very low probability of failure (2.4*10-6) compared

to the probability of failure extreme storm condition (2.3*10-71).

The very low probability of failure under extreme storm conditions is due to the

dependency of the reliability of offshore structures on the environment. Van de

Graaf et al. (1994) studied extreme load normalized to its 100 year value against

return period for offshore platforms in a number of geographic areas around the

world. The study concluded that the reliability of offshore platforms in the

Northern, Central and Southern North Sea, which were designed to API RP2A-

WSD, is significantly higher than those in the Gulf of Mexico. The result of the

study is reproduced in Figure 6-18 which shows reliability levels for return periods

up to one million years. Van de Graaf et al. (1994) clarified that reliability levels

calculated beyond ten thousand years are of a “notional” nature as the extrapolation

beyond ten thousand years is so great. However, Van de Graaf et al. (1994) pointed

out that non-linear effects associated with very long return periods could be

expected to limit, rather than amplify, extreme conditions.

The normalized load against return period for the offshore platform in the Arabian

Gulf is imposed in Figure 6-18. Inspection of Figure 6-18 reveals the benign nature

of the long term environmental conditions in the Arabian Gulf compared to other

parts of the world especially that of the North West Shelf of Australia. A feature

particular to the Arabian Gulf is the relative insensitivity of extreme loads to return

period when compared to the results for other locations. The high reliability level

for platforms in the North Sea reported by van de Graaf et al. (1994) is consistent



with findings of this research for platforms in the Arabian Gulf.

The long term statistics of the wave heights are central to the reliability calculations.

Hence, a description of the long term wave heights in various parts of the world was

of interest to justify the high reliability levels in the Arabian Gulf when compared to

the reliability of offshore platforms in the North Sea. The ratio of long term (ten

thousand year) maximum wave heights to the 100 year values for a number of

geographic locations around the world, including the Arabian Gulf, is shown in

Table 6-5. The long term wave heights in various parts of the world were derived

from research publications as outlined in Table 6-5, while the long term wave height

for the Arabian Gulf was based on findings of this research.

In conclusion, the long term maximum wave height in the Arabian Gulf explains the

high reliability for offshore platforms in the Arabian Gulf. This observation

provided a partial explanation of the consistent finding of the Author regarding the

dominance of operating overload when reviewing and designing offshore structures

in the Arabian Gulf.

6.8.2. VALIDATING PROBABILITY OF FAILURE UNDER

OPERATING OVERLOAD CONDITION

As previously mentioned in Section 6.6.2, pushover analysis has usually been

associated with lateral overload due to storm events. In this research, the probability

of failure was calculated using the results of pushover analysis in the vertical

overload condition. To the best knowledge of the Author, application of pushover

analysis in the vertical direction has not been contemplated in previous research

work. Hence, it was important to validate the results of applying pushover analysis

in the vertical direction.

The validation of the system probability of failure under operating overload

conditions was established using an analytical approach which computes the

reliability of a single pile and then incorporates system and group effects.

The reliability of single axially loaded piles was studied by several Authors

including Bea (1983), Sidi (1985), Orchant et al. (1988), Tang (1989) and Barker et

al. (1991).

If the only load effects to be considered are dead and live loads, the reliability index



β associated with the linear performance function can be calculated using first order

reliability method or FORM (Whitman, 1984, Barker et al., 1984, Becker, 1996,

Withiam et al., 1997) as described in Section 3.5.

Equation 3-12 reveals that the reliability index is a function of the bias factors,

COV, dead to live load ratio and the safety factor. However, Moses (1980) used a

reliability index of 2.11 for the calibration of piles resistance factors in API RP2A-

LRFD (1993). The use of a single reliability index in code formulation implies

insensitivity to the many factors that affect the calculation of reliability index.

In this research, the reliability index βs of a single pile under operating overload for

the platform shown in Figure 6-6 was calculated using the following input

parameters:

• Factor of safety = 2.0, which was calculated from the deterministic analysis of

the platform,

• Ratio of dead to live load = 3, which was calculated from the deterministic

analysis of the platform,

• Dead load statistics as determined in Section 6.7.2,

• Statistical parameters (Bias = 1.0, COV = 0.15) for live loads as determined in

Section 5.10, and

• Statistical parameters for the resistance was based on the posterior distribution

values (Bias = 1.08, COV = 0.23) derived in Table 4-22.

( )( )

( ) ( )[ ]

( ) ( )( )

( ) ( )[ ]60.2

15.005.0123.01ln

23.0115.005.01

1305.113208.1ln

11ln

11

1ln

222

2

22

222

2

22

=++×+

⎥⎥⎦

⎤

⎢⎢⎣

⎡

+++

+×+××

=++×+

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

+

++

+

⎟⎟⎠

⎞⎜⎜⎝

⎛+

=QLQDR

R

QLQD

QLQD

R

sCOVCOVCOV

COVCOVCOV

QLQD

QLQDFS

λλ

λ

β

The calculated reliability index for a single pile (2.60) corresponds to a probability

of failure of 4.9*10-3 for a single pile using Microsoft Excel function

NORMDIST(β,0,1,TRUE).

Under operating conditions, the calculated probability of failure for a single pile

(4.9*10-3) is three orders of magnitude greater than the calculated probability of

failure using pushover analysis in Section 6.8.1 for the vertical direction (2.4x10-6)

because the pushover analysis was carried out for the complete structural system



while the above analysis was performed for a single pile.

The derivation of system reliability from single pile reliability required

consideration of system and group effects. Survey of the literature identified a

method that was developed by Zhang et al. (2001) which derived groups and system

effects from single pile reliability index. The method of Zhang et al. (2001) was

used to compute the system reliability index for the system from the results of the

single pile reliability.

Zhang et al. (2001) measured group effect using group efficiency factor. This factor

is the ratio of the group ultimate capacity to the sum of individual capacities of all

piles. The group effect factor is a function of pile spacing and group size.

System effects in piled foundations arise due to pile-superstructure interaction. A

system factor was used to measure system effects and is defined as the ratio of the

load required for all the piles in a pile system to reach their ultimate computed

capacity and that required for the most heavily loaded pile to reach the same state

(Bea, 1983). For a 4-leg jacket platform, Zhang et al. (2001) suggested a unity

value for system effect to model no load distribution.

To account for group and system effects, Zhang et al. (2001) developed the

following relationship:

( )Β

⎟⎟⎠

⎞⎜⎜⎝

⎛

++

+Β

×+

ΒΑ

=2

2

11

ln21

ln RG

RS

SG

COVCOV

ζχ λλββ

Equation 6-1

In which:

RSRG λλλλ ζχ ××= Equation 6-2

222RSxRG COVCOVCOVCOV ++= ξ Equation 6-3

( ) ( )[ ]222 11ln QLQDRS COVCOVCOVA ++×+= Equation 6-4

( ) ( )[ ]222 11ln QLQDRG COVCOVCOV ++×+=Β

Equation 6-5

The group reliability index βG equation was simplified as



( )c

BSG ++= ζχ λλββ

ln Equation 6-6

Where: βs = Reliability index of a single pile

ΒG = Reliability index of a pile group

COVRS = Coefficient of variation of resistance for a single pile

COVRG = Coefficient of variation of resistance for pile group

λζ = Bias factor of the group effect

λχ = System bias factor

c = Constant that accounts for the errors associated with simplification of the main equation

COVχ = Coefficient of variation of system effect

COVζ = Coefficient of variation of group efficiency

The bias factor of the group effect (λζ) is defined as the ratio of the measured and

nominal group efficiency factors and ranges from 1.19 to 1.4 (Zhang et al., 2001).

The system bias factor can be taken as 1.0, 1.25, 1.5 and 2.0, with unity indicating

no system effect and the constant was calculated as -0.20 (Zhang et al., 2001). The

system COV was estimated between 0.10 and 0.20 (Bea, 1983) and the COV of

group efficiency ranges from 0.10 to 0.14 (Zhang et al., 2001).

Once the overall bias factor λRG and coefficient of variation COVRG of a pile group

associated with a specific prediction method are determined, the reliability index of

the pile group associated with the allowable stress design could be calculated.

Using the method described above, the probability of failure under operating

overload was calculated. The load factors used in the reliability analysis were 1.25

for dead load and 1.50 for live load in accordance with API RP2A-LRFD (1993)

load combination requirements. The load statistics are presented in terms of mean

and coefficient of variation (COV) of the bias factors. The bias factor is defined as

the ratio of the actual load over the nominal load.

A lognormal distribution of the bias factors of both dead and live loads was

assumed. The use of a lognormal distribution for OALL agreed with assumptions

made by Moses (1980) and also agreed with the calibration of the resistance factors

for bridge foundations adopted by AASHTO (Barker et al., 1991).



A dead to live load ratio of three was used in this research and reflects the actual

ratio for this platform on the basis of the derived live load magnitudes shown above.

The sensitivity of various dead to live load ratios was examined in this research and

is shown in Figure 6-19. It is clear that changing the dead to live load ratio is

insensitive to changes in the calculated reliability index.

For a constant dead to live load ratio of 3 and changing the factor of safety, the

relationship between the reliability index and the COV is Figure 6-20. The

relationship shows that the reliability index increases with increasing factor of safety

but reduces with increasing COV.

In the application of the method proposed by Zhang et al. (2001), a range of values

for the various parameters can be assumed. The values were selected so as to verify

the research premise that operating overload dominates the failure mechanism in the

Arabian Gulf. Consequently, the parameters were selected to provide a lower bound

and an upper bound for the reliability index.

To maximize the reliability index, upper limit values for the numerator (λχ = 2.0, λζ

= 1.4) and lower limit for the denominator (COVχ = 0.10, COVζ = 0.10) were

selected. To minimize the reliability index, the numerator values were minimized

(λχ = 1.0, λζ =1.19), and the denominator values were maximized (COVχ = 0.20,

COVζ = 0.14).

Hence, the maximum reliability index is:

386.036.01.01.0 222 =++=RGCOV

( )( ) ( )[ ] 4.52.0

15.005.01386.01ln4.12ln60.2

222=−

++×+

×+≈Gβ

( ) 8107.2,1,0, −×=−= TRUENORMDISTPf β

The minimum reliability index is:

43.036.014.02.0 222 =++=RGCOV

( )( ) ( )[ ] 85.22.0

15.005.0143.01ln19.11ln6.2

222=−

++×+

×+≈Gβ

A comparison of the probability of failure using the pushover in the vertical

direction (Pf = 2.4*10-6), which is described in Section 6.8.1, falls within the range



of Pf = 2.2*10-3 to 2.7*10-8 calculated using the method developed by Zhang et al.

(2001).

To illustrate the system factor effect, the calculations were repeated for various

values of system bias factors while keeping all other parameters unchanged. A

value of 1.19 was assumed for the group effect λζ. The coefficient of variation

values of 0.40, 0.20 and 0.14 were assumed for the single pile reliability, system

effect and group effect, respectively. Figure 6-21 shows the significance of the

system effect on the calculated reliability index. For system factor bias of 1.50, as

in the case of four-legged platforms in the Gulf of Mexico (Agarwal et al., 1996),

the probability of failure is 1 to 2 orders of magnitude smaller than those of single

piles.

To check the sensitivity of the assumed coefficient of variation, the analysis was

repeated with various other values for the coefficient of variation for the group and

system factors. As can be seen from Figure 6-22, an increase in the group and

system coefficient of variation reduces the group reliability index for a specified

bias factor. At higher system bias factors, the higher the COV, the more

pronounced the reduction in group reliability.

6.9. CALIBRATION OF ENVIRONMENTAL PARTIAL LOAD

FACTORS

Despite the need to limit specifications for reassessment of existing platforms in the

Arabian Gulf to operating overload conditions, there was need to identify

environmental load factors to produce a complete set of specifications.

Partial load factors in API RP2A-LRFD (1993) offers plenty of scope for

influencing the outcome of reassessment by calibrating the partial factors to suit

regional environmental conditions. As previously mentioned, API RP2A-LRFD

(1993) was developed for the Gulf of Mexico and US waters and implicitly accounts

for the long term environmental conditions of the Gulf of Mexico as shown in

Figure 6-18. For other regions such as the North Sea and the North West Shelf of

Australia, several researchers identified that load factors should not be similar to

those for the Gulf of Mexico conditions.

Theophanatos et al. (1992) presented results from a joint industry project on the



calibration of LRFD for Mediterranean Sea platforms while Turner et al. (1992)

described the joint industry project work to adapt LRFD for use in the North Sea.

The calibration of the partial factors was undertaken using a reliability-based

procedure. Firstly, a set of one or more target reliabilities were assessed from a

range of representative components which were designed to the API RP2A-WSD

code. The partial factors were then adjusted so that the average reliability is close to

the targets.

The most significant difference between components designed for the Gulf of

Mexico and those designed for the North Sea and the Mediterranean arises from the

uncertainty in the environmental loading. The probability distribution for the

uncertainty in the extreme environmental load i.e., F/Fdes (20 year maximum base

shear force/ design base shear force) was typically found to have the parameters

shown in Table 6-7 for various geographic locations.

In both joint industry projects, the probabilistic description of gravity loads was

based on similar data to that adopted for the calibration of API RP2A-LRFD (1993).

Theophanatos et al. (1992) justified this approach due to the paucity of data related

to the Mediterranean Sea platforms. For dead loads, a bias of 1.0 and a COV of

0.06 were used. The short term live loads were defined only for the operating

conditions and values (bias = 1.0, COV = 14%) from the calibration of API RP2A-

LRFD (1993) were used. Theophanatos et al. (1992) assumed that these values

represented the expected maximum during the 20 year reference period.

Inspection of Table 6-7 reveals that the coefficient of variation for environment in

the Gulf of Mexico conditions is lower than the North Sea and the Mediterranean.

Hence, there was a case to justify lower partial load factors for extreme storm

loading in both regions. Nevertheless, the calibrated environmental load factors

(1.3) for the North Sea and the Mediterranean was found to be close to that adopted

for the Gulf of Mexico (1.35) and both studies concluded the practicality of

maintaining the environmental load factor of 1.35.

An extension of this argument was applied to the Arabian Gulf conditions using the

wave height statistics developed in Section 6.7.4 and comparing these to the

statistics shown in Table 6-7. The coefficient of variation of wave loads in Arabian

Gulf is smaller (6%) compared to other geographical regions. Hence, there was a

case to reduce the environmental load factor for reassessment of offshore platforms



in the Arabian Gulf. For the calculated COV of 6%, an environmental load factor of

1.21 would be recommended based on the application of the method described in

API RP2A-LRFD (1993) Commentary and discussed in Section 5.12.

However, in keeping with the approach adopted for the Mediterranean and the North

Sea, the Author considered it more justifiable to maintain the environmental load

factor (1.35) used in API RP2A-LRFD (1993) for reassessment of platforms in the

Arabian Gulf.

Nevertheless, the Author performed an analysis to determine the effect of reducing

the load factor on the computed reliability level. The use of a lower load factor

(1.25) results in higher return period compared to the use of a load factor of 1.35.

The relationship between the load factor and the return period is shown below (Bea,

2008):

SeLF ln**8.0 σβ= Equation 6-7

)ΤΡΥΕ,0,1,= βNORMDIST(fP Equation 6-8

Where: LF = Load Factor

0.8 = A coefficient used to separate the loading and capacity uncertainties

β = Target annual safety index

Slnσ = Standard deviation of the logarithms of the annual maximum

combined loading.

fP = Probability of failure, calculated using Excel spreadsheet function

TYPE = TRUE or FALSE as per Excel function

Rearranging the terms in Equation 6-7:

( ) SLF ln**8.0ln σβ= Equation 6-9

Hence, for the Arabian Gulf, a reduction in the load factor (from 1.35 to 1.25)

results in an increase in the reliability index, a reduction in the probability of failure

and an increase in the return period.

( )( ) 25.1

35.1

25.1

35.1

lnln

ββ

=LFLF

Equation 6-10



( )( )

( )( ) 2485.17

25.1ln35.1ln

lnln

35.125.1

35.125.1 =∗=∗= ββ

LFLF

This leads to an extremely low ( )12710− probability of failure. Such extreme value is

only possible in a mathematical model and has little physical meaning. Such result

suggests that the reliability of offshore platforms in the Arabian Gulf is insensitive

to the selected environmental load factor.

6.10. SUMMARY

This Chapter presented the reliability analysis for a selected platform in the Arabian

Gulf under extreme storm and operating overload conditions. The selection criteria

for this platform were such that any conclusions from this reliability analysis could

be extended to cover other platforms in the Arabian Gulf.

A summary of the calculated probability of failure under operating overload and

extreme environmental conditions is shown in Table 6-6. The operating overload

conditions result in probability of failure that is many orders of magnitudes higher

than the corresponding storm overload conditions.

Inspection of Table 6-6 indicates that the pushover results with FORM in the

vertical direction described in this thesis to treat overload in the vertical direction

produced comparable results to the analytical method and can therefore be

considered appropriate for future use.

Given that operating overload conditions dominate the failure mechanism in the

Arabian Gulf, reassessment of existing platforms in the Arabian Gulf would be

sufficiently based on considering operating overload only.



Table 6-1: Extrapolation of maximum wave heights and current speeds in the Arabian Gulf to produce log term values for maximum wave heights and current speeds. The long term maximum wave heights and current speeds are employed in a series of linear static analysis to calculate base shear for the various return periods. The red colored bold figures indicate that the value is extrapolated. A plot of the maximum wave height values versus return period is shown in Figure 6-14 while Figure 6-15 extrapolates long term values for current speed

Ret

urn

Peri

od

Log

[ln(R

P)]

Max

imum

Wav

e H

eigh

t (H

max

) in

met

ers

Ln[

Hm

ax]

Cur

rent

Spe

ed (V

) in

m/s

L

n[V

]

5 0.2067 7.9 1.4586 0.93 -0.0758

10 0.3622 8.7 1.5476 0.96 -0.0373

50 0.5924 9.6 1.6487 0.98 -0.0186

100 0.6632 9.8 1.6864 1.00 -0.0003

1000 0.8393 10.7 1.7724 1.02 0.0243

10000 0.9643 11.3 1.8335 1.04 0.0430



Table 6-2: Using a series of linear elastic analyses of the platform model described in Section 6.5 and using the maximum wave heights and current speeds shown in Table 6-1, this table presents the output of the analyses (total base shear at every return period). The input and extracts from the output data for the SACS analyses are shown in Appendix J

Return Period (RP) (year)

Maximum Wave Height (m)

Total Base Shear (BS) (kN)

5 year 7.9 979

10 year 8.7 1090

50 year 9.6 1205

100 year 9.8 1281

1000 year 10.7 1392

10000 year 11.3 1540



Table 6-3: Tabulation of the parameters used to calculate the parameters of Weibull distribution for base shears in the Arabian Gulf

Empirical CDF RP BS x=ln(BS) i =

rank F(x)=i/(N+1) y=ln(ln(1/(1-F(x)))

1 850 6.745 1 0.125 -2.013

5 979 6.886 2 0.250 -1.245

10 1090 6.994 3 0.375 -0.755

50 1205 7.095 4 0.500 -0.366

100 1281 7.155 5 0.625 -0.019

1000 1392 7.239 6 0.750 0.326

10000 1540 7.339 7 0.875 0.732



Table 6-4: Input data for calculating the reliability index and corresponding probability of failure for the operating and extreme storm conditions. The reliability index and probability of failure for the operating condition was calculated using First Order Reliability Method (FORM) and employed the result of the pushover analysis described in Section 6.6. The resistance coefficient of variation of the piles in the vertical direction adopted the results of Section 4.6. The coefficient of variation for live load effects was based on the values developed in Section 5.7.3. The probability of failure for the extreme storm condition was computed using the long term base shear values developed in Figure 6-16. The probability of failure under extreme storm condition was defined as the inverse of the return period corresponding to the collapse load developed in Section 6.6

Operating Overload

Extreme Storm

Overload

Dead Load (kN) 9300 9300

Mean Value of Operating Live Load (kN) 1688 -

Base Shear (kN) for 100 year RP from

Table 6-2

- 1281

Reserve Strength Ratio (RSR) 6.82 5.50

Mean of Extreme Storm Load (kN) Section 6.7.4 - 1186

Collapse Load (kN) 9300+1688*6.82 = 20812

1281*5.50 = 7045

COV DEAD 5% 5%

COV LIVE Refer to Section 5.10 15% -

COVE Refer Section 6.7.4 - 6.1%

COVR Refer Section 6.7.1 36% 30%



Table 6-5: Comparison of the long term maximum wave height in the Arabian Gulf against other parts of the world showing the relatively benign environment in the Arabian Gulf that leads to the high reliability of offshore platforms in this region

Region Bias COV (H)

H10,000

/H100 References

North Sea 0.84 21% 1.32 DOE, 1990

North West Shelf, Australia 0.78 33% 1.55 Stroud, 1999

Gulf of Mexico 0.79 32% 1.37 Efthymiou et al., 1997

Arabian Gulf 0.91 6% 1.16 Table 6-1



Table 6-6: Summary of the calculated probability of failure under operating overload and extreme storm conditions using several method of calculation for the operating overload condition

Method of Analysis Operating Overload Storm Overload

FORM with Pushover Analysis Pf = 2.4*10-6 Pf = 2.3*10-71

FORM with Pile Group Method Pf = 2.2*10-3 to 2.7*10-8 -



Table 6-7: Environmental Partial Load Factors for the Gulf of Mexico conditions (API RP2A-LRFD, 1993) and factors proposed by LRFD for the Mediterranean and the North Sea for a reference period of 100 years (Moses and Stahl, 1998)

Location Bias COV (H)

Environmental Load Factor γW

Gulf of Mexico 0.79 32% 1.35

Central & Southern North Sea 0.84 21% 1.18

Northern North Sea 0.81 27% 1.25

South/ Eastern Mediterranean 0.87 25% 1.30

NW Australia 0.78 33% 1.36



10-1

10-2

10-3

10-4

10-5

10-6

10-21

10-22

10-40

PR

OB

AB

ILIT

Y O

F FA

ILU

RE

Operating Overloadcondition

Extreme storm conditions

HIG

HLO

W

Operating Conditions

Storm Conditions


Storm Conditions


Storm Conditions

Interaction of extreme storm and operating

conditions

Dominant failure

mechanism

U N

S A

F E

R

E G

I O

NS

A F

E

R E

G I

O N

Figure 6-1: Illustration of the dominant failure mechanism under various conditions. The chart shows that the dominant failure mechanism is determined by comparing the probability of failure under operating overload against extreme storm conditions. When the probability of failure under operating conditions is much lower than the probability of failure under extreme storm condition, then operating conditions dominate the failure mechanism. Conversely, when the probability of failure under extreme storm conditions is lower than that under operating conditions, then extreme storm dominate the failure mechanism. When the probability of failure under extreme storm condition is similar to that under operating overload condition, the dominant failure mechanism is determined on the basis of interaction of both conditions



Figure 6-2: Performance model for the cases of dominant extreme storm (left) and operating overload (right) conditions. In the dominant operating condition, the effect of horizontal load PH on the pile system is relatively small when compared to the effect of the vertical load PV



Figure 6-3: Plan view of the model platform decks used in the pushover analysis. The platform is 15m by 15m between the gridlines



Figure 6-4: The requirement for large open area on wellhead platforms is driven by the dimensions of drilling rig. This figure shows a jack-up drilling rig adjusted for one well and also shows alternative locations of the rig to drill other wells



Figure 6-5: Illustration of the probability of failure for the selected platform in this research under various conditions. The direction of the arrows indicates increasing/ decreasing probability of failure. The “X” indicates the selected platform positioning in relation to the population. This demonstrates that the selected platform provided a lower bound solution for the extreme storm condition as a result of choosing the deepest water in the Arabian Gulf and an upper bound solution for the operating condition as a result of choosing a wellhead platform with large open areas



Figure 6-6: SACS computer model geometry showing the 4-legged jacket structure in 100m water depth and the topside structure. The piles are driven 70m into the soil



Figure 6-7: A description of the nonlinear SACS computer model used in the static pushover analysis



Figure 6-8: A plot of the soil shear strength of two boreholes at a given site. The plot shows the variation of the interpreted shear strength along the depth, but also within the same layer. In this analysis, the average shear strength value in each layer was adopted



Figure 6-9: Input p-y curves for the soils in the case research platform. The curves were computed in SACS using API RP2A procedure described



Figure 6-10: Input t-z curves for the various layers in the case research platform. The curves were computed in SACS using API RP2A procedure but a reduction factor was applied to the calculated spring stiffness to reflect the findings of this research showing the reduced axial capacity of piles in carbonate soils



Figure 6-11: Assessing applicable wave theory for use in the analysis (Source: API RP2A-LRFD, 1993). The vertical axis is entered with the maximum wave height and apparent wave period. The horizontal axis is entered with the mean water depth and the apparent wave period. The outcome of the analysis defines the applicable wave theory to be used to derive hydrodynamic loading on the structure

Where: 2

appgTH

=

29.9*81.98.9

= 0.010

2

appgTd

=

104.09.9*81.9

1002 =



Figure 6-12: Results of the static pushover analysis showing the collapse mechanism to be shear dominated, where the piles are subject to critical failure. The deflected shape is shown only for the framed structure (in red) and not for the piles. The discontinuity shown between the piles and the frame represents the deformation when the frame collapsed



LOAD STEP 69 LOAD FACTOR 6.82LOAD STEP 69 LOAD FACTOR 6.82

Figure 6-13: The pushover analysis in the vertical direction was carried out to assess the dominant failure mechanism in the Arabian Gulf under operating overload. The COLLAPSE analysis shows that the dominant failure mechanism is in the piles



Figure 6-14: Extrapolation of maximum wave height in the Arabian Gulf shows its long term distribution follows a Weibull distribution

y = 0.4521x + 1.9901

1.0

1.3

1.5

1.8

2.0

2.3

2.5

2.8

3.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

log(ln(RP))

ln(H

max

)

100

year

RP



Figure 6-15: Extrapolation of current speed in the Arabian Gulf shows that its long term distribution follows a Weibull distribution

y = 0.1495x - 0.1012

-0.20

-0.16

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

0.16

0.20

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

log(ln(Return Period))

ln(C

urre

nt S

peed

)

100-

year

RP



Figure 6-16: Fitting the calculated base shears computed from long term maximum wave heights indicated that a Weibull distribution provided the best fit compared to other distributions as evident from the straightness of the trend line

y = 4.3939x - 31.497

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

6.80 6.90 7.00 7.10 7.20 7.30 7.40

ln(Base Shear)

Empi

rical

CD

F =

ln(ln

(1/(1

-F(x

)))

100-

year

RP



0

500

1000

1500

2000

2500

0.1 1 10 100 1000 10000 100000

Return Period

Bas

e Sh

ear

Figure 6-17: The probability of failure under extreme storm condition was calculated from the relationship between the long term base shear values and the corresponding return periods. Using the collapse load calculated from pushover analysis, the relationship provides the return period which corresponds to the collapse load. The probability of failure was (2.3*10-71) calculated as the inverse of the return period



Gulf of M

exico

Southern North Sea

Northern North Sea

Central North Sea

NWS of Australia

Arabian Gulf

1

1.5

2

2.5

3

1E+02 1E+03 1E+04 1E+05 1E+06Return Period (Years)

Nor

mal

ized

Loa

d

Figure 6-18: A comparison of the severity of environmental data in the Arabian Gulf to other parts of the world. The graph shows normalized extreme environmental load versus return period and demonstrates the dependence of platform reliability level on its environment (Van de Graaf et al., 1994). The normalized extreme environmental load versus return period for the Arabian Gulf was derived in this research



0.0

1.0

2.0

3.0

4.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

5.4

5.6

Factor of Safety

Rel

iabi

lity

Inde

xD/L=3 D/L=0.5

Figure 6-19: Effect of dead to live load on reliability index showing insensitivity of the D/L ratio on the reliability index



Figure 6-20: Effect of changing factor of safety and resistance COV on the calculated reliability index for single piles

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

Resistance Coefficient of Variation

Rel

iabi

lity

Inde

x

FoS = 2

FoS = 3

FoS = 4



2.0

3.0

4.0

5.0

6.0

1 1.25 1.5 1.75 2 2.25 2.5

System Bias Factor

Gro

up R

elia

bilit

y In

dex

Figure 6-21: The effect of system factor on the computed group reliability index. The green horizontal line presents the reliability index for a single pile and the red curve presents the reliability index for a group



2.0

2.8

3.5

4.3

5.0

5.8

1.00 1.25 1.50 1.75 2.00 2.25 2.50

System Bias Factor

Gro

up R

elia

bilit

y In

dex

COV=0.42 COV=0.49 COV=0.58 COV=0.69

Figure 6-22: Effect of changing the system and group coefficient of variation on the computed reliability index


Chapter 7.

CASE STUDY

7.1. BACKGROUND

A real life situation is presented in this Chapter to demonstrate the value of this

research. In one of the projects with an operator in the Arabian Gulf, the field

development department requested an investigation of the possibility of installing a

new piece of equipment weighing 800 tonnes (8000kN) on an existing platform

deck. The distribution of the additional load was not uniform to all four piles that

supported the platform. The reaction on the heaviest loaded pile was 336 tonnes

(3365kN).

An external consultant was appointed to investigate the case. The consultant

applied API RP2A-LRFD (1993) using subjective parameters for OALL and for the

limiting soil parameters. The consultant concluded that the additional load would

overstress the platform piles. To mitigate the overstress, the consultant

recommended strengthening the foundation system of the platform.

The field development department compared the cost of installing a new platform

with the costs associated with strengthening the existing foundation system and

decided to install a new platform at a cost of approximately USD35 million. The

decision was not driven by the cost of strengthening the existing foundation system

alone, as there was a need for a shutdown to effect the strengthening with

consequent loss of production. The difficulty of offshore work, accessibility

problems and the need for more effort to ensure adequate quality of workmanship

were contributing factors to the excessive costs associated with strengthening the

existing foundation system. Strengthening of existing offshore piles can be

prohibitively costly as was demonstrated by the historical case of North Rankin “A”

platform in the North West Shelf of Australia.

An investigation of this historical case, using the results of this research, showed

CHAPTER 7: CASE STUDY 308


that the reserve capacity of the existing piled foundations could have safely

accommodated the additional pile load of 336 tonnes.

Consequently, the requirement for strengthening of the existing piles or the

subsequent decision to install a new platform was not necessary. The decision to

install a new platform was fundamentally driven by lack of appropriate parameters

to be used in the reassessment of existing platforms in the Arabian Gulf.

A description of the platform and the consultant’s approach is presented, and is

followed by applying outcome of this research to demonstrate its value in practice.

7.2. DESCRIPTION OF THE PLATFORM

The platform in question is an unmanned wellhead platform in the Arabian Gulf,

which was installed in 41m water depth. The platform substructure is a 4 legged

jacket with nine well conductors and a superstructure consisting of cellar (EL (+)

12.5) and upper (EL (+) 17.8) decks and a helideck. The substructure was designed

to support eight (8) risers. Three of the risers had an outside diameter (OD) of 6”,

one with 12” OD and for with 10” OD. The platform has one boat landing, risers,

riser guard, barge bumper and other appurtenances like anodes.

The cellar deck area had a grated area of 131m2 and the upper deck had a grated

area of 213m2. The open areas were approximately 50m2 and 35m2 on the cellar and

upper decks, respectively. The open areas allowed for the footprint of the required

new equipment.

The consultant performed calculation of the pile capacity using the soil stratigraphy

shown in Table 7-1 and adopted API RP2A-LRFD (1993) method but used

subjective engineering parameters for the limiting values. Table 7-1 identifies the

soil strata and provides a generic description of each layer from the mudline to

80.8m below mudline. The consultant provided a capacity curve against the pile

penetration as shown in Figure 7-1.

Figure 7-2 shows an isometric view of the platform. The platform foundation

system consisted of four (4) similar piles, which were driven to the design

penetration depth. The pile outside diameter (OD) is 1219mm with 25mm wall

thickness for the pile full length. The pile makeup is shown in Figure 7-3.



7.3. MATHEMATICAL MODEL

Finite element models of the jacket together with the pile-soil properties were

developed to monitor the behavior of the structure. The mathematical model is

shown in Figure 7-4.

To model soil-structure interaction, each soil layer was substituted with an

equivalent tubular having identical soil-pile lateral characteristics. The equivalent

tubular non-linear properties were derived by the soil consultant from pile lateral

load (p-y) curves using similar approach to that described in Section 6.5.2. The (p-

y) curve data considered soil layer thicknesses, pile diameter and the equivalent

tubular sectional area.

The pile was modeled up to the actual penetration as a tubular inside the leg without

using grout in between the jacket leg and the pile. The analysis considered P-Delta

effects, with an initial imperfection of 0.5% of the member length for braces to

monitor the buckling and post-buckling behavior and a strain hardening of 3% for

all jacket steel components.

The loading input data to the computer model was grouped to vertical loads and

lateral loads. The vertical loads consisted of dead loads and OALL. The self weight

of the modeled members were generated by the software through assigning a value

for gravity (g = 9.81 m/s2). The non-generated dead loads were calculated and

entered into the model as uniformly distributed or concentrated loads on the

respective members. Table 7-2 summarizes the load reactions on the structure.

The analysis only investigated the operating overload as the objective of the

research was to assess the effect of adding more equipment loads on the platform

integrity. The additional piece of equipment did not affect the extreme storm

conditions.

Environmental parameters shown in Table 7-3 for the operating condition were used

in the calculation of the hydrodynamic lateral loads. A constant marine growth

thickness of 50mm radial from EL (+) 1.5 to EL (-) 16.5 was assumed to reduce

linearly to zero at mudline.

The hydrodynamic coefficients were in line with API RP2A-LRFD (1993). Wave

and current forces exerted on the anodes were accounted for by increasing the

coefficient of drag and inertia of the member at the location of anodes. Wave and



current forces exerted on the gratings, handrails and non-modeled members were

also accounted for by increasing the drag and inertia coefficient values of the tubular

members by a factor of 1.2. The structural analysis of the platform was performed

using SACS program modules.

7.4. ANALYSIS OF THE RESULTS

The analysis was executed and an output file was produced in Table 7-4 to show the

reactions of the most loaded pile in two cases. The first case represented the soil

consultant’s calculations, which used subjective parameters and industry practice in

the Arabian Gulf. The second case implemented findings of this research to analyze

the case.

In Table 7-4, the existing dead loads (13096kN) on the piles were distributed

equally to the four piles. Applying a load factor of 1.3 in accordance with API

RP2A-LRFD (1993), the factored pile dead load was computed as 4256kN.

The factored OALL computed by the consultant was based on 17kPa with a 60%

carry down factor. The OALL of 17kPa was based on the project basis of design

(BOD) defined by the owner and operator of the platform. The 60% reduction

factor was based on Clause C.2.8 in the API RP2A-LRFD (1993) which allows a

reduction in the internal forces but only if operating practices provide adequate

safeguards to prevent loads from exceeding the reduced values. The 60% reduction

factor was not accompanied by measures to ensure that such load would not be

exceeded. Hence, the application of 60% reduction was questionable but was

nevertheless implemented by the consultant who subjectively considered that the use

of 17kPa was too conservative.

The OALL was computed using the tributary areas for the most heavily loaded pile.

The resulting factored OALL on one pile was 1300kN, which was obtained as the

product of OALL (17kPa) by the tributary area (85m2) and using 60% carry down

factor and a load factor of 1.5.

The reaction from the new equipment was not equally distributed to all piles due to

its position on the deck. The layout required the new equipment to be located off-

center and towards the edge of the platform. The most heavily loaded pile

supported an additional load of 3365kN. Using a load factor of 1.3, the factored

equipment weight transmitted to the pile is 4375kN. Hence, the consultant



estimated a total load on the most heavily loaded pile of 11454kN.

The consultant computed the pile capacity using API RP2A-LRFD (1993) on the

basis of 50m penetration. The consultant did not investigate the pile driving records

to reflect the actual installation conditions. The consultant produced the capacity

chart presented in Figure 7-1. For 50m penetration, the capacity chart read an

ultimate compression pile capacity of 12589kN using the API RP2A-LRFD (1993)

design method. The ultimate capacity of the piles was factored using a capacity

reduction factor of 0.7 in accordance with API RP2A-LRFD (1993)

recommendation, resulting in a factored capacity of 8812kN.

The consultant compared the factored pile capacity (8812kN) with the factored load

effect (11454 – 4375 = 7079kN) on that pile without the additional piece of

equipment and concluded that the piles were safe in the inplace condition without

the addition of any more loads. To add a piece of equipment weighing 800 tonnes

(8000kN), the consultant estimated an increase in the pile load effect of 3365kN and

applied a load factor of 1.30 resulting in factored load effect of 4375kN on the most

heavily loaded pile. Since the combined factored load effect (11454kN) exceeded

the factored capacity (8812kN), the consultant concluded that the piles would be

overstressed and recommended construction intervention.

Using the outcome of this research, the analysis was repeated as described in Table

7-4. OALL was recomputed employing the findings described in Section 5.13. The

material handling equipment on the platform included a jib crane with SWL of 10

tonnes on the upper deck and a monorail with SWL of 5 tonnes on lower decks.

Using Equation 5-18, the OALL was computed as 4.28kPa and 8.28kPa on the

lower and upper decks, respectively. Hence, the total unfactored live load effects

were computed as 504kN (4.28kPa*50m2+ 8.28kPa*35m2=214kN+290kN).

Applying a load factor of 1.5 to the live loads in accordance with API RP2A-LRFD

(1993) recommendations produced live load of 756kN.

Using the findings of this research, the back-analysis procedure was applied. The

pile driving records (PDR) and soil reports were used to back-calculate the pile

capacity. The PDR showed final blow counts of 37, 30, 32 and 49. The blow

counts corresponded to capacities of 16722kN, 14647kN, 15769kN and 17135kN

for the four piles respectively. The pile in question was the most heavily loaded

pile, yet it had the lowest capacity (14647kN).



The calibrated capacity factors derived in this research shown in Table 4-21 were

employed in these calculations. The applicable capacity reduction factor of 0.82

was used. Hence, the factored pile capacity was computed as 12010kN as shown in

Table 7-5.

Using the results of this research, Table 7-6 shows that the factored capacity

exceeded the factored load effect on the most heavily loaded pile. Consequently,

there was reserve capacity in the foundation system to accommodate the required

additional equipment (3365kN) load with no need for construction intervention.

7.5. SUMMARY

The value of this research was demonstrated using a real life situation showing that

considerable (US$35 million – 2005 prices) savings could have been achieved had

the results of this research been available. The results of this research would have

led to the conclusion that the capacity of an existing offshore platform was adequate

for the additional loading and that a new platform was not required.



Table 7-1: Description of the soil stratigraphy

Depth (m) Layer #

From To Generic Description

I 0.00 1.00 CAPROCK

II 1.00 9.00 Silty carbonate SAND

III 9.00 21.00 Very weak CALCARENITE

IV 21.00 49.00 Very stiff calcareous CLAY

V 49.00 50.00 Crystalline GYPSUM

VI 50.00 54.50 Hard Silty calcareous CLAY

VII 54.50 61.00 Medium dense silica SAND

VIII 61.00 79.00 Dense carbonate SILT

IX 79.00 80.80 Dense carbonate SAND



Table 7-2: Description of the gravity loads assumed in the computer model by the engineering consultant

Designation Description Load Effect (kN)

DL SACS Generated load: self weight + marine growth + buoyancy of jacket + topside weights

5280

DL Non-generated dead loads for Jacket: submerged weight of anodes + conductor guides + upending and lifting trunnions/ padeye + submerged weight of mudmat plates and sections + crown shim plate + pile spacers + grating and hand railing on boat landing + riser clamps

842

DL Non-generated dead loads on Topside: hatch covers + grating + handrail + stairway + Christmas tree platform

904

DL Riser dead load (1-12”+3-6”+4-10”+J-Tube) 464

DL Crane dead load 147

DL Equipment dry weight: instrument air receiver + survival craft + test separator + drain vessel + safety items + solar panel + battery + remote terminal units + cables + shutdown panel + miscellaneous E&I items + hydraulic power unit

1034

DL Piping dry weight: present and future 642

OPER Equipment operating content weight: present and future

245

OPER Piping operating content weight: present + future 14

LL OALL @ 17kPa * 60% pile rundown @ EL +12.5 2179

LL OALL @ 17kPa * 60% pile rundown @ EL +17.8 1342

TOTAL UNFACTORED LOADS ON ALL PILES 13096



Table 7-3: Environmental data used in the analysis of the operating condition

Parameter Assumption

Wave Theory Stokes 5th

Maximum Wave Height (one year return period) 7.3m

Wind speed (1 year return period for 1-hour average) 13.9 m/s

Wave Period 8.2 seconds

Wave Length 117.2m

Angle from X Towards Y 0.0 Degrees

Mudline Elevation -40.8m

Wave Celerity 13.6 m/s

Unmodified Wave Period 7.9 seconds

Admiralty chart datum 40.8m

L.A.T 0.15m

Astronomical tide 1.85m

Storm tide 1.3m

Total water depth for operating overload 44.1m

Crest Position determined by Maximum Moment

Starting Crest Position -12.00

Number of Steps 18

Step Size 1.0m

Crest Water Depth 46.8m

Trough Water Depth 41.7m



Table 7-4: Analysis of the pile loads for the case study. The results show the factored loads for the most heavily loaded pile

Pile Load Description Units Without results

of this researchWith

results of research

A=Dead load*1.3 kN 3274*1.3=4256

B=OALL*1.5 kN 887*1.5=1300 504*1.5=756

C=Environmental*1.35 kN 1128*1.35=1523

D=Additional Equipment on pile*1.3 kN 3365*1.3=4375

F=Total factored pile load (A+B+C+D) kN 11454 10910



Table 7-5: Analysis of the pile capacity for the case study. The results show the factored capacity for the most heavily loaded pile. The design drawings showed one pile assembly covering all piles. Thus, the theoretical capacity, using API RP2A-LRFD (1993), of all piles is the same. However, pile driving records showed different penetrations for the various piles

PILE Without results of

this research

With results of research

G=Design Pile Penetration m 50.0 53.5

H=Blow count (blows per foot) at tip Bpf - 30

I=Pile Capacity kN 12589 14647

J=Capacity Reduction Factor φ 0.70 0.82

K=Factored Capacity Piles (I*J) kN 8812 12010



Table 7-6: A comparison of the factored load effect on the pile against the factored capacity. The results show that using subjective parameters for the load effect and the capacity concluded that the pile capacity would be exceeded when subjected to an increase in load effect. Using the results of this research, this pile could have been justified to support the load and therefore saving USD 35 million which was the cost of construction of a new platform

PILE Units Without results of

this research

With results of research

F=Total factored pile load (A+B+C+D) kN 11454 10910

K=Factored Capacity Piles (I*J) kN 8812 12010

L=Reserve capacity in pile (K-F) kN -2642 +1100



Figure 7-1: Results of the geotechnical analysis carried out by the consultant to calculate the capacity of piled foundations using API RP2A LRFD (1993). In deriving the ultimate axial capacity along the depth of the pile, the consultant used the capacity reduction factors as per API RP2A-LRFD (1993) but employed subjective limiting parameters to predict the pile capacity



Figure 7-2: An isometric view of the platform analyzed in this research



Figure 7-3: Pile assembly of the platform investigated in this research. The diagram is extracted from the pile drawing and shows pile diameters and penetration lengths in mm



Figure 7-4: Mathematical model showing the finite elements used to study the behavior of the structure in the case study

323


Chapter 8.

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

8.1. SUMMARY

This research develops guidelines for reassessment of existing offshore platforms

which are not covered in international codes and standards such as Section ‘R’ in


Section ‘R’ addresses extreme storm conditions only and is specific to the

conditions in the Gulf of Mexico and US waters. In these regions, extreme storm

conditions dominate the failure mechanism. The Arabian Gulf environment is much

more benign compared to that in the Gulf of Mexico and US waters. Consequently,

there is a need to assess the applicability of Section ‘R’ when conducting

reassessment of existing offshore platforms in the Arabian Gulf.

Further, Section ‘R’ lacks guidance on reassessment of axial pile capacity driven in

carbonate soils. This is because the behavior of piles driven in carbonate soils is

very different, and their capacity is much lower, than those driven in “normal” soils.

Since the Arabian Gulf soils are characterized by their high carbonate content, there

is a need to develop parameters to predict the axial capacity of piles in carbonate

soils.

Moreover, API RP2A-LRFD (1993) refers to ASCE Standard 7-05 to determine live

loads. However, live loads nominated in ASCE Standard 7-05 only cover building

structures and does not quantify live loads on offshore platforms. Therefore, one of

the objectives of this research is to provide a methodology to calculate live loads on

offshore platforms.

8.2. METHODOLOGY

This research follows similar approach to that used in the calibration of API RP2A-

CHAPTER 8: SUMMARY, CONCLUSIONS and RECOMMENDATIONS 324


LRFD (1993) and borrows from decades of code development experience.

However, this research employs a database, which is collected in this research and is

pertinent to the conditions of the Arabian Gulf.

The output from this research is a set of specifications that address the following

topics:

• Prediction of axial capacity of piles driven in carbonated soils,

• Calculation of live loads on open areas of offshore platforms, which can be used

to develop load effect on piles, and

• Determination of dominant failure mechanism in the Arabian Gulf.

8.2.1. OPEN AREA LIVE LOAD (OALL)

Development of OALL is based on deriving lifetime maximum loads, which is

equivalent to EUDL adopted in building codes and standards such as ASCE

Standard 7-05.

The derivation of OALL for offshore platforms in the Arabian Gulf utilizes

statistical parameters of equipment weights. A suitable probabilistic model is

identified in this research and applied to the statistical parameters to calculate the

mean lifetime maximum axial load on a pile. The probabilistic model utilizes the

influence surface method, which is consistent with the approach used by ANSI A58

(now called ASCE Standard 7-05) to derive maximum load effect on a pile due to

the random nature of the loads. An extreme value analysis is then applied to the

statistical parameters of the live load effect to produce the mean of the lifetime

maximum load effect.

8.2.2. AXIAL PILE CAPACITY IN CARBONATE SOILS

Calibration of resistance factors for axial pile capacity in carbonate soils is

conducted in this research using a database of offshore piles installed in the Arabian

Gulf. The database comprised installation records of 138 offshore piles, which were

installed between 1960 and 2003. The installation records include pile driving

records, soil profile and pile drawings for each pile.

Calibration of axial pile capacity is conducted using bias factors of axial pile

capacities. The bias factor for each pile is computed by dividing actual pile capacity



over its predicted capacity.

The predicted capacity of each pile is derived in this research using API RP2A-

LRFD (1993) formulation, except that limiting soil parameters identified in Table

4-6 are used in lieu of the specified values in API RP2A-LRFD (1993) in order to

model conditions in the Arabian Gulf. The prediction of axial pile capacities using

API RP2A-LRFD (1993) is tedious, with a higher likelihood of errors when hand

calculations are performed. To overcome potential errors, an Excel spreadsheet

coded APIPILE is developed in this research. The operating manual for APIPILE is

described in Appendix I.

To derive “actual” pile capacities, an approach termed back-analysis is

implemented. The back analysis approach is developed in this research and

employs installation records and time effect to calculate the “actual” pile capacity.

The installation records are analyzed using GRLWEAP to produce short term pile

capacity. To model the effect of time at the end of driving (EOD) and provide the

actual long term capacity, setup factor is applied to the short term capacity. The

back-analysis method results are validated using PDA and CAPWAP results, which

were obtained from actual offshore installation in the Arabian Gulf.

The computed bias factors are partitioned in this research to reduce the error in the

statistical parameters. The partitioned bias factors reflect installation methods, soil

profile (cohesive soils overlain by cohesionless soils or vice versa), and level of

conservatism in the original design.

Calibration of axial pile capacity in carbonate soils requires calculation of statistical

parameters of the bias factors and determination of target reliability levels. The

statistical parameters are derived using the parametric approach described in Section

3.4. The selection of target reliability levels is based on survey of literature and also

accounts for the target reliability level employed in calibrating axial pile capacity in


8.2.3. DOMINANT FAILURE MECHANISM

Reliability analysis is applied to a specific platform in this research to determine the

dominant failure mechanism in the Arabian Gulf. The reliability analysis is

performed for extreme storm and operating overload conditions to calculate

probability of failure under each failure condition. The condition that corresponds



to the highest probability of failure is defined as the dominant failure mechanism,

and the corresponding parameters for such dominant failure mechanism would then

be required to formulate reassessment guidelines.

To perform reliability analysis, a pushover analysis has been employed and the

resulting reserve strength ratio (RSR) is used in a First Order Reliability Method

(FORM) to derive reliability index, which is then used to compute probability of

failure. Pushover analysis is conducted using COLLAPSE module in SACS

software. The mathematical model employed DNV guidelines to model the

structure and pile-soil interaction. The statistical parameters for the loads and

resistance are derived in this research for the conditions of the Arabian Gulf.

8.3. CONCLUSIONS

A number of conclusions are derived in the course of this research and are presented

in this section. The value of this research is demonstrated using a case research

from a real life situation, showing that a saving of about US$35 million (2005

prices) could have been achieved if the results of this research were available at the

time of executing that project.

8.3.1. DOMINANT FAILURE MECHANISM IN THE ARABIAN

GULF

This research reveals that, unlike in the Gulf of Mexico and the North Sea where

extreme storm conditions dominate the failure mechanism, the failure mechanism in

the Arabian Gulf is dominated by operating overload conditions.

This research identifies a platform to examine its dominant failure mechanism. The

platform is selected such that the conclusion from the reliability analysis is

applicable to other platforms in the Arabian Gulf. The calculated probability of

failure of this platform under extreme storm conditions is infinitesimally (10-71)

small. On the other hand, the probability of failure under operating overload is

much higher (10-6).

The dominance of operating overload conditions is attributed to the combined effect

of benign environment, large open deck areas, water depth and the low axial

capacity of piled foundations in carbonate soils in the Arabian Gulf.



The benign environment in the Arabian Gulf reflects the low (6%) COV of the base

shear calculated in Section 6.7.4. The required guidelines for reassessment of

existing platforms in the Arabian Gulf focus on operating overload conditions. An

outline of such procedure is shown in Figure 8-1.

8.3.2. APPLICABILITY OF SECTION ‘R’ TO THE ARABIAN

GULF CONDITIONS

Given that operating overload conditions dominate the failure mechanism in the

Arabian Gulf, this research concludes that guidelines contained in Section ‘R’ of

API RP2A-LRFD (1993) are inapplicable to the Arabian Gulf conditions. Section

‘R’ addresses extreme storm conditions only when performing reassessment of

existing offshore platforms on the basis that extreme storm conditions dominate the

failure mechanism. Section ‘R’ lacks guidance on axial pile capacity and open area

live loads, which are two primary requirements needed for reassessment of existing

offshore platforms in the Arabian Gulf.

8.3.3. SPECIFICATIONS FOR OALL

The Author’s experience reveals that industry practice employs subjective values for

OALL ranging from 2.5kPa to 17kPa when performing reassessment of existing

platforms. The use of an arbitrary value for the live load is likely to result in either

unsafe or uneconomic assessment.

The need to quantify OALL is actually applicable to all platforms around the world

but is critical for the rational reassessment of existing platforms in the Arabian Gulf

due to the dominant nature of the operating overload on the failure mechanism.

This is not the case in other parts of the world, such as the Gulf of Mexico, where

extreme storm conditions dominate the failure mechanism. In those regions, API

RP2A-LRFD (1993) does not require live loads to be combined with extreme storm

conditions when reassessment of existing platforms is carried out.

This research reveals that the magnitude of OALL is affected by various parameters

including:

• Platform size,

• Platform expected life span,



• Location of the deck on the platform (upper deck, other decks),

• The selected influence surface (pile, primary beams, secondary beams or topside

columns),

• The safe working load (SWL) or capacity of the crane or monorail used to

handle equipment on a deck, and

• The separation distance between equipment on a deck.

The research examines the sensitivity of each parameter on the magnitude of OALL

and concludes that the magnitude of OALL effect on piles is dominated by SWL of

the crane or monorail on a deck if the distance between equipment on that deck

exceeds 3m. For situations when the separation distance between equipment pieces

is less than 3m, this research point to the dominance of the separation distance in

determining the magnitude of OALL effect on piles.

Figure 8-2 provides a methodology that can be used to determine OALL effect on

piles.

The use of the approach recommended in this research achieves significant cost

savings in the reassessment of existing platforms in the Arabian Gulf. These

savings can be brought about by either minimizing risk or by avoiding unnecessary

remedial work to existing piled foundations. Use of a rational OALL may also

provide development opportunities by allowing the addition of loads on existing

facilities without the need for construction intervention.

8.3.4. LIMITING ENGINEERING PARAMETERS OF

CARBONATE SOILS

International codes and standards, including API RP2A-LRFD (1993), do not

provide limiting values for pile end bearing and friction for carbonate soils. This

research reveals two issues associated with the selection of limiting engineering

parameters for carbonate soils.

Firstly, the limiting soil parameters for carbonate soils are site specific and currently

depend on experience. A survey of the literature revealed that limiting parameters

for carbonate soils have been suggested for various parts of the world such as

Australia and India, and are predominantly based on experience in these regions.

The literature lacks guidance on limiting parameters in the Arabian Gulf.



Previous test results are sometimes used to evaluate limiting engineering parameters

but these are even more problematic to apply in practice due to the wide range of

recommended limiting values for the skin friction and the end bearing from those

test results as described in Section 2.7.4. This wide range is understood to be a

function of the geographic locations, but could also be a result of the loading test

method, interpretation method and the soil stratification. Further, researchers warn

against extrapolating their site-specific research findings to other parts of the world.

Secondly, specifying limiting parameters for carbonate soils is not independent of

resistance factors. Survey of the literature reveals that the suggested limiting

parameters by various researchers are derived independently with no relation to the

capacity reduction factors. However, this research shows that the capacity reduction

factors are heavily dependent on the selected limiting engineering parameters, as

these are used to predict axial pile capacity, which is used to calculate bias factors

which are in turn used to calibrate resistance factors.

Therefore, a study of the limiting parameters is not independent of capacity

prediction equations. Hence, this research selects a set of limiting soil parameters

which are representative of carbonate soils and focuses on calibrating resistance

factors for the selected limiting parameters.

The limiting soil parameters adopted in this research are taken from the study

reported by Lacasse and Goulois (1989), which were derived by averaging opinion

of experts in the field. The limiting parameters are shown in Table 4-5.

8.3.5. BIAS FACTORS OF AXIAL PILE CAPACITY IN THE

ARABIAN GULF

Calibration of API RP2A-LRFD (1993) initially considered a large (1004 load test)

database, with a large (0.786) bias and substantial (68%) error in the prediction

model. After screening out low quality load test data and those in carbonate soils,

Tang (1988) selected 44 pile load tests. For the 44 load tests, the prediction was

improved (1.007, 46%) and used to calibrate API RP2A-LRFD (1993).

The statistical parameters developed in this research for carbonate soils (0.93, 36%)

compares reasonably well with those (1.007, 46%) reported by Tang (1988) in non-

carbonate soils. Further reduction in the bias and error in the prediction model is



achieved by subdividing the database into groups with similar characteristics. In

this research, the database (138 piles) is sub-grouped to reflect physical differences

in the database, resulting in a reduction (20%-26%) in the error and an improvement

to the prediction model.

The probability distribution of the bias factors for carbonate soils shows a normal

distribution, which is consistent with findings by other researchers (Efthymiou et al.,

1996 and Tang, 1988).

8.3.6. TARGET RELIABILITY LEVELS

Target reliability level represents the implied risk level for a platform. Despite the

vast amount of studies and literature, there is no consensus on target reliability

values that could be directly used in calibration of existing codes and standards.

Target reliability levels implicit in codes and standards range from 1.75 to 4.3 as

shown in Table 2-1. Yet, the calibration of resistance factors in API RP2A-LRFD

(1993) employed a single target level (β = 2.11).

This research shows that the resistance factors of piled foundations are heavily

influenced by the selection of target reliability levels. Consequently, it is crucial to

select appropriate target levels to ensure acceptable risk level for reassessment. In

this research, calibration of resistance factors for the axial capacity of piles

employed a range of target reliability values as shown in Table 4-21.

8.3.7. SPECIFICATIONS FOR AXIAL PILE CAPACITY IN

CARBONATE SOILS

This section defines a procedure to predict axial capacity of piled foundations in

carbonate soils. The procedure is mapped out in Figure 8-3.

The first step in reassessment of an existing platform piled foundation is to collect

available data from the list identified below:

• Pile configuration and splice schedule from engineering drawings,

• Pile driving records,

• Pile penetration resistance versus penetration depth,

• Installation delay records due to welding add-on and hammer and cushion

changes,



• Details of hammers used in the operation,

• Details of the pile and follower make-up,

• Geotechnical information obtained for the site from soil consultant’s report,

which includes shear strength profile, soil reports identifying engineering

parameters to be used in the prediction of the static pile capacity, continuous

hammer steam pressure records and soil plug field log of soil exploration and

pile installation activities, and

• Pile Driving Analyzer (PDA) and CAPWAP analysis reports.

The application of the procedure depends on the available data. If available

installation records include PDA or CAPWAP results, the pile capacity can

generally be estimated without further work by directly reading results from the

dynamic measurement and CAPWAP report but applying setup factors as described

in section 4.5.4 to compute the long term capacity.

In practice, and especially for older platforms, dynamic pile monitoring was not

performed. However, it is not uncommon to find pile driving records with soil

profile data. In such case, the back-analysis method developed in this research and

described in Section 4.5 can be applied.

To apply the back-analysis procedure, the following procedure is recommended:

• Sketch a soil profile and pile diagram as shown in Figure 4-1 and classify soil

type in general terms to be either sand or clay.

• Assign dynamic soil parameters to each soil layer using the recommended

values in Table 4-9 for the Arabian Gulf. The use of site-specific dynamic soil

parameters is recommended, but these are typically not available, hence the need

for the generic values identified in Table 4-9.

• Model pile cross sections and the actual soil profile accurately. However,

accuracy must be weighed against robustness. The findings of this research

show that dividing the soil every 1.0m produces reliable results.

• Perform one-way wave equation analysis (WEA) using GRLWEAP. The input

data of the pile and hammer parameters may adopt the default values in the

software.

• The result of the WEA provides an estimate of the capacity at the end of driving,

also referred to as short term capacity.

• To incorporate time effects, use a setup factor of 2 as presented in Section 4.5.4.



• Calculate long term capacity of the pile by multiplying the setup factor by the

short term capacity calculated above.

In the absence of pile driving records, the capacity can be derived using the soil

profile and the pile cross section and applying the engineering limiting values

included in Table 4-5 to API RP2A-LRFD (1993) formulation. The calculated

capacity represents the ultimate capacity and must be factored using one of the

resistance factors developed in this research and shown in Table 4-21. The use of

Table 4-21 requires a qualitative assessment of the soil type, a determination of the

level of conservatism in the design and a description of the installation method. The

capacity reduction factors are much lower for piles installed using supplementary

installation procedures (such as air or water jetting, drilling and grouting).

If soil profile data are not available, there may be a need for an offshore campaign to

identify the soil profile and provide the engineer with the required engineering

parameters for the reassessment. However, such a geotechnical campaign should

only be specified as a last resort and only when absolutely necessary. The cost of an

offshore geotechnical campaign in the Arabian Gulf was around USD500, 000

(2005 prices).

8.3.8. MODIFICATION TO DETERMINISTIC METHOD FOR

REASSESSMENT

Current practice of using the design level method to perform reassessment of driven

piles in existing platforms does not account for data accumulated during installation,

which would not normally be known at the time of design. For example, the

availability of pile driving records through installation records make the design level

inapplicable as it inherently assumes lack of such data. Further, current

deterministic methods do not consider risk levels in the assigned load and resistance

factors.

This research provides a set of guidelines that recognize that reassessment is

fundamentally different from and requires knowledge beyond the scope of design

codes. The guidelines allow for availability of installation records and consider the

various risk levels associated with the different platform types and functions.

Calibration of the resistance factor is a function of the bias factor statistics and target



reliability levels. Consequently, the use of a single resistance factor of 0.7, as per

API RP2A-LRFD (1993), implies different safety levels for various platforms. To

produce consistent safety levels, a higher value for the target level should be used

with structures of high importance such as manned compression platforms or those

processing sour gas. On the other hand, lower target values may be used for

unmanned satellite platforms with no storage facilities. This approach would

harmonize and rationalize codes such that structures with similar functions and

similar manning levels have comparable safety levels. Such approach would

overcome one of the drawbacks associated with the use of deterministic methods,

typically used in design applications, as discussed in Section 2.5.

Hence, this research adopts a range (1.5 to 3.5) of target reliability levels and

calibrates corresponding resistance factors using First Order Reliability Method

(FORM).

The outcome of this research reveals that the calibrated resistance factors range from

0.38 to 1.00 and depend on the statistics of bias factors and the reliability index (risk

level). For example, the capacity reduction factor of a pile that is installed using a

supplementary installation method (drilling) is 0.38 if that pile supports a platform

with high consequences of failure (βT = 3.5) while a capacity reduction factor of

0.95 may be used if the pile is driven with no supplementary installation methods

and if it supports a platform with low consequences of failure (βT = 1.5).

8.3.9. LIMITATIONS OF API RP2A PREDICTION MODEL

The API RP2A-LRFD (1993) model used to predict axial capacity of piles requires

nominating a wall thickness. In practice, it is not uncommon for an offshore pile to

have a variable cross section along its length. Consequently, there is a question of

which wall thickness to use in the model.

This research reveals that, for a pile composed of many cross sections, the use of an

average wall thickness to model such pile poorly predicts its axial pile capacity.

This research also reveals that the API RP2A-LRFD (1993) model poorly predicts

the axial pile capacity for driven piles that are installed using supplementary

installation methods (such as drilling).

Therefore, for pile configuration with considerable variation in cross section along

its length, or for piles driven in carbonate soils using supplementary methods, the



API RP2A-LRFD (1993) prediction model is inapplicable as it produces poor

results. In such cases, the Author recommends the use of the back-analysis

procedure, which has been shown in this research to produce reliable results.

8.3.10. MODELING PILE-SOIL INTERACTION

For the Gulf of Mexico and the North Sea platforms, modeling of piled foundations

has usually been ignored when conducting structural pushover analyses due to the

large bias in the design formulation of piled foundations in non-carbonate soils

(Bea, 1983; Edwards et al., 1984). However, disregarding pile modeling of offshore

platforms in the Arabian Gulf is questionable due to lack of consensus on

established limiting soil parameters for carbonate soils. This research provides a

mechanism to make a decision on whether foundation modeling can be ignored.

The application of the back-analysis procedure developed in this research provides

the means to determine the degree of bias in a piled foundation in carbonate soils.

Without ensuring that the degree of conservatism is sufficiently high, foundations

must be included in the analysis of offshore platforms in the Arabian Gulf.

8.4. RECOMMENDATIONS FOR FUTURE RESEARCH

During the course of this research, a number of issues were identified for future

research. These are classified into technical and philosophical issues.

8.4.1. TECHNICAL ISSUES

A number of technical issues have been identified during the course of this research,

which would complement the developments in this research to enable a generic set

of reassessment specifications for all geographic regions.

This research adds to the various findings on limiting values for piles

installed in carbonate soils, and is in line with the general approach that

recommends developing limiting values for each geographical location

(Kolk, 1999). However, there is a need for a constitutive model for

carbonate soils, enabling modeling of large geological units which are

detached from the assumptions of extrapolations based on small diameter

sampling and testing.

Significant research has been conducted to understand the behavior of piled



foundations in carbonate soils. Despite the vast amount of literature and

expertise in the field, there is lack of an accepted method in the industry.

There is a need to consolidate recent knowledge on the subject with an

objective of gaining consensus on an acceptable design method that can be

used in industry practice.

Future projects that encounter carbonate soils should be undertaken with a

view to extending the knowledge. Areas where current knowledge is lacking

or where work could be undertaken include non-standard laboratory and

small scale in situ pile tests. Future research is required to assess the effects

of scale, develop better tests and standardize more effective procedures for

various types of model pile tests to enable correlation with prototype full-

scale tests.

There is a need to collect additional pile driving records to increase the

sample size and refine the calibrated resistance factors shown in this

research. Additionally, there is a need to promote more instrumentation and

monitoring of existing platforms such that reliable pile data can be collected.

This would also enable the development of bias factors for the toe and skin

separately and hence the calibration of resistance factors for skin and toe

instead of applying a single resistance factor for the total capacity.

Current API RP2A static prediction formulation does not include the effect

of wall thickness. However, during the course of this research, the one-way

WEA demonstrated that the calculated capacity of a pile with various wall

thicknesses along the pile length might be very different from the calculated

capacity using average wall thickness. Hence, there is a need to examine the

API RP2A formulation to verify and include the effect of using various wall

thicknesses in a pile cross section when calculating the pile capacity.

Current practice for predicting axial capacity of piles does not differentiate

between soil types when API RP2A-LRFD (1993) formulation is used to

predict axial pile capacity. However, Section 4.7.1 reveals that piles in clay

have a higher reliability than piles in sand. This discrepancy in apparent

reliability justifies the later consideration of separate resistance factors for

piles in sand and clay. Additionally, since piles in stiff, over consolidated

clay do not show the same loading rate effect as those in soft clay, they too



may require a separate resistance factor.

There is a need to fully understand the effects of long term setup through an

analytical approach and the time duration required to achieve a specific

setup. Current reassessment methods of such effects can only consider short

term setup, which may be overconservative. Setup effect – normally taken at

the beginning of restrike – could be different to the long term setup.

This research employed @RISK functions to select best fit distributions to

the data. There is a need to repeat the work using different distributions to

the data to determine the effects of alternative distributions on the computed

parameters.

There is also a need to provide standard specifications for drilling and

jetting. The procedure should ideally be linked to a specified resistance

factor. If actual drilling or jetting conditions deviate from such procedures,

lower bound resistance factors should then be used to ensure reliable but

economic foundations. This would also partly explain the scatter in bias

factors for the drilled and jetted piled foundations.

OALL in this research used equipment database from the Arabian Gulf

platforms. An investigation of the statistical parameters in other geographic

parts of the world is an area for future research.

An increase in the probability of failure can also take place under structural

deteriorating conditions. Future research should be conducted to extend the

present work to model deterioration of capacities, time-dependent reliability

and reliability-based inspection and maintenance.

Further research is also needed to model structural deterioration due to boat

impact in conjunction with operational overload conditions.

8.4.2. PHILOSOPHICAL ISSUES

Related to the above technical issues there are a number of philosophical issues that

also need to be addressed within the context of a more consistent application of

system reliability techniques for offshore structures.

An important question associated with reliability assessments is the setting

of target safety levels. This question needs to be addressed in parallel with



other technical developments if these methods are to be widely applied to

demonstrate the safety of new and existing structures. The historical

performance of existing structures and the application of the ALARP

principle provide an initial basis for setting such targets.

The integration of reliability methods into the design process will form an

important step towards the use of the methods in life cycle planning

optimization. The system reliability assessment should not be seen in

isolation, and it is important to be viewed within the context of an overall

hazard management strategy. An improved understanding of how it

interfaces with other hazard management systems is an important

development in this direction. This will enable a more rational use of the

methods within the overall strategy for design and life cycle planning of a

fixed platform.

Lack of sufficient test data for carbonate soils should be accounted for in

future projects, and a combined effort by operators would enable creation of

such database. There is no added cost to operators to contribute to such

database. The specification of dynamic monitoring is common practice in

many recent projects to avoid contractual issues. Hence, an international

effort to provide results of dynamic monitoring would produce an excellent

database for future research. The availability of such a database will enable

better understanding of piles driven in carbonate sediments. This approach

is consistent with recommendations made by Terzaghi (1927) who states that

“Foundation problems are of such character that a strictly theoretical

mathematical treatment will always be impossible. The only way to handle

them efficiently consists of finding out, first, what has happened on

preceding jobs of a similar character; next, the kind of soil on which the

operations were performed; and finally, why the operations have led to

certain results. By systematically accumulating such knowledge, the

empirical data being well defined by the results of adequate soil

investigations; foundation engineering could be developed into a semi-

empirical science”. Figure 8-4 presents a methodology to guide such future

database.

This research work has identified negligible risks related to environmental



overload and that operating overload conditions are the dominant failure

mechanism in the Arabian Gulf. There are potentially significant gains from

future improvements of operational practices in the Arabian Gulf. Unlike

natural hazards that can not be controlled, operational loading can be

controlled by monitoring and management strategies. The likelihood of

overload condition in such case would result from human behavior and

failure to follow and rules. Failure of human behavior to follow rules and in

such cases is an area for future studies.

While extreme storm overload conditions are caused by natural events that

can not be controlled, this research shows that operational overload

conditions dominate the failure mechanism in the Arabian Gulf. A root

cause of such failure mechanism is in human and organization factor (HOF).

Hence, there is a need to study HOF causing operational overload.

The American Petroleum Institute has formed a Committee to oversee the

transition between API RP2A and ISO Standard for the design of fixed

structures. The latter will be the path forward for future designs. Future

research should include this work as a starting point for a Regional Annex

for the Arabian Gulf where it could be made widely available to practicing

engineers.



Figure 8-1: Flowchart showing an outline of the specifications that can be used for reassessment of existing offshore platforms in the Arabian Gulf under operating overload conditions



Figure 8-2: Flowchart showing a proposed method developed in this research to calculate OALL



PDA/CAPWAP available?

Apply setup factor as ratio BOR / EOD capacity and calculate ultimate axial pile capacity

Is PDR available?

Perform back-analysis procedure using GRLWEAP with hammer, soil and pile parameters using procedure identified in Section 4.5

NO

Soil data available

Use API RP2A-LRFD (1993) procedure with the limiting soil parameters defined in Table 4-5 and the capacity reduction factors In Table 4-21

Obtain soil borehole data from platform site

NO

NO

YES

YES

YES

Collect available installation data:Installation records: PDR/ PDA/ CAPWAPSoil borehole dataMean values of the soil parameters

STOP

STOP

STOP

Figure 8-3: Flowchart showing a proposed method developed in this research to predict axial capacity of piles driven in carbonate soils in the Arabian Gulf



Figure 8-4: A proposed flowchart to identify the requirement and steps required to collect a future database for pile capacities

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AUTHOR’S PUBLICATIONS

These conference papers have been published to address topics relevant to this

research:

Zaghloul H; Ronalds, B, 2004, “The Use of Implicit Safety Factors in the

Reassessment of Offshore Platforms in the Arabian Gulf”, Proceedings of 23rd

International Conference on Offshore Mechanics and Arctic Engineering, Paper

51529, Vancouver, Canada

Zaghloul H; Ronalds, B; Cole, G, 2005, “Probabilistic Analysis of Live Loads on

Offshore Platforms”, Proceedings of 24th International Conference on Offshore

Mechanics and Arctic Engineering, Paper 67266, Halkidiki, Greece.

Zaghloul H; Ronalds, B; Cole, G, 2005, “Development of Piled Foundation Bias

Factors in the Arabian Gulf”, Proceedings of 24th International Conference on

Offshore Mechanics and Arctic Engineering, Paper 67269, Halkidiki, Greece.

Zaghloul, H; Imms, K, 2004, “The Use of New Code to Qualify Existing Structures

– A Case Study”, Proceedings of the Second International Conference on Structural

Engineering, Mechanics and Computations, South Africa

APPENDICES

Appendix A

EQUIPMENT DATABASE

Operating ElevationWeight Length Width Area

kN m m m2 mmGlycol Drain Pump 6.0 0.7 0.5 0.4 Sub CellarClosed Drain Drum 5.0 0.7 0.4 0.3 Sub CellarOpen Drain Drum 4.0 0.7 0.4 0.3 Sub CellarHot Oil Drain Pump 6.0 0.7 0.4 0.3 SubcellarClosed Drain Pump 6.0 0.7 0.4 0.3 Sub CellarClosed Drain Pump 6.0 0.7 0.4 0.3 Sub CellarOpen Drain Pump 3.0 0.3 0.3 0.1 Sub CellarOpen Drain Pump 3.0 0.3 0.3 0.1 Sub CellarOpen Drain Vessel 75.0 1.6 1.6 2.6 92546Closed Drain Vessel 75.0 1.6 1.6 2.6 92546Open Drain Pump 4.0 1.0 1.0 1.0 92546Closed Drain Pump 4.0 1.0 1.0 1.0 92546Closed Drain Vessel 75.0 2.5 2.5 6.3 93460Closed Drain Pump 4.0 1.0 1.0 1.0 93460Solid Potable Water Filter 25.0 0.5 0.5 0.3 95725Carbon Potable Water Filter 25.0 0.7 0.7 0.5 95725Open Drain Vessel 75.0 1.4 1.4 2.0 94304Closed Drain Vessel 75.0 1.4 1.4 2.0 94304Open Drain Pump 4.0 0.8 0.8 0.6 94304Closed Drain Pump 4.0 0.8 0.8 0.6 94304Instrument Air Buffer Vessel 25.0 3.2 0.7 2.2 95545Instrument Air Buffer Vessel 25.0 3.2 0.7 2.2 95545Chemical Injection Pump Skid 50.0 2.0 0.8 1.6 96838Central Open Drain Vessel 93.0 4.0 2.4 9.6 94222Central Closed Drain Vessel 93.0 4.0 2.4 9.6 94222Closed Drain Pump 6.0 1.0 1.0 1.0 94222Closed Drain Pump 6.0 1.0 1.0 1.0 94222Drain Oil Pump 6.0 1.0 1.0 1.0 94222Closed Drain Vessel 75.0 1.2 1.2 1.4 93460Closed Drain Pump 4.0 1.0 1.0 1.0 93460Open Drain Vessel 75.0 1.2 1.2 1.4 94100Closed Drain Vessel 75.0 1.2 1.2 1.4 94100Open Drain Pump 4.0 0.3 0.3 0.1 94100Closed Drain Pump 4.0 0.3 0.3 0.1 94100Open Drain Vessel 75.0 1.2 1.2 1.4 94527Closed Drain Vessel 75.0 1.2 1.2 1.4 94527Open Drain Pump 4.0 0.3 0.3 0.1 94527Closed Drain Pump 4.0 0.3 0.3 0.1 94527Open Drain Vessel 93.0 4.0 1.4 5.6 97100Closed Drain Vessel 93.0 4.0 1.4 5.6 97100Closed Drain Pump 6.0 0.7 0.4 0.3 97100Closed Drain Pump 6.0 0.7 0.4 0.3 97100Open Drain Oil Pump 6.0 0.7 0.4 0.3 97100Open Drain Vessel 75.0 1.2 1.2 1.4 93460Closed Drain Vessel 75.0 1.2 1.2 1.4 93460Open Drain Pump 4.0 0.3 0.3 0.1 93460Closed Drain Pump 4.0 0.3 0.3 0.1 93460Solid Potable Water Filter 25.0 0.5 0.5 0.3 96639Carbon Potable Water Filter 25.0 0.7 0.7 0.5 96639U.V. Steriliser 5.0 0.4 0.4 0.2 96639Hypochlorite generator Package 80.0 2.5 2.0 5.0 CellarFirewater Pump 95.0 5.5 2.5 13.8 CellarFirewater Jockey Pump 6.0 4.0 3.0 12.0 CellarFirewaterJockey Pump 6.0 4.0 3.0 12.0 CellarFuel Gas Super Heater 29.0 11.0 1.5 16.5 CellarHP Fuel Gas Scrubber 65.0 2.0 2.0 4.0 CellarInstrument Air Receiver 45.0 2.0 2.0 4.0 CellarGlycol Regeneration Package 350.0 9.0 4.5 40.5 Cellar1st Stage Liquid Recycle Heater 5.0 10.0 1.5 15.0 Cellar1st Stage Liquid Recycle Heater 5.0 10.0 1.5 15.0 Cellar

DescriptionEquipment Footprint


kN m m m2 mmDescription

Equipment Footprint

1st Stage Recycle Cooler 19.0 11.0 1.5 16.5 Cellar1st Stage Recycle Cooler 19.0 11.0 1.5 16.5 CellarArab 'C' Slug Catcher 1590.0 12.0 4.0 48.0 Cellar1st Stage Compressor Suction KO Drum 34.0 2.5 2.5 6.3 Cellar1st Stage Compressor Suction KO Drum 34.0 2.5 2.5 6.3 Cellar2nd Stage Comprerssor Suction KO Drum 211.0 2.5 2.5 6.3 Cellar2nd Stage Comprerssor Suction KO Drum 211.0 2.5 2.5 6.3 Cellar3rd Stage Comprerssor Suction KO Drum 105.0 2.0 2.0 4.0 Cellar3rd Stage Comprerssor Suction KO Drum 105.0 2.0 2.0 4.0 Cellar4th Stage Comprerssor Suction KO Drum 106.0 2.0 2.0 4.0 Cellar4th Stage Comprerssor Suction KO Drum 106.0 2.0 2.0 4.0 CellarGlycol Contactor 1110.0 3.0 3.0 9.0 CellarCondensate Flash Vessel 610.0 10.0 3.5 35.0 CellarCondensate Stabilizer 754.0 4.0 4.0 16.0 CellarCondensate/Condensate Exchanger 48.0 4.5 4.5 20.3 CellarStabilizer Reboiler 74.0 8.0 4.0 32.0 CellarStabilizer Reboiler 74.0 8.0 4.0 32.0 CellarStabilizer Side Exchanger 124.0 4.0 4.0 16.0 CellarCondensate Transfer Pumps 66.0 6.0 4.0 24.0 CellarCondensate Transfer Pumps 66.0 6.0 4.0 24.0 CellarTX Inlet K. O. Drum 258.0 7.0 2.0 14.0 CellarTX Outlet K. O. Drum 239.0 7.0 2.0 14.0 Cellar Flame Front Generator Package 12.0 7.0 2.0 14.0 CellarHP Flare KO Drum 120.0 3.0 10.0 30.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSea Water / Tempered Water Exchanger 61.0 10.0 2.0 20.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Lift Pumps 70.0 6.0 2.0 12.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarSeawater Filters 25.0 8.0 2.0 16.0 CellarTempered Water Circulation Pumps 46.0 8.0 2.5 20.0 CellarTempered Water Circulation Pumps 46.0 8.0 2.5 20.0 CellarTempered Water Circulation Pumps 46.0 7.0 2.0 14.0 CellarInstrument Air Dryer Set 50.0 0.8 0.8 0.6 100000Electric Heater 5.0 1.0 1.0 1.0 100000Air Blower 5.0 1.0 1.0 1.0 100000Prefilter 5.0 1.0 1.0 1.0 100000Drying Vessel 5.0 1.0 1.0 1.0 100000Instrument Air Receiver 33.0 1.3 1.3 1.7 100000Whittaker Escape Capsule 25.0 6.2 3.7 22.9 100000Whittaker Escape Capsule 25.0 6.2 3.7 22.9 100000Chillers for Air Conditioning Unit 56.0 1.0 1.0 1.0 100000After Cooler for Inst. Air Comp. 5.0 1.0 1.0 1.0 100000Start-up Generator/Diesel Engine 90.0 6.6 3.0 19.8 100000New Package Glycol Unit 597.0 15.5 5.0 77.5 100000Air Handling Unit 50.0 1.0 1.0 1.0 100000Instrument Air Compressors 108.0 6.0 3.6 21.6 100000Air Pump 5.0 1.0 1.0 1.0 100000Diesel Oil Supply Pumps 2.0 0.3 0.3 0.1 100000Instrument Air Intake Filter 12.0 2.5 0.9 2.3 100000Duplex Filter/ Coalescer Diesel 20.0 1.4 1.0 1.4 100000Fuel Tank for G/T Driven Elec.Power Generator 490.0 2.6 1.3 3.3 100000Instrument Air Receiver 38.0 5.0 1.9 9.5 100000Low Pressure Fuel Gas KO Drum 28.0 1.8 1.8 3.2 100000Instrument Air Buffer Tank 16.0 3.9 2.0 7.8 100000Switch Board 270.0 12.6 2.0 25.2 100000Switch Board 60.0 15.6 1.6 25.0 100000Transformer 63.0 3.1 3.1 9.6 100000



Equipment Footprint

Transformer 63.0 3.1 3.1 9.6 100000Fuel Gas Scrubber 6.0 0.4 0.4 0.1 100000Fire Water Pump 175.0 7.0 3.5 24.5 100000Manifold 1175.0 16.0 3.5 56.0 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Formation Water Disposal Pump 22.0 3.4 1.4 4.8 100000Surge Vessel Skid 93.0 3.5 2.5 8.8 100000XX HP Separator 597.0 8.9 2.0 17.8 100000XX HP Separator 597.0 8.9 2.0 17.8 100000HP/MP Test Separator 426.0 10.2 2.2 22.4 100000Panametric 15.0 2.4 0.7 1.7 100000Hydrocyclone Skid 216.0 4.5 4.5 20.3 100000Manifold 758.0 15.2 4.0 60.8 100000Manifold 469.0 7.2 4.0 28.8 100000Manifold 864.0 15.0 3.9 58.5 100000K-143 Injection Unit 75.0 2.4 1.9 4.5 100000Glycol Absorber 101.0 2.2 2.2 4.8 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Sea Water Temp. Water Exchanger 54.0 2.7 1.0 2.5 100000Fuel Gas Super Heater 13.4 5.9 0.6 3.5 100000Pipeline Gas Heater 15.0 5.2 0.3 1.6 100000Air Pump 2.0 0.1 0.1 0.0 100000Air Pump 2.0 0.1 0.1 0.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Sea Water Cooler Pump 139.0 2.8 1.8 5.0 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 66.0 4.5 2.8 12.6 100000Temp. Water Cir. Pump 2.0 1.0 1.0 1.0 100000S.W. Cooling Filter 150.0 0.8 0.8 0.6 100000Temp. Water Storage Tank 949.0 4.9 3.0 14.7 1000001st Stage KO Drum 160.0 2.0 2.0 4.0 1000003rd Stage KO Drum 162.0 2.3 2.3 5.3 1000004th Stage KO Drum 118.0 1.7 1.7 2.9 100000Pipeline Gas KO Drum 45.0 0.8 0.8 0.6 100000Gas Turbine KO Drum 27.0 0.7 0.7 0.5 100000Liquid Blow Down Drum 33.0 1.0 1.0 1.0 1000002nd Stage KO Drum 231.0 2.8 2.8 7.8 100000Corrosion Inhibitor Dosing Package 126.0 4.3 3.3 14.2 100000Corrosion Inhibitor Dosing Package 101.0 4.0 2.5 10.0 100000Corrosion Inhibitor Dosing Package 101.0 4.0 2.5 10.0 100000Chemical Injection Skid 78.5 3.8 3.7 14.1 100000Fire Water Pump Drive 175.0 7.0 3.6 25.2 100000Diesel Transfer Pump 4.5 1.0 1.0 1.0 100000Diesel Transfer Pump 4.5 1.0 1.0 1.0 100000Fire Water Pump 175.0 1.0 1.0 1.0 100000Diesel Oil Tank 446.0 7.0 4.0 28.0 100000Black Start Meg Injection Package 75.0 4.0 1.4 5.6 100000Instrument Air Buffer Vessel 26.0 4.5 1.8 8.1 100000Instrument Air Surge Vessel 25.0 1.2 1.2 1.4 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000Main Oil Line Pump 69.0 5.2 1.6 8.3 100000



Equipment Footprint

Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Formation Water Disposal Pump 51.0 4.2 1.6 6.7 100000Surge Vessel Transfer Pumps/ Skid 127.0 4.0 3.2 12.8 100000Scale Inhibitor Inection Skid 27.0 2.5 1.9 4.8 100000Intelligent Pig Launcher 25.0 13.8 0.7 9.7 100000Biocide Chemical Injection Skid Skid 27.0 2.0 1.9 3.8 100000Drain Vessel 75.0 2.0 2.0 4.0 100000Chemical Injection Skid 150.0 3.0 2.1 6.3 100000Reverse Demulsifier InjectionSkid 142.0 3.0 3.0 9.0 100000Demulsifier (Petrolite) Injection Skid 98.0 4.8 2.9 13.9 100000K-143 Chemical Injection Skid 5.0 2.3 2.0 4.6 100000Glycol Absorber 207.0 2.2 2.2 4.8 100000Seawater/Tempered Water Exch. 414.0 14.4 4.2 60.5 100000Fuel Gas Super Heater 10.0 5.9 0.6 3.5 100000Outlet Knock-Out Drum 72.0 0.9 0.9 0.7 100000Glycol Unit Feed Knock Out Drum 71.0 0.9 0.9 0.7 100000Glycol Absorber 113.0 0.8 0.8 0.6 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 1000003rd Stage Liquid Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Sea Water Cooling Pump 204.0 2.8 1.8 5.0 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Circulation Pump 73.0 4.5 2.8 12.6 100000Tempered Water Make-Up Pump 2.0 1.0 1.0 1.0 100000Air Pumps 230.0 1.0 1.0 1.0 100000Sea Water Cooling Filters 115.0 2.5 2.5 6.3 100000Tempered Water Storage Tank 949.0 6.0 4.0 24.0 1000001st Stage Knock Out Drum 222.0 4.0 4.0 16.0 1000002nd Stage Knock Out Drum 129.0 3.0 3.0 9.0 1000003rd Stage Knock Out Drum 286.0 4.0 4.0 16.0 1000004th Stage Knock Out Drum 182.0 3.2 3.2 10.2 100000Glycol Unit Feed Knock Out Drum 169.0 2.5 2.5 6.3 100000Pipeline Gas Knock Out Drum 91.0 2.2 2.2 4.8 100000Gas Turbine Knock Out Drum 27.0 1.8 1.8 3.2 100000Liquid Blow Drum 34.0 2.0 2.0 4.0 100000Instrument Air Dryer Set 50.0 3.7 2.0 7.4 100000Electric Heater 5.0 1.0 1.0 1.0 100000Air Blower 5.0 1.0 1.0 1.0 100000Prefilters 5.0 1.0 1.0 1.0 100000Drying Vessel 5.0 1.0 1.0 1.0 100000Chillers for AC Unit 56.0 1.0 1.0 1.0 100000After Cooler for Instrument Air Dryer 5.0 1.0 1.0 1.0 100000Start Up Generator/ Diesel Engine 90.0 6.5 3.0 19.5 100000Package Gycol Unit 872.0 16.6 5.1 84.7 100000Instrument Air Receiver 33.0 1.3 1.3 1.7 100000Air Handling Units 50.0 1.0 1.0 1.0 100000Instrument air Compressor 108.0 6.8 3.5 23.8 100000Air Pump 5.0 1.0 1.0 1.0 100000Diesel Oil Supply Pumps 1.3 0.3 0.3 0.1 100000Insturment Air Intake Filter 12.0 2.5 0.9 2.3 100000Fuel Tank for G/T Elec.Power Generator 490.0 2.6 1.3 3.3 100000Instrument Air Receiver 43.5 5.0 2.0 10.0 100000Low Pressure Fuel Gas KO Drum 27.8 1.8 1.8 3.2 100000Instrument air Buffer Tank 16.0 2.7 1.0 2.7 100000Diesel Filter Unit for Power Generator 10.0 1.0 1.0 1.0 100000Escape Capsule 25.0 6.2 3.7 22.9 100000Switch Board 310.0 14.4 2.0 28.8 100000



Equipment Footprint

Switch Board 60.0 16.2 1.6 25.9 100000Transformer 40.0 3.2 3.2 10.2 100000Transformer 40.0 3.2 3.2 10.2 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Buffer 1.5 0.1 0.1 0.0 100000Instrument Air Receiver 32.0 1.1 1.1 1.2 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Receiver 32.0 1.1 1.1 1.2 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 54.0 1.7 1.7 2.9 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 54.0 1.7 1.7 2.9 100000Gas Scrubber 1.0 0.2 0.2 0.0 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 57.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Instrument Air Buffer 64.0 1.7 1.7 2.9 100000Turbo Expander Precooler 140.0 5.0 3.0 15.0 Mezz1st Stage Compressor Discharge Cooler 78.0 11.0 1.5 16.5 Mezz1st Stage Compressor Discharge Cooler 78.0 11.0 1.5 16.5 Mezz2nd Stage Compressor Discharge Cooler 24.0 11.0 2.0 22.0 Mezz2nd Stage Compressor Discharge Cooler 24.0 11.0 2.0 22.0 Mezz3rd Stage Compressor Discharge Cooler 241.0 20.0 2.0 40.0 Mezz3rd Stage Compressor Discharge Cooler 241.0 20.0 2.0 40.0 Mezz4th Stage Compressor Discharge Cooler 32.0 10.0 1.5 15.0 Mezz4th Stage Compressor Discharge Cooler 32.0 10.0 1.5 15.0 MezzGas Scrubber 1.0 0.2 0.2 0.0 102438Gas Scrubber 1.0 0.2 0.2 0.0 103352Gas Scrubber 1.0 0.2 0.2 0.0 103353Gas Scrubber 1.0 0.2 0.2 0.0 103353Gas Scrubber 1.0 0.2 0.2 0.0 103353Instrument Air Buffer 64.0 1.7 1.7 2.9 104000Gas Scrubber 1.0 0.2 0.2 0.0 104384Instrument Air Buffer 64.0 1.7 1.7 2.9 104384Diesel Oil Storage Tank 544.0 4.0 2.4 9.6 105480Diesel Oil Transfer Pump 6.0 1.8 0.8 1.4 105480Diesel Oil Transfer Pump (Stand By) 6.0 1.8 0.8 1.4 105480Separator M.P. 408.0 9.0 2.8 25.2 105480Separator M.P. 408.0 9.0 2.8 25.2 105480Corrosion Inhibitor Pumps 10.0 1.5 3.0 4.5 105480Corrosion Inhibitor Skid 75.0 1.9 1.5 2.9 105480H.P. Separator 610.0 11.0 2.8 30.8 105480



Equipment Footprint

Hydrocyclone Skid 265.0 3.0 3.0 9.0 105480CK-352 Injection Pump Skids 30.0 1.5 3.0 4.5 105480Intelligent Pig Launcher 15.0 11.3 0.8 9.0 105480Test Separator 610.0 9.0 3.5 31.5 106663High Pressure Separator 1039.0 13.5 3.7 50.0 106663M.P. Separator 391.4 10.6 2.8 29.7 106663M.P. Separator 391.4 10.6 2.8 29.7 106663Degassing Vessel Skid 2079.0 11.9 4.5 53.6 106663Surge Vessel 870.0 13.9 3.8 52.8 106663Hydrocyclone Skid 618.0 4.5 4.5 20.3 106663M.P. Separator 640.0 9.4 2.8 26.3 106401Surge Vessel 1066.0 11.3 4.2 47.5 106401Degassing Vessel 588.0 10.0 4.5 45.0 106401Hydrocyclone Skid 49.0 4.0 2.0 8.0 106401Scale Inhibitor Skid 27.0 2.0 1.9 3.8 106401Biocide Injection Skid 29.0 2.0 1.9 3.8 106401Power Generator 95.0 2.5 2.3 5.8 107162Power Generator 95.0 2.5 2.3 5.8 107162Power Generator 95.0 2.5 2.3 5.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Power Generator Gas Turbine 413.0 10.5 3.5 36.8 107162Air Blast Cooler GT-4201A 25.0 2.0 1.1 2.2 107162Air Blast Cooler GT-4201B 25.0 2.0 1.1 2.2 107162Air Blast Cooler GT-4201S 25.0 2.0 1.1 2.2 1071626.3T OHT Crane 160.0 1.0 1.0 1.0 107163Expansion Tank 10.0 0.5 0.3 0.2 107162Instrument Air Comp./ Drier Skid 75.0 11.2 3.2 35.8 107162Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator 95.0 2.5 2.3 5.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Power Generator Gas Turbine 388.0 10.5 3.5 36.8 107163Instrument Air Compressor/ Drier Skid 75.0 5.5 2.1 11.3 107163Demulsifier Injection Skid 150.0 2.3 1.0 2.3 108000Corrosion Inhibitor Skid 110.0 4.0 2.6 10.4 108000Fuel Gas Sphere Launcher 79.0 7.0 1.4 9.8 108000Scale Inhibitor Mixer 30.0 2.0 1.0 2.0 108000Chemical Injection Skid 95.0 5.0 2.6 13.0 108000Mixer for T-5208 20.0 1.0 1.0 1.0 108000Corrosion Inhibitor Unit 87.0 7.3 3.2 23.4 108000Chemical Injection Skid 27.0 2.0 1.7 3.4 108000MP Separator 484.0 14.2 3.8 54.0 108000Extra High Pressure Test Separator 727.0 8.5 2.4 20.4 108000High Pressure Separator 610.0 14.2 2.7 38.3 108000Inlet K.O. Vessel 171.0 1.5 1.5 2.3 108000Outet K.O. Vessel 171.0 1.5 1.5 2.3 108000Mixer for T-5209 50.0 1.9 1.6 3.0 108000Glycol Contactor 324.0 1.6 1.6 2.6 108000Glycol Contactor 184.0 4.0 4.0 16.0 108000Panametric (Train`A') 25.0 1.4 1.0 1.4 108000Glycol Heater 75.0 3.3 0.4 1.3 108000Pedestal Crane 81.0 15.1 1.2 18.1 108000Hydraulic Oil Tank 50.0 2.0 1.5 3.0 108000Chemical Injection Skid 50.0 2.1 0.9 1.9 108000Outet K.O. Vessel 90.0 1.0 1.0 1.0 108000Inlet K.O. Vessel 117.0 1.2 1.2 1.4 108000Reverse Demulsifier Injection Skid 83.0 5.4 1.9 10.3 108000



Equipment Footprint

CK-352 Injection for V-5246 2.0 0.5 0.3 0.2 108000Degassing Buffer Tank 50.0 3.7 1.5 5.6 110058Process Gas Compressor 634.0 12.6 4.0 50.4 110058Process Gas Compressor Turbine 1469.0 17.0 4.2 71.4 1100583rd Stage Separator 226.0 6.1 2.1 12.8 1100584th Stage Separator 1561.0 14.5 2.7 39.2 1100581st Stage Recycled Cooler 22.0 1.2 0.6 0.7 110058MP Gas Cooler 217.0 7.2 1.2 8.6 110058Nitrogen Generation Package 100.0 4.1 1.3 5.3 110058Crane Support M-4203 330.0 1.0 1.0 1.0 110058Pedestal on KT-4201 585.0 15.3 5.1 78.0 110058Pedestal on K-4201 285.0 12.8 4.8 61.4 110058H.P. Separator 1441.0 15.2 4.1 62.3 110000H.P. Separator 1441.0 15.2 4.1 62.3 110000Extra High Pressure Separator 862.0 9.2 2.3 21.2 110000Extra High Pressure Test Separator 821.0 8.5 2.4 20.4 110000Hydrocyclone Skid 696.0 6.6 5.0 33.0 110000Process Gas Compressor 1068.0 12.6 4.0 50.4 110059Process Compressor Gas Turbine 2152.0 17.0 4.2 71.4 1100593rd Stage Separator 370.0 7.2 3.5 25.2 1100594th Stage Separator 2053.0 13.5 4.1 55.4 1100591st Stage Recycle Gas Cooler 30.0 4.0 1.6 6.4 1100592nd Stage Suction Cooler 27.0 4.0 1.6 6.4 110059Nitrogen Generation Package 100.0 4.1 1.3 5.3 110059Turbo Expander Package 125.0 8.0 3.5 28.0 MainFlash Gas Compressor Package 1600.0 23.0 5.0 115.0 MainFlash Gas Compressor Package 1600.0 23.0 5.0 115.0 MainRe-Injection Compressor Package 3500.0 23.0 5.0 115.0 MainRe-Injection Compressor Package 3500.0 23.0 5.0 115.0 MainRe-Injection Compressor Package Lube Oil Skid 150.0 23.0 5.0 115.0 MainRe-Injection Compressor Package Lube Oil Skid 150.0 23.0 5.0 115.0 MainTempered Water Make-up Pump 4.0 7.0 2.0 14.0 MainChemical Injection Barrel Pump 1.0 7.0 2.0 14.0 MainHot Oil Circulation Pumps 29.0 5.0 3.0 15.0 MainHot Oil Circulation Pumps 29.0 5.0 3.0 15.0 MainHot Oil Filters 50.0 2.0 2.0 4.0 MainSphere Launcher 10.0 4.1 0.5 2.0 1164592nd Stage Condenser 230.0 7.0 1.2 8.4 1164593rd Stage Condenser 341.0 7.0 1.3 9.1 1164594th Stage Condenser 480.0 8.0 1.1 8.8 11645915th OHT Crane 270.0 79.3 0.4 31.7 116459Pedestal Crane 432.0 4.1 4.1 16.8 116459Temp. Water H. Tank 92.0 3.7 1.8 6.7 116459Seal Oil Tank 130.0 3.0 2.0 6.0 116459Sphere Launcher 22.0 4.5 0.5 2.3 1164592nd Stage Condenser 437.5 7.9 1.6 12.6 1164593rd Stage Condenser 507.5 8.9 1.6 14.2 1164594th Stage Condenser 910.0 9.7 1.1 10.7 116459OHT Crane 24T 270.0 79.3 0.4 31.7 116459Pedestal Crane 15T 432.0 4.1 4.1 16.8 116459Tempered Water Heater Tank 91.5 3.8 1.8 6.8 116459Lube Oil Storage Tank 130.0 3.0 2.0 6.0 116459Heat Recovery Unit 925.0 16.0 8.0 128.0 ElevatedHeat Recovery Unit 925.0 16.0 8.0 128.0 Elevated

419.0

Appendix B

PILED FOUNDATION DATABASE

Penetration Pile Wall above mudline Penetration Total Hammer Blow

S.N. Ratio Year W.D. Pile Dia. Thick L1 L2 L1+L2 Type Countmm mm m m bpm

1 56.0 1965 34.4 A1 762 16 39.6 42.7 82.3 Vulcan 140-C 410

2 56.0 1965 A2 762 39.6 42.7 82.3 Vulcan 140-C 446

3 56.0 1965 A3 762 39.6 42.7 82.3 Vulcan 140-C 446

4 56.0 1965 A4 762 39.6 42.7 82.3 Vulcan 140-C 482

5 58.0 1965 B1 762 39.6 44.2 83.8 Vulcan 140-C 348

6 55.6 1965 B2 762 39.6 42.4 82.0 Vulcan 140-C 469

7 56.0 1965 B3 762 39.6 42.7 82.3 Vulcan 140-C 351

8 56.0 1965 B4 762 39.6 42.7 82.3 Vulcan 140-C 476

9 90.7 1983 36.3 A1 914 25 41.0 82.9 123.9 Vulcan 560 174

10 90.4 1983 A2 914 41.0 82.6 123.6 Vulcan 560 144

11 90.0 1983 A3 914 41.0 82.3 123.2 Vulcan 560 269

12 90.7 1983 B1 914 41.0 82.9 123.9 Vulcan 560 292

13 90.0 1983 B2 914 41.0 82.3 123.2 Vulcan 560 148

14 91.0 1983 B3 914 41.0 83.2 124.2 Vulcan 560 220

15 84.1 1978 35.97 A1 914.4 32 41.1 76.9 118.0 Menck 3000 292

16 95.9 1978 A2 914.4 41.1 87.7 128.8 Menck 4600 958

17 93.8 1978 A3 914.4 41.1 85.7 126.8 Menck 4600 968

18 83.5 1978 A4 914.4 41.1 76.4 117.5 Menck 3000 197

19 92.6 1978 B1 914.4 41.1 84.7 125.8 Menck 4600 581

20 93.4 1978 B2 914.4 41.1 85.4 126.5 Menck 4600 659

21 92.8 1978 B3 914.4 41.1 84.9 126.0 Menck 4600 984

22 92.8 1978 B4 914.4 41.1 84.8 125.9 Menck 4600 958

23 82.0 1978 35.96 A1 762 32 41.1 62.5 103.6 MRBS 3000/150 85

24 82.2 1978 A2 762 41.1 62.6 103.7 MRBS 3000/150 75

25 82.1 1978 B1 762 41.1 62.6 103.7 MRBS 3000/150 98

26 82.1 1978 B2 762 41.1 62.6 103.7 MRBS 3000/150 66

27 86.0 1978 35.96 A1 762 32 41.1 65.5 106.6 Menck 3000 203

28 86.1 1978 A2 762 41.1 65.6 106.7 Menck 3000 210

29 92.9 1978 B1 762 41.1 70.8 111.9 Menck 3000 131

30 86.3 1978 B2 762 41.1 65.7 106.8 Menck 3000 180

31 80.1 1966 35.6 A1 762 16 40.7 61.0 101.7 Vulcan 040 364

32 81.0 1966 B2 762 40.7 61.7 102.4 Vulcan 040 292

33 80.6 1966 A3 762 40.7 61.4 102.1 Vulcan 040 554

34 59.9 2002 35.55 A1 1219 32 41.5 73.0 114.5 MHU500T 148

35 59.3 2002 A2 1219 41.5 72.2 113.7 MHU500T 210

36 60.0 2002 B1 1219 41.5 73.2 114.7 MHU500T 151

37 59.3 2002 B2 1219 41.5 72.2 113.7 MHU500T 213

38 42.5 1982 19.2 A1 1219 32 24.1 51.8 75.9 Menck3000 69

39 42.5 1982 A2 1219 24.1 51.8 75.9 Menck3000 69

40 42.5 1982 B1 1219 24.1 51.8 75.9 Menck3000 75

41 42.5 1982 B2 1219 24.1 51.8 75.9 Menck3000 59

42 42.5 1982 C1 1219 24.1 51.8 75.9 Menck3000 69

43 42.5 1982 C2 1219 24.1 51.8 75.9 Menck3000 75

44 65.5 1978 20.116 A1 1219.2 32 25.3 79.9 105.2 4600/150 85

45 65.0 1978 A2 1219.2 25.3 79.2 104.5 4600/150 112

46 70.5 1978 A3 1219.2 25.3 86.0 111.3 4600/150 121

47 65.3 1978 A4 1219.2 25.3 79.6 104.9 4600/150 95

48 81.0 1978 B1 1219.2 25.3 98.8 124.1 4600/150 942

49 80.5 1978 B2 1219.2 25.3 98.1 123.4 4600/150 525

50 81.0 1978 B3 1219.2 25.3 98.8 124.1 4600/150 866

51 64.8 1978 B4 1219.2 25.3 78.9 104.2 4600/150 102

52 100.8 1978 20.116 A1 762 32 25.3 76.8 102.1 3000/150 85

53 91.2 1978 A2 762 25.3 69.5 94.8 3000/150 92

54 78.8 1978 B1 762 25.3 60.0 85.3 3000/150 59

55 78.8 1978 B2 762 25.3 60.0 85.3 3000/150 59

56 105.2 1978 20.116 A1 762 32 25.3 80.2 105.5 3000/150 79



57 96.8 1978 A2 762 25.3 73.8 99.1 3000/150 89

58 102.4 1978 B1 762 25.3 78.0 103.3 3000/150 82

59 93.6 1978 B2 762 25.3 71.3 96.6 3000/150 85

60 69.4 1998 27.2 A1 914.4 varies 35.2 63.5 98.7 Vulcan 530 196

61 69.4 1998 A2 914.4 35.2 63.5 98.7 Vulcan 530 148

62 69.4 1998 B1 914.4 35.2 63.5 98.7 Vulcan 530 148

63 69.4 1998 B2 914.4 35.2 63.5 98.7 Vulcan 530 136

64 86.7 1978 18.6 A1 762 25 23.3 66.1 89.4 Vulcan 040 794

65 100.3 1978 B2 762 23.3 76.4 99.7 Vulcan 040 33

66 90.4 1978 A3 762 23.3 68.9 92.2 Vulcan 040 817

67 80.0 1979 32.1 A1 762 25 36.9 61.0 97.9 Vulcan 340 322

68 80.1 1979 B2 762 36.9 61.0 97.9 Vulcan 340 433

69 80.1 1979 A3 762 36.9 61.0 97.9 Vulcan 340 194

70 111.2 1979 20.9 A1 762 25 25.6 84.7 110.3 Vulcan 340 262

71 109.6 1979 B2 762 25.6 83.5 109.1 Vulcan 340 157

72 110.4 1979 A3 762 25.6 84.1 109.7 Vulcan 340 246

73 75.2 1980 38.7 A1 762 25 43.4 57.3 100.7 MENCK 1500 617

74 74.0 1980 B2 762 43.4 56.4 99.8 MENCK 1500 715

75 74.8 1980 A3 762 43.4 57.0 100.4 MENCK 1500 837

76 55.0 1991 29.1 A1 914.4 25 35.1 50.3 85.4 Vulcan 530 154

77 64.2 1991 A2 914.4 35.1 58.7 93.8 Vulcan 530 121

78 64.7 1991 B1 914.4 35.1 59.1 94.2 Vulcan 530 154

79 64.7 1991 B2 914.4 35.1 59.1 94.2 Vulcan 530 161

80 78.9 1993 21.6 A1 762 25 27.6 60.1 87.7 Menck3000 104

81 78.3 1993 B2 762 27.6 59.7 87.3 Menck3000 120

82 79.0 1993 A3 762 27.6 60.2 87.8 Menck3000 124

83 72.4 1995 20.7 A1 914.4 25 28.7 66.2 94.9 70M 39

84 80.1 1995 A2 914.4 28.7 73.2 101.9 70M 49

85 72.4 1995 B1 914.4 28.7 66.2 94.9 70M 33

86 80.1 1995 B2 914.4 28.7 73.2 101.9 70M 39

87 67.8 1995 12.5 A1 914.4 25 20.5 62.0 82.5 Vulcan-560 33

88 76.6 1995 A2 914.4 20.5 70.0 90.5 Vulcan-560 46

89 67.8 1995 B1 914.4 20.5 62.0 82.5 Vulcan-560 26

90 76.6 1995 B2 914.4 20.5 70.0 90.5 Vulcan-560 39

91 36.7 1997 29.9 A1 1219 25 36.3 44.8 81.1 Menck 3000 80

92 36.3 1997 A2 1219 36.3 44.3 80.6 Vulcan 560 75

93 36.3 1997 B1 1219 36.3 44.3 80.6 Menck 3000 84

94 36.3 1997 B2 1219 36.3 44.3 80.6 Vulcan 560 66

95 49.9 1998 40.8 A1 1066.8 25 46.6 53.3 99.9 MENCK 3000 121

96 47.3 1998 A2 1066.8 46.6 50.5 97.1 Vulcan 560 98

97 49.9 1998 B1 1066.8 46.6 53.3 99.9 Menck 3000 105

98 47.3 1998 B2 1066.8 46.6 50.5 97.1 Menck 3000 160

99 53.3 2000 25.2 A1 1219.2 32 33.2 65.0 98.2 Vulcan 560 124

100 65.6 2000 A2 1219.2 33.2 80.0 113.2 Menck 3900 312

101 53.3 2000 B1 1219.2 33.2 65.0 98.2 Vulcan 560 156

102 64.4 2000 B2 1219.2 33.2 78.5 111.7 Menck 3900 1216

103 76.6 2000 12.8 A1 914 25 22.0 70.0 92.0 Vulcan 530 152

104 76.6 2000 A2 914 22.0 70.0 92.0 Vulcan 530 140

105 76.6 2000 B1 914 22.0 70.0 92.0 Vulcan 530 168

106 76.6 2000 B2 914 22.0 70.0 92.0 Vulcan 530 144

107 61.3 1981 A1 914.4 25 28.9 56.1 85.0 Delmag D55 302

108 61.3 1981 A2 914.4 28.9 56.1 85.0 Delmag D55 276

109 62.3 1981 B1 914.4 28.9 57.0 85.9 Delmag D55 420

110 62.3 1981 B2 914.4 28.9 57.0 85.9 Delmag D55 525

111 60.4 1981 A1 914 25 35.1 55.2 90.2 Menck 3000 135

112 60.4 1981 A2 914 35.1 55.2 90.2 Menck 3000 161

113 60.9 1981 B1 914 35.1 55.6 90.7 Menck 3000 92



114 47.0 1981 B2 914 35.1 43.0 78.0 Menck 3000 272

115 45.7 2004 27.24 A1 1219 32 34.4 55.8 90.2 MHU600 80

116 46.0 2004 A2 1219 34.4 56.1 90.5 MHU600 100

117 45.9 2004 B1 1219 34.4 56.0 90.4 MHU600 80

118 45.9 2004 B2 1219 34.4 56.0 90.4 MHU600 80

119 109.7 1992 35 A1 762 16 40.0 83.6 123.6 Vulcan 530 384

120 109.8 1992 B2 762 40.0 83.6 123.6 Vulcan 530 1529

121 109.7 1992 A3 762 40.0 83.6 123.6 Vulcan 530 377

122 67.3 1981 36.1 A1 914 25 41.4 61.6 103.0 Menck MRBS 1500 197

123 67.5 1981 A2 914 41.4 61.7 103.1 Menck MRBS 1500 253

124 67.3 1981 B1 914 41.4 61.6 103.0 Menck MRBS 1500 151

125 67.3 1981 B2 914 41.4 61.6 103.0 Menck MRBS 1500 190

126 41.4 1999 35.55 A1 1219 25 41.3 50.5 91.8 Vulcan 530 32

127 41.4 1999 A2 1219 41.3 50.5 91.8 Vulcan 530 140

128 43.3 1999 B1 1219 41.3 52.8 94.1 Vulcan 530 96

129 41.4 1999 B2 1219 41.3 50.5 91.8 Vulcan 530 28

130 77.2 1978 25.9 A1 762 25 30.6 58.8 89.4 Vulcan 020 184

131 77.2 1978 B2 762 30.6 58.8 89.4 Vulcan 020 217

132 76.0 1978 A3 762 30.6 57.9 88.5 Vulcan 020 217

133 68.0 1980 22.9 A1 762 25 27.6 51.8 79.4 Vulcan 040 1047

134 80.0 1980 B2 762 27.6 61.0 88.6 Vulcan 040 66

135 66.2 1980 A3 762 27.6 50.4 78.0 Vulcan 040 991

136 86.5 1981 30.1 A1 762 25 34.8 65.9 100.7 Vulcan 040 138

137 108.6 1981 B2 762 34.8 82.8 117.6 Vulcan 040 125

138 86.4 1981 A3 762 34.8 65.8 100.6 Vulcan 040 141

Appendix C

SOIL PROFILE DATABASE AND ENGINEERING PARAMETERS

16

16

22

40

40

162

34

14

762 PILEABOVE

MUDPENETRA

TION HAMMER PS2A

A1 40 43Vulcan 140-C

A2 40 43 do

B3 40 43 doB4 40 43 do

B2 40 43 do

A4 40 43 doA3 40 43 do

B1 40 43 do

43MUDLINE

MEAN SEA LEVEL

45

38

38

38

50

PS2C

25

169

541

1532

PILEABOVE

MUDPENETRA

TION HAMMER PS2CA1 41 83 Vulcan 560A2 41 83 do

B1 41 83 doB2 41 83 do

A3 41 83 do

B3 41 83 do

4183

23

914

MUDLINE

MEAN SEA LEVEL

PS 2

32

4121

62

PILEABOVE

MUDPENETRA

TION HAMMER PS2D

A1 41 77Menck 3000

A2 41 87Menck 4600

B1 41 85Menck 4600

B2 41 85 do

A4 41 77Menck 3000

A3 41 86Menck 4600

B3 41 85 doB4 41 85 do

32

87

77

23

10

0.686 shoe -45mm

4120

914

MUDLINE

MEAN SEA LEVEL

32

&

45 SHOE

5025

452

3

PILEABOVE

MUDPENETRA

TION HAMMER PS2E

A1 41 63MRBS 3000

A2 41 63 doB1 41 63 doB2 41 63 do

76

762

MUDLINE

MEAN SEA LEVEL

32

4121

23

414

762 PILEABOVE

MUDPENETRA

TION HAMMER

A1 41 66Menck 3000

A2 41 66 doB1 41 71 doB2 41 66 do

66

71

32

45 shoe

572mm

45 shoe

572mm

762

762

MUDLINE

MEAN SEA LEVEL

16

5327

462

4

PILEABOVE

MUDPENETRA

TION HAMMERA1 41 61 Vulcan 040B2 41 61 doA3 41 61 do

79

762

MUDLINE

MEAN SEA LEVEL

25

25

38

32

2228

1016

23

5019

3

762 PILEABOVE

MUDPENETRA

TION HAMMERA1 40 84 Vulcan 530B2 40 84 DOA3 40 84 DO

4910

3

762

MUDLINE

MEAN SEA LEVEL

40

25

31.2

305

PILE ABOVE MUD PENETRATION HAMMERA1 42 51 Vulcan 530A2 42 51 DOB1 42 51 DOB2 42 51 DO

54.6

66.3

1219

MUDLINE

MEAN SEA LEVEL

50

40

38

32

38

38

25 38

1118

315

326

6

1219 PILEABOVE

MUDPENETRA

TION HAMMERA1 42 73 MHUT500TA2 42 73 DOB1 42 73 DOB2 42 73 DO

4985

2

1219

MUDLINE

MEAN SEA LEVEL

25 & 38 SHOE

6.1

4. 443

.14. 1

54.7

25

914 PILEABOVE

MUDPENETRA

TION HAMMER

A1 41 62Menck 3000

A2 41 62 DOB1 41 62 DOB2 41 62 DO

82.7

914

MUDLINE

MEAN SEA LEVEL

32

47

2238

1910

28

82

3

1219

MUDLINE

MEAN SEA LEVEL

1219 PILEABOVE

MUDPENETRA

TION HAMMERA1 25 80 4600/150A2 25 80 DOA3 25 86 DOA4 25 80 DOB1 25 98 DOB2 25 98 DOB3 25 98 DOB4 25 79 DO

2914

720

2545

92

99

112

1219

1219

1219

915 mm shoe

45mm 915 mm shoe

45mm

MUDLINE

MEAN SEA LEVEL

2713

719

2323

85

762 PILEABOVE

MUDPENETRA

TION HAMMERA1 25 80 3000/150A2 25 74 DOB1 25 78 DOB2 25 77 DO

21

78

16

79

MUDLINE

MEAN SEA LEVEL

2713

719

2420

762 PILEABOVE

MUDPENETRA

TION HAMMERA1 25 77 3000/150A2 25 70 DOB1 25 60 DOB2 25 60 DO

84

76

65

137

1924

13

137

1924

2

MUDLINE

MEAN SEA LEVEL

25

u

25

314

205

2536

7

914 PILEABOVE

MUDPENETRA

TION HAMMERA1 35 64 Vulcan 530A2 35 64 DOB1 35 64 DOB2 35 64 DO

3971

MUDLINE

MEAN SEA LEVEL

25

45

38

25 24

1521

2618

43

4

762 PILEABOVE

MUDPENETRA


2891

4

25

45

38

25

83

1512

105

107

2126

4

MUDLINE

MEAN SEA LEVEL

38

25

32

25

1028

39

34 1

194

PILEABOVE

MUDPENETRA


76

762

MUDLINE

MEAN SEA LEVEL

38

25

32

25 16

305

314

2618

4

762 PILEABOVE

MUDPENETRA


4676

33MUDLINE

MEAN SEA LEVEL

38

25

32

25

611

33

525

436

42

4

762 PILEABOVE

MUDPENETRA


3410

9

MUDLINE

MEAN SEA LEVEL

25

&

35 SHOE

15.3

35.4

6.2

54.5

11

762 PILEABOVE

MUDPENETRA

TION HAMMER

A1 43 57Menck 1500

B2 43 57 doA3 43 57 do

72.2

762

MUDLINE

MEAN SEA LEVEL

25

38

4031

2215

141

4

762 PILEABOVE

MUDPENETRA


75

87

25

38

MUDLINE

MEAN SEA LEVEL

38

25

35

25 24

194

33

425

244

95

PILEABOVE

MUDPENETRA


4380

38

25

35

25

423

1245

414

14

4310

1

34

5MUDLINE

MEAN SEA LEVEL

25

32

38

25 5

264

97

2024

762 PILEABOVE

MUDPENETRA

TION HAMMER

A1 28 60Menck 3000

B2 28 60 DOA3 28 60 DO

3268

MUDLINE

MEAN SEA LEVEL

25

32

25

107

1110

2635

174

88

PILEABOVE

MUDPENETRA

TION HAMMERA1 29 66 70MA2 29 73 doB1 29 66 doB2 29 73 do

7925

32

25

152

1926

97

824

MUDLINE

MEAN SEA LEVEL

25

25

32

32

176

147

137

2512

78

914 PILEABOVE

MUDPENETRA

TION HAMMERA1 21 62 Vulcan 560A2 21 70 doB1 21 62 doB2 21 70 do

69

23

25

25

32

32

815

3

MUDLINE

MEAN SEA LEVEL

38

25

0

453

43

1425

32

PILEABOVE

MUDPENETRA

TION HAMMER

A1 36 44Menck 3000

A2 36 44 Vulcan 560

B1 36 44Menck 3000

B2 36 44 Vulcan 560

55

MUDLINE

MEAN SEA LEVEL

38

32.9

13.6

57.6

511

PILEABOVE

MUDPENETRA

TION HAMMER

A1 47 53Menck 3000

A2 47 50 Vulcan 560

B1 47 53Menck 3000

B2 47 50 Vulcan 560

55.3

62.4

MUDLINE

MEAN SEA LEVEL

PILEABOVE

MUDPENETRATION HAMMER

A1 33 65 Vulcan 560

A2 33 80Menck 3900

B1 33 65 Vulcan 560

B2 33 80Menck 3900

71

88

38

32

32

40

50

44

45

32 11

.15

15.4

6.6

9.9

11.1

123

75

25

38

32

32

40

50

44

45

32

25 14

.413

.28.

410

15.4

6.6

9.9

27.6

43

55

244 shoe

36.2

515

44 shoe

MUDLINE

MEAN SEA LEVEL

38

32

25

25

32

38

50

44

32 12

.29.

725

.67.

37.

37.

96.

718

.115

.5

914 PILEABOVE

MUDPENETRA

TION HAMMERA1 22 70 Vulcan 530A2 22 70 DOB1 22 70 DOB2 22 70 DO

26.8

85.2

MUDLINE

MEAN SEA LEVEL

5038

25

32

38

44

32

25

38

32 55

247

1212

1614

45

41

44

2 . 0

PILEABOVE

MUDPENETRA

TION HAMMERA1 34 56 MHU 600A2 34 56 doB1 34 56 doB2 34 56 do

7546

MUDLINE

MEAN SEA LEVEL

914 PILEABOVE

MUDPENETRA

TION HAMMER

A1 29 56Delmag

D55A2 29 56 doB1 29 57 doB2 29 57 do

25

&

38 SHOE

38

359

3815

43

69

MUDLINE

MEAN SEA LEVEL

34

33

1313

134

PILEABOVE

MUDPENETRA

TION HAMMER

A1 35 55Menck 3000

A2 35 56 DOB1 35 56 DOB2 35 43 DO

66

25

38

41

33

43

1321

3

25

38

MUDLINE

MEAN SEA LEVEL

32

&

45 SHOE

4121

372

3

PILEABOVE

MUDPENETRA

TION HAMMER PS2E

A1 41 63MRBS 3000

A2 41 63 doB1 41 63 doB2 41 63 do

63

762

MUDLINE

MEAN SEA LEVEL

Appendix D

PREDICTED STATIC CAPACITY OF PILES IN THE DATABASE

INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 1219 mm Ko cohesive 0.5Pile Wall Thickness 32.0 mm Ko non-cohesive 1.0 Steel cross section area 0.119 m2 mm/mOutside Radius 609.5 0.14 Soil tip area 1.048 m2 mm/m

mm

Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Q p Q u Sunc Q f Q p Q u

m deg kN/m3 kPa kPa kPa kPa kPa deg Limiting kPa kPa kN kN kN kN kN kN

Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.17 0.14 0.7 3 2 3 2 1.00 3 2 3 2

1.00 0 8 8 0.00 0.00 1.1 1.4 320 320 335 38 2 43 Cored 0.20 0.89 335 38 2 43 12% 0.001.00 0 8 8 0.00 0.00 1.0 1.0 64 64 67 2 67 2 0.00

6.50 0.13 0.13 4.7 117 111 119 113 1.00 117 111 119 1137.50 0 67 67 0.00 0.00 8.4 8.4 532 532 557 63 113 296 Cored 557 63 113 296 79% 0.027.50 75 67 44 1.69 0.44 32.9 32.9 675 675 707 113 707 113 0.00

1.70 0.00 0.00 33.8 220 208 339 322 0.67 147 139 266 2529.20 75 83 55 1.36 0.46 34.7 34.7 675 675 707 81 322 742 Cored 707 81 252 599 87% 0.039.20 0 83 83 0.00 0.00 18.1 11.6 1984 1984 2078 322 2078 252 0.00

2.60 0.22 0.14 13.2 132 125 471 446 1.00 132 125 398 37711.80 0 106 106 0.00 0.00 23.2 14.8 2545 2545 2667 304 446 1221 Cored 2667 304 377 1078 72% 0.0611.80 75 106 71 1.06 0.49 37.0 37.0 675 675 707 446 707 377 0.00

2.10 0.00 0.00 38.1 306 290 777 736 0.67 204 193 602 57013.90 75 123 82 0.92 0.52 39.2 39.2 675 675 707 81 707 1565 Plugged 707 81 570 1252 94% 0.0713.90 0 123 123 0.00 0.00 21.0 17.2 4914 4914 5149 736 5149 570 0.00

1.10 0.17 0.14 17.9 75 71 852 808 1.00 75 71 677 64215.00 0 133 133 0.00 0.00 22.7 18.6 5310 5100 5343 609 808 2269 Cored 5343 609 642 1927 68% 0.1115.00 75 133 89 0.85 0.54 40.7 40.7 675 675 707 707 707 642 0.00

3.00 0.00 0.00 42.7 491 465 1343 1273 0.67 327 310 1004 95118.00 75 160 107 0.70 0.60 44.7 44.7 675 675 707 81 707 2131 Plugged 707 81 707 1792 96% 0.1018.00 0 160 160 0.00 0.00 27.3 22.0 1278 1278 1339 1273 1339 951 0.00

1.10 0.17 0.14 22.0 93 88 1436 1360 1.00 93 88 1097 103919.10 0 170 170 0.00 0.00 29.0 22.0 1357 1357 1422 162 1360 2958 Cored 1422 162 1039 2298 93% 0.1319.10 0 170 170 0.00 0.00 29.0 22.0 6786 5100 5343 1360 5343 1039 0.00

2.40 0.17 0.14 22.0 202 192 1638 1552 1.00 202 192 1299 123121.50 0 191 191 0.00 0.00 32.7 22.0 7650 5100 5343 609 1552 3799 Cored 5343 609 1231 3139 81% 0.1821.50 0 191 191 0.00 0.00 32.7 22.0 4590 4590 4809 1552 4809 1231 0.00

3.00 0.17 0.14 22.0 253 239 1891 1792 1.00 253 239 1552 147024.50 0 218 218 0.00 0.00 37.3 22.0 5238 5100 5343 609 1792 4291 Cored 5343 609 1470 3631 83% 0.2124.50 130 218 146 0.89 0.53 68.8 68.8 1170 1170 1226 1226 1226 1226 0.00

14.00 0.00 0.00 78.0 4182 3962 6073 5754 0.67 2788 2642 4340 411238.50 130 351 234 0.56 0.67 87.2 87.2 1170 1170 1226 140 1226 7438 Plugged 1226 140 1226 5705 98% 0.3338.50 0 351 351 0.00 0.00 77.0 22.0 8430 5100 5343 5343 5343 5343 0.00

8.50 0.22 0.14 22.0 716 679 6789 6433 1.00 716 679 5056 479147.00 0 419 419 0.00 0.00 91.9 22.0 10062 5100 5343 609 5343 12741 Plugged 5343 609 4791 10455 94% 0.6147.00 0 419 419 0.00 0.00 71.7 22.0 16770 5100 5343 5343 5343 4791 0.00

1.10 0.17 0.14 22.0 93 88 6882 6520 1.00 93 88 5149 487848.10 0 429 429 0.00 0.00 73.4 22.0 17166 5100 5343 609 5343 12834 Plugged 5343 609 4878 10636 94% 0.6248.10 0 429 429 0.00 0.00 73.4 22.0 3433 3433 3597 3597 3597 3597 0.00

41.90 0.17 0.14 22.0 3530 3345 10412 9865 1.00 3530 3345 8679 822390.00 0 827 827 0.00 0.00 141.5 22.0 6618 5100 5343 609 5343 16364 Plugged 5343 609 5343 14631 96% 0.8590.00 300 827 551 0.54 0.68 203.4 203.4 2700 2700 2829 2829 2829 2829 0.00

10.20 0.00 0.00 209.2 8170 7741 18582 17606 0.67 5447 5161 14126 13384100.20 300 924 616 0.49 0.72 215.0 215.0 2700 2700 2829 322 2829 21733 Plugged 2829 322 2829 17277 98% 1.00

5100 100000 100000 0

22 5100 20

hard CLAY C 0 9.5 0 0 22

0.47 8 22 5100dense carbonate S 25 9.5

5100 22 5100 20

22 5100 25

CALCARENITE, S 25 9.0 0.47 40 22

25

20

medium dense t S 30 8.0 0.47 24 22 5100

24

40

8

0

0

510022

510022

100000

CALCULATIONS SRDq

kPa

fkPa

5100

5100

20

15

100000

5100

22

100000

0.47

25510022

0 10000022 5100

220.47

100000

20

0

5100

5100

5100

20

20

0.47

0

0

0.47

0

0.47

0.47

25

0

8.0

9.0

9.0

9.0

9.0

9.0

9.5

25

0

25

25

9.5

9.0

8.0

0

30

25

20

0

9.0

C

S

C

S

0

8

40

0

8

24

40

S

C

S

S

stiff calcareous s

dense carbonate

CALCARENITE,

medium dense c

dense carbonate

firm to stiff carbo

CALCARENITE,

stiff carbonate C

THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULF

SOIL & FOUNDATION ENGINEERING - PILE BEARING CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul

stiff carbonate cl

INPUT DATA

CALCARENITE,

loose to medium

C

S

S

0.47

22

22

22

100000

22

100000

22

8

22 5100

5100

22 5100

22 5100

510022

SOIL IDENTIFICATION: 3M05

K.tan��limit

K.tan�

22

10000022 5100

22 5100

5100


mm

Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Qp Qu Sunc Q f Qp Qu


Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

re

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssu

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

er

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r =

Shaf

t out

er fr

ictio

n fo

r eac

h la

y e

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.20 0.17 0.14 0.1 0 0 0 0 1.00 0 0 0 0

0.20 0 2 2 0.00 0.00 0.2 0.3 19 19 21 2 0 2 Cored 0.20 0.18 21 2 0 2 10% 0.000.20 0 2 2 0.00 0.00 0.3 0.2 19 19 21 0 21 0 0.00

0.70 0.17 0.14 0.6 2 2 2 2 1.00 2 2 2 20.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 8 2 12 Cored 93 8 2 12 30% 0.000.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 2 93 2 0.00

6.10 0.17 0.14 4.6 108 104 110 106 1.00 108 104 110 1067.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 66 106 282 Cored 761 66 106 282 76% 0.027.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 106 761 106 0.00

3.00 0.17 0.14 9.9 114 110 224 215 1.00 114 110 224 21510.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 93 215 533 Cored 1070 93 215 533 82% 0.0410.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 215 1070 215 0.00

1.50 0.17 0.14 12.5 72 69 296 284 1.00 72 69 296 28411.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 108 284 688 Cored 1234 108 284 688 84% 0.0511.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 284 1234 284 0.00

3.50 0.17 0.14 15.5 208 199 504 483 1.00 208 199 504 48315.00 0 126 126 0.00 0.00 21.5 17.6 1507 1507 1617 141 483 1129 Cored 1617 141 483 1129 87% 0.0915.00 0 126 126 0.00 0.00 27.5 17.6 1507 1507 1617 483 1617 483 0.00

6.40 0.22 0.14 19.8 485 465 989 948 1.00 485 465 989 94821.40 0 177 177 0.00 0.00 38.7 22.0 2121 2121 2276 199 948 2136 Cored 2276 199 948 2136 91% 0.1721.40 150 177 118 1.27 0.47 70.6 70.6 1350 1350 1449 948 1449 948 0.00

15.60 0.00 0.00 79.3 4736 4542 5725 5490 0.67 3157 3028 4146 397637.00 150 309 206 0.73 0.59 87.9 87.9 1350 1350 1449 127 1449 7301 Plugged 1449 127 1449 5722 98% 0.4437.00 0 309 309 0.00 0.00 52.9 22.0 3712 3712 3984 3984 3984 3976 0.00

1.00 0.17 0.14 22.0 84 81 5809 5571 1.00 84 81 4231 405738.00 0 319 319 0.00 0.00 54.6 22.0 3827 3827 4108 359 4108 10276 Plugged 4108 359 4057 8647 96% 0.6738.00 150 319 213 0.71 0.60 89.3 89.3 1350 1350 1449 1449 1449 1449 0.00

10.50 0.00 0.00 95.9 3855 3697 9664 9268 0.67 2570 2465 6801 652248.50 150 420 280 0.54 0.68 102.4 102.4 1350 1350 1449 127 1449 11240 Plugged 1449 127 1449 8376 98% 0.6548.50 0 420 420 0.00 0.00 71.8 22.0 5037 5037 5406 5406 5406 5406 0.00

2.00 0.17 0.14 22.0 169 162 9833 9429 1.00 169 162 6969 668350.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 478 5474 15785 Plugged 5474 478 5474 12921 96% 1.0050.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 5474 5474 5474 0.00

4.50 0.17 0.14 22.0 379 364 10212 9793 1.00 379 364 7348 704755.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 478 5474 16164 Plugged 5474 478 5474 13300 96% 1.0355.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 5474 5474 5474 0.00

10.00 0.17 0.14 22.0 843 808 11054 10601 1.00 843 808 8191 785565.00 0 558 558 0.00 0.00 95.4 22.0 6693 5100 5474 478 5474 17006 Plugged 5474 478 5474 14143 97% 1.0965.00 0 558 558 0.00 0.00 122.2 22.0 11155 5100 5474 5474 5474 5474 0.00

2.50 0.22 0.14 22.0 211 202 11265 10803 1.00 211 202 8401 805767.50 0 583 583 0.00 0.00 127.7 22.0 11655 5100 5474 478 5474 17217 Plugged 5474 478 5474 14353 97% 1.1167.50 150 583 389 0.39 0.80 120.7 120.7 1350 1350 1449 1449 1449 1449 0.00

6.00 0.00 0.00 123.4 2836 2720 14101 13523 0.67 1891 1813 10292 987073.50 150 637 425 0.35 0.84 126.2 126.2 1350 1350 1449 127 1449 15677 Plugged 1449 127 1449 11868 99% 0.9273.50 0 637 637 0.00 0.00 108.9 22.0 7641 5100 5474 5474 5474 5474 0.00

1.00 0.17 0.14 22.0 84 81 14185 13604 1.00 84 81 10376 995174.50 0 645 645 0.00 0.00 110.3 22.0 7737 5100 5474 478 5474 20137 Plugged 5474 478 5474 16328 97% 1.2674.50 150 645 430 0.35 0.85 127.0 127.0 1350 1350 1449 1449 1449 1449 0.00

11.50 0.00 0.00 131.6 5796 5558 19981 19162 0.67 3864 3705 14240 1365686.00 150 743 495 0.30 0.91 136.2 136.2 1350 1350 1449 127 1449 21557 Plugged 1449 127 1449 15816 99% 1.22

100000 100000 0

5100 20

silt C 0 8.5 0 0 22 5100

12 22 5100 22S 25 8.0 0.47

5100 100000 100000 0

22 5100 25

silt C 0 9.0 0 0 22

5100 20

gypsum S 30 10.0 0.47 20 22 5100

20

silt S 25 8.5 0.47 12 22 5100 22

22 5100 22 5100calc S 25 8.0 0.47 12

calc

CALCULATIONS

5100

5100

5000

5100 20

0.47

0

20

qkPa

20

25

20

20

20

20

12

5100

5100

0.47

12

12

12

120.47 22

510022

5100

0

20

100000

5100

100000

22

22

0

0.47

0.47

0.47

0.47

0

0.4725

0

25

8.5

8.5

8.0

8.5

9.6

9.6

8.5

25

25

25

0

8.025

25

25

25 8.5

8.0

8.0

S

S

S

S

22 5100

22 5100

C

S

S

C

sand

clay

sand

clay

calc

sand

silt

calc

22



SRD

SOIL IDENTIFICATION: 3N09

K.tan��limit

K.tan�

100000

sand

INPUT DATA

sand

calc

S

S

S

0.47

22

5100

22

12

fkPa

12

12

12

0

22

22

22 5100

5100

22 5100

22 5100

22

100000

0 22 5100

22 5000

22 5100

22 5100

22

INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 762 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section area 0.058 m2 mm/mOutside Radius 381 0.14 Soil tip area 0.398 m2 mm/m

mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.05 0.22 0.14 2.3 17 16 17 16 1.00 17 16 17 16

3.05 0 21 21 0.00 0.00 3.0 4.7 897 897 357 52 16 85 Cored 0.20 2.36 357 52 16 85 39% 0.013.05 45 21 14 3.16 0.37 16.9 16.9 405 405 161 16 161 16 0.00

3.35 0.00 0.00 18.9 151 141 168 157 0.67 101 94 118 1106.40 45 50 33 1.35 0.46 20.9 20.9 405 405 161 23 157 349 Cored 161 23 110 252 91% 0.046.40 0 50 50 0.00 0.00 13.5 0.0 2093 0 0 0 0 0 0.00

1.22 0.27 0.14 0.0 0 0 168 157 1.00 0 0 118 1107.62 0 61 61 0.00 0.00 16.7 0.0 2579 0 0 0 0 168 Plugged 0 0 0 118 100% 0.027.62 60 61 41 1.47 0.45 27.3 27.3 540 540 215 157 215 110 0.00

1.52 0.00 0.00 27.9 102 95 270 252 0.67 68 63 186 1749.14 60 74 50 1.21 0.48 28.6 28.6 540 540 215 31 215 516 Plugged 215 31 174 390 92% 0.069.14 0 74 74 0.00 0.00 16.3 10.4 1784 1784 710 252 710 174 0.00

3.05 0.22 0.14 12.1 88 83 358 335 1.00 88 83 274 25612.19 0 99 99 0.00 0.00 21.6 13.8 2370 2370 943 137 335 831 Cored 943 137 256 667 79% 0.1012.19 0 99 99 0.00 0.00 26.8 13.8 2370 2370 943 335 943 256 0.00

3.66 0.27 0.14 16.3 142 133 501 468 1.00 142 133 417 38915.85 0 134 134 0.00 0.00 36.2 18.7 3204 3204 1276 185 468 1154 Cored 1276 185 389 991 81% 0.1415.85 0 134 134 0.00 0.00 29.3 18.7 1335 1335 532 468 532 389 0.00

0.91 0.22 0.14 19.1 42 39 543 507 1.00 42 39 458 42816.76 0 140 140 0.00 0.00 30.7 19.6 1399 1399 557 81 507 1131 Cored 557 81 428 967 92% 0.1416.76 0 140 140 0.00 0.00 30.7 19.6 3357 3357 1337 507 1337 428 0.00

4.58 0.22 0.14 20.8 228 213 771 720 1.00 228 213 686 64121.34 0 179 179 0.00 0.00 39.2 22.0 4291 4291 1709 248 720 1739 Cored 1709 248 641 1576 84% 0.2321.34 225 179 119 1.89 0.43 96.0 96.0 2025 2025 806 720 806 641 0.00

15.23 0.00 0.00 103.0 3755 3508 4525 4228 0.67 2503 2339 3189 298036.57 225 308 206 1.09 0.49 110.0 110.0 2025 2025 806 117 806 5449 Plugged 806 117 806 4113 97% 0.6036.57 0 308 308 0.00 0.00 83.6 22.0 7398 5100 2031 2031 2031 2031 0.00

7.43 0.27 0.14 22.0 391 366 4916 4594 1.00 391 366 3581 334644.00 0 379 379 0.00 0.00 102.8 22.0 9092 5100 2031 295 2031 7242 Plugged 2031 295 2031 5906 95% 0.8644.00 0 379 379 0.00 0.00 64.8 22.0 9092 5100 2031 2031 2031 2031 0.00

18.50 0.17 0.14 22.0 974 910 5891 5504 1.00 974 910 4555 425662.50 0 518 518 0.00 0.00 88.5 22.0 12422 5100 2031 295 2031 8216 Plugged 2031 295 2031 6881 96% 1.0062.50 0 518 518 0.00 0.00 140.5 22.0 12422 5100 2031 2031 2031 2031 0.00

6.50 0.27 0.14 22.0 342 320 6233 5824 1.00 342 320 4897 457669.00 0 579 579 0.00 0.00 157.2 22.0 13904 5100 2031 295 2031 8559 Plugged 2031 295 2031 7223 96% 1.0569.00 0 579 579 0.00 0.00 157.2 81.1 13904 10000 3982 3982 3982 3982 0.00

6.00 0.27 0.14 85.1 1222 1142 7455 6966 1.00 1222 1142 6120 571875.00 0 636 636 0.00 0.00 172.7 89.1 15272 10000 3982 579 3982 12016 Plugged 3982 579 3982 10680 95% 1.55

10000 100 10000 30

22 5100 30

dense carbonate SAND S 35 9.5 0.47 24 100

0.47 24 22 5100dense carbonate SAND S 35 9.5

CALCULATIONS SRD

25

0

qkPa

30

25

30

25

0

5100

5100

100000

25

0

0

0

5100

5100

0.47

0100000100000

5100

30

20

5100

100000

51000.47

0.47

0.47

0.47

0.47

0.47

00

35

25

8.0

9.5

7.0

8.5

8.5

9.5

7.5

30

35

25

30

8.50

30

0

35 9.5

8.5

7.0

0

42

42

420.47 0

S

S

S

C

0 0

0 0

S

S

C

S

very weak carbonate SILTSTONE wit

dense cemented carbonate SAND wit

stiff carbonate CLAY with traces of gy

very weak fine to coarse carbonate SA

firm to stiff carbonate CLAY

dense carbonate SAND


clayey carbonate SILT interbedded w



dense carbonate SILTY SAND slightly

INPUT DATA

loose carbonate SAND slightly cemen

firm carbonate CLAY with shell fragm

S

C

S

0

22

100000

24

fkPa

24

24

10

24

100000

22

22

22

22 5100

22 5100

22

22

22

22

22 5100

5100

22 5100

0 0

SOIL IDENTIFICATION: 3SC-K05

K.tan��limit

K.tan�

22 5100

22 5100

22 5100

24


mm

Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Qf Qp Qu Sunc Qf Qp Qu


Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 07.50 0.22 0.14 7.1 127 119 127 119 1.00 127 119 127 119

7.50 0 65 65 0.00 0.00 9.1 14.2 2591 2591 1032 150 119 396 Cored 0.20 7.17 1032 150 119 396 62% 0.037.50 75 65 43 1.73 0.44 32.6 32.6 674 674 268 119 268 119 0.00

2.32 0.00 0.00 33.9 188 175 315 295 0.67 125 117 253 2369.81 75 87 58 1.30 0.47 35.1 35.1 674 674 268 39 268 623 Plugged 268 39 236 528 93% 0.059.81 0 87 87 0.00 0.00 23.5 12.1 3464 3464 1379 295 1379 236 0.00

4.63 0.27 0.14 15.2 168 157 484 452 1.00 168 157 421 39314.45 0 130 130 0.00 0.00 35.3 18.2 5210 5100 2031 295 452 1231 Cored 2031 295 393 1110 73% 0.0914.45 0 130 130 0.00 0.00 35.3 18.2 3126 3126 1245 452 1245 393 0.00

7.96 0.27 0.14 20.1 383 358 867 810 1.00 383 358 804 75122.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 285 810 1962 Cored 1961 285 751 1840 85% 0.1622.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 810 1961 751 0.00

4.08 0.27 0.14 22.0 215 201 1082 1011 1.00 215 201 1019 95226.49 0 244 244 0.00 0.00 66.1 22.0 5848 5100 2031 295 1011 2388 Cored 2031 295 952 2267 87% 0.1926.49 0 244 244 0.00 0.00 66.1 22.0 2924 2924 1164 1011 1164 952 0.00

8.02 0.27 0.14 22.0 422 394 1504 1405 1.00 422 394 1441 134734.50 0 325 325 0.00 0.00 88.3 22.0 3905 3905 1555 226 1405 3135 Cored 1555 226 1347 3014 93% 0.2634.50 0 325 325 0.00 0.00 71.3 22.0 3905 3905 1555 1405 1555 1347 0.00

7.01 0.22 0.14 22.0 369 345 1873 1750 1.00 369 345 1811 169241.51 0 386 386 0.00 0.00 84.6 22.0 4632 4632 1844 268 1750 3892 Cored 1844 268 1692 3770 93% 0.3241.51 300 386 257 1.17 0.48 144.4 144.4 2700 2700 1075 1075 1075 1075 0.00

27.98 0.00 0.00 162.3 10870 10157 12744 11907 0.67 7247 6771 9058 846369.49 300 650 433 0.69 0.60 180.2 180.2 2700 2700 1075 156 1075 13975 Plugged 1075 156 1075 10289 98% 0.8869.49 0 650 650 0.00 0.00 176.3 22.0 15590 5100 2031 2031 2031 2031 0.00

6.49 0.27 0.14 22.0 342 319 13086 12227 1.00 342 319 9399 878375.99 0 711 711 0.00 0.00 192.9 22.0 17058 5100 2031 295 2031 15411 Plugged 2031 295 2031 11725 97% 1.00

K.tan��limit

100000 100000

K.tan�

22 5000

INPUT DATA

SANDSTONE S 25220.47 5000

5100

22 5100

22 5100

5100

22 5100

22

35 9.4 5100

0

22

22

22

12

24 220.47

0.47

22

CORAL & SAND

SAND WITH SO

SANDSTONE

SILT WITH SOM

CLAY WITH SO

SAND S

0

35

0

35

510022

5100S

C

CALCULATIONS

MUD

fkPa

9.4

8.630

0

40

0

40

9.4

9.4

22SILTSTONE 510024

35

C

0.47 22

S

S

S

S

22 5100

10000022 5100

35

30

0

10.2

8.6

9.4

9.4 24

5100

5100

0

0.47

100000

5100

12

0.47

0.47

SRD

0

30

qkPa

30

25

30

30

30

SOIL IDENTIFICATION: 3SA




mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 35 0 0 0.00 0.00 0.0 0.0 315 315 0 0 012.19 0.00 0.00 13.1 459 434 459 434 0.67 306 290 459 290

12.19 35 118 79 0.44 0.75 26.2 26.2 315 315 184 22 184 666 Plugged 0.20 8.73 184 22 184 666 97% 0.0512.19 0 118 118 0.00 0.00 41.9 16.6 4730 4730 2773 434 2773 290 0.00

8.53 0.35 0.14 19.3 472 447 932 881 1.00 472 447 932 73620.73 0 224 224 0.00 0.00 79.4 22.0 8964 5100 2990 356 881 2169 Cored 2990 356 736 2024 82% 0.1520.73 165 224 149 1.10 0.49 80.5 80.5 1485 1485 871 871 871 736 0.00

4.57 0.00 0.00 83.4 1095 1035 2026 1916 0.67 730 690 1662 142625.30 165 271 180 0.91 0.52 86.3 86.3 1485 1485 871 104 871 3001 Plugged 871 104 871 2636 96% 0.1925.30 0 271 271 0.00 0.00 95.9 22.0 10829 5100 2990 1916 2990 1426 0.00

3.35 0.35 0.14 22.0 212 200 2238 2116 1.00 212 200 1873 162628.65 0 312 312 0.00 0.00 110.6 22.0 12492 5100 2990 356 2116 4710 Cored 2990 356 1626 3856 91% 0.2828.65 165 312 208 0.79 0.56 92.7 92.7 1485 1485 871 871 871 871 0.00

31.39 0.00 0.00 112.3 10122 9568 12360 11684 0.67 6748 6379 8621 800560.05 165 633 422 0.39 0.80 131.9 131.9 1485 1485 871 104 871 13334 Plugged 871 104 871 9595 99% 0.7060.05 0 633 633 0.00 0.00 178.6 22.0 15181 5100 2990 2990 2990 2990 0.00

11.58 0.28 0.14 22.0 732 692 13092 12375 1.00 732 692 9353 869671.63 0 734 734 0.00 0.00 207.4 22.0 17624 5100 2990 356 2990 16438 Plugged 2990 356 2990 12699 97% 0.9371.63 0 734 734 0.00 0.00 160.9 22.0 7343 5100 2990 2990 2990 2990 0.00

16.15 0.22 0.14 22.0 1020 965 14112 13340 1.00 1020 965 10373 966187.78 0 912 912 0.00 0.00 199.8 22.0 9119 5100 2990 356 2990 17458 Plugged 2990 356 2990 13720 97% 1.00

K.tan��limit

K.tan�

22 5000 100000

22 5100

100000

5100

22

22

22 5100

22 5100

22

fkPa

0

24

10

40

8

40

0CLAY

INPUT DATA

SILT MUDDY

SANDSTONE

C

S

C

0.47

100000

22



SANDSTONE

CLAY

SAND

SILT

0 100000

S

S

C

S

22 5100

22 510012.442

0

42

0 10.2

12.4

9.7

0

36

40

10.2

8.8

11.0

0

0.47

0.47

0

37

0.47

5100

5100

0

37510022

100000

100000


31

25

0

0

5100

CALCULATIONS SRDq

kPa


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

eri

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 02.59 0.19 0.14 1.7 11 10 11 10 1.00 11 10 11 10

2.59 0 18 18 0.00 0.00 2.6 3.4 0 0 0 0 0 11 Plugged 0.20 2.03 0 0 0 11 100% 0.002.59 40 18 12 3.23 0.37 14.7 14.7 356 356 142 10 142 10 0.00

2.01 0.00 0.00 16.2 78 73 88 83 0.67 52 48 62 584.60 40 37 25 1.59 0.45 17.6 17.6 356 356 142 21 83 192 Cored 142 21 58 141 85% 0.024.60 50 37 25 2.00 0.42 20.9 20.9 448 448 178 83 178 58 0.00

1.58 0.00 0.00 21.7 82 77 171 160 0.67 55 51 117 1106.19 50 50 33 1.50 0.45 22.5 22.5 448 448 178 26 160 356 Cored 178 26 110 253 90% 0.046.19 0 50 50 0.00 0.00 11.5 7.0 0 0 0 0 0 0 0.00

2.62 0.23 0.14 8.7 55 51 225 211 1.00 55 51 172 1618.81 0 74 74 0.00 0.00 17.2 10.4 0 0 0 0 0 225 Plugged 0 0 0 172 100% 0.038.81 60 74 50 1.21 0.48 28.6 28.6 539 539 214 211 214 161 0.00

2.68 0.00 0.00 29.9 192 179 417 390 0.67 128 119 300 28011.49 60 98 65 0.92 0.52 31.2 31.2 539 539 214 31 214 663 Plugged 214 31 214 545 94% 0.0911.49 0 98 98 0.00 0.00 22.5 13.7 0 0 0 0 0 0 0.00

9.51 0.23 0.14 17.8 406 379 823 769 1.00 406 379 706 65921.00 0 187 187 0.00 0.00 43.2 22.0 0 0 0 0 0 823 Plugged 0 0 0 706 100% 0.1221.00 0 187 187 0.00 0.00 34.9 22.0 0 0 0 0 0 0 0.00

1.65 0.19 0.14 22.0 87 81 910 850 1.00 87 81 792 74022.65 0 199 199 0.00 0.00 37.1 22.0 0 0 0 0 0 910 Plugged 0 0 0 792 100% 0.1322.65 150 199 133 1.13 0.48 72.7 72.7 1349 1349 537 537 537 537 0.00

7.71 0.00 0.00 76.6 1414 1321 2323 2171 0.67 942 881 1735 162130.36 150 259 173 0.87 0.54 80.5 80.5 1349 1349 537 78 537 2938 Plugged 537 78 537 2350 97% 0.3930.36 300 259 173 1.74 0.44 130.7 130.7 2700 2700 1075 1075 1075 1075 0.00

11.70 0.00 0.00 136.3 3819 3568 6142 5739 0.67 2546 2379 4281 400042.06 300 360 240 1.25 0.47 141.9 141.9 2700 2700 1075 156 1075 7374 Plugged 1075 156 1075 5512 97% 0.9242.06 0 360 360 0.00 0.00 83.2 22.0 0 0 0 0 0 0 0.00

10.06 0.23 0.14 22.0 530 495 6672 6234 1.00 530 495 4810 449552.12 0 447 447 0.00 0.00 103.3 22.0 0 0 0 0 0 6672 Plugged 0 0 0 4810 100% 0.8052.12 0 447 447 0.00 0.00 65.1 22.0 0 0 0 0 0 0 0.00

22.89 0.15 0.14 22.0 1206 1126 7878 7361 1.00 1206 1126 6016 562175.01 0 663 663 0.00 0.00 96.5 22.0 0 0 0 0 0 7878 Plugged 0 0 0 6016 100% 1.00

CALCULATIONS SRD

0

0

qkPa

30

25

0

0

100000

100000

5000

100000

25

0

0.4

5100

5100

0.4

30510022

5100

30

20

100000

100000

51000.4

0.4

0

0.4

0.4

0

00

35

25

8.6

9.4

7.1

7.9

8.6

8.6

9.4

0

35

25

0

9.435

30

0

0 7.9

9.4

7.1

0 100000

S

S

C

S

22 5100

22 5100

S

S

C

C

SILTSTONE/

CLAY

CLAY

SANDSTONE

SAND/SAND

CLAY

SANDSTONE

SILT/SAND



CLAY

INPUT DATA

SAND/SAND

CLAY

C

C

S

0

22

100000

fkPa

100000

22

22

100000

22 5100

22 5100

22

100000

22

22

22 5100

5100

22 5100

22 5100

SOIL IDENTIFICATION: 3SC

K.tan��limit

K.tan�

22 5100

22 5000

22 5100


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.00 0.13 0.13 1.5 17 17 17 17 1.00 17 17 17 17

3.00 0 24 24 0.00 0.00 3.0 3.0 1008 900 966 84 17 118 Cored 0.20 2.66 966 84 17 118 29% 0.013.00 0 24 24 0.00 0.00 4.1 3.4 1008 1008 1082 17 1082 17 0.00

4.20 0.17 0.14 6.0 97 93 114 109 1.00 97 93 114 1097.20 0 62 62 0.00 0.00 10.6 8.7 2596 1900 2039 178 109 401 Cored 2039 178 109 401 56% 0.027.20 70 62 41 1.70 0.44 30.7 30.7 630 630 676 109 676 109 0.00

6.80 0.00 0.00 34.3 892 856 1006 965 0.67 595 571 709 68014.00 70 123 82 0.85 0.54 37.9 37.9 630 630 676 59 676 1742 Plugged 676 59 676 1444 96% 0.0614.00 0 123 123 0.00 0.00 21.0 17.2 2952 1900 2039 965 2039 680 0.00

9.00 0.17 0.14 18.6 641 615 1648 1580 1.00 641 615 1350 129523.00 0 204 204 0.00 0.00 34.9 20.0 4896 1900 2039 178 1580 3406 Cored 2039 178 1295 2823 94% 0.1323.00 200 204 136 1.47 0.45 90.8 90.8 1800 1800 1932 1580 1932 1295 0.00

37.00 0.00 0.00 114.6 16233 15568 17881 17148 0.67 10822 10378 12173 1167360.00 200 574 383 0.52 0.69 138.3 138.3 1800 1800 1932 169 1932 19982 Plugged 1932 169 1932 14273 99% 0.6460.00 500 574 383 1.31 0.47 233.8 233.8 4500 4500 4830 4830 4830 4830 0.00

4.50 0.00 0.00 236.2 4070 3903 21951 21051 0.67 2713 2602 14886 1427564.50 500 621 414 1.21 0.48 238.5 238.5 4500 4500 4830 422 4830 27203 Plugged 4830 422 4830 20138 98% 0.9164.50 0 621 621 0.00 0.00 136.2 67.0 0 0 0 0 0 0 0.00

8.00 0.22 0.14 67.0 2053 1968 24004 23019 1.00 2053 1968 16939 1624472.50 0 701 701 0.00 0.00 153.7 67.0 0 0 0 0 0 24004 Plugged 0 0 0 16939 100% 0.7672.50 0 701 701 0.00 0.00 120.0 50.0 16830 1800 1932 1932 1932 1932 0.00

12.50 0.17 0.14 50.0 2394 2295 26397 25315 1.00 2394 2295 19332 1853985.00 0 826 826 0.00 0.00 141.3 50.0 19830 1800 1932 169 1932 28498 Plugged 1932 169 1932 21433 99% 0.9685.00 0 826 826 0.00 0.00 181.1 50.0 0 0 0 0 0 0 0.00

15.10 0.22 0.14 50.0 2891 2773 29289 28087 1.00 2891 2773 22223 21312100.10 0 977 977 0.00 0.00 214.2 50.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00

0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00

0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00

CALCULATIONS SRD

20

25

qkPa

0

25

0

0

100000

100000

900

1900

15

20

0.47

0

2900

100000

0.47

20190020

100000

0

0

1800

3000

1000000

0

0

0

0.47

0.47

0.4730

0

0

10.0

10.5

10.0

10.0

10.0

0.0

0.0

0

0

25

25

9.025

20

25

0 9.0

9.0

8.0

24

42

42

420 100000

S

C

C

S

0 0

20 1900

C

C

S

Sdense carbonate

dense silty carb

very weak to we

stiff to hard silty

very hard clayey

loose to very de



firm to stiff claye

INPUT DATA

verysoft carbona

very weak to we

C

S

S

0.47

10

20

24

fkPa

0

24

0

24

50

100000

100000

50

0 0

0 0

67

100000

100000

67

0 0

2900

50 1800

50 3000

SOIL IDENTIFICATION: 2G08

K.tan��limit

K.tan�

0 0

10 900

20 1900

24

INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m

mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

� =

pile

fric

tion

angl

e

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r ea

ch la

ye

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r ea

ch la

ye

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.25 0.17 0.14 0.2 0 0 0 0 1.00 0 0 0 0

0.25 0 2 2 0.00 0.00 0.3 0.3 84 84 49 6 0 6 Cored 0.20 0.22 49 6 0 6 4% 0.000.25 0 2 2 0.00 0.00 0.4 0.3 84 84 49 0 49 0 0.00

1.95 0.22 0.14 1.4 8 7 8 7 1.00 8 7 8 72.20 0 18 18 0.00 0.00 3.9 2.5 739 739 433 52 7 67 Cored 433 52 7 67 23% 0.002.20 0 18 18 0.00 0.00 3.0 2.5 422 422 248 7 248 7 0.00

8.80 0.17 0.14 8.0 202 191 210 199 1.00 202 191 210 19911.00 0 97 97 0.00 0.00 16.6 13.6 2323 2323 1362 162 199 571 Cored 1362 162 199 571 72% 0.0411.00 0 97 97 0.00 0.00 12.2 12.2 774 774 454 199 454 199 0.00

4.00 0.13 0.13 14.5 166 157 376 356 1.00 166 157 376 35615.00 0 133 133 0.00 0.00 16.7 16.7 1062 1062 623 74 356 806 Cored 623 74 356 806 91% 0.0615.00 30 133 89 0.34 0.86 25.8 25.8 270 270 158 158 158 158 0.00

3.50 0.00 0.00 27.2 274 259 650 614 0.67 182 172 559 52818.50 30 164 110 0.27 0.96 28.7 28.7 270 270 158 19 158 827 Plugged 158 19 158 736 97% 0.0518.50 0 164 164 0.00 0.00 28.1 22.0 3943 3943 2312 614 2312 528 0.00

3.50 0.17 0.14 22.0 221 209 871 823 1.00 221 209 780 73722.00 0 191 191 0.00 0.00 32.6 22.0 4573 4573 2681 319 823 2013 Cored 2681 319 737 1836 83% 0.1322.00 70 191 127 0.55 0.67 47.1 47.1 630 630 369 369 369 369 0.00

11.40 0.00 0.00 52.8 1729 1634 2600 2457 0.67 1153 1090 1932 182733.40 70 293 195 0.36 0.84 58.5 58.5 630 630 369 44 369 3013 Plugged 369 44 369 2346 98% 0.1733.40 0 293 293 0.00 0.00 50.1 22.0 7036 5100 2990 2457 2990 1827 0.00

2.30 0.17 0.14 22.0 145 137 2745 2595 1.00 145 137 2078 196435.70 0 314 314 0.00 0.00 53.7 22.0 7532 5100 2990 356 2595 5696 Cored 2990 356 1964 4397 92% 0.3235.70 0 314 314 0.00 0.00 53.7 22.0 12554 5100 2990 2595 2990 1964 0.00

10.30 0.17 0.14 22.0 651 615 3396 3210 1.00 651 615 2728 257946.00 0 407 407 0.00 0.00 69.5 22.0 16262 5100 2990 356 2990 6742 Plugged 2990 356 2579 5663 94% 0.4146.00 150 407 271 0.55 0.67 100.8 100.8 1350 1350 792 792 792 792 0.00

18.00 0.00 0.00 110.5 5711 5398 9106 8608 0.67 3807 3599 6535 617864.00 150 578 385 0.39 0.80 120.2 120.2 1350 1350 792 94 792 9992 Plugged 792 94 792 7421 99% 0.5464.00 350 578 385 0.91 0.52 183.5 183.5 3150 3150 1847 1847 1847 1847 0.00

14.00 0.00 0.00 194.1 7802 7375 16908 15983 0.67 5201 4917 11736 1109478.00 350 718 478 0.73 0.58 204.6 204.6 3150 3150 1847 220 1847 18975 Plugged 1847 220 1847 13803 98% 1.0078.00 0 718 718 0.00 0.00 157.3 22.0 17221 5100 2990 2990 2990 2990 0.00

4.50 0.22 0.14 22.0 284 269 17192 16252 1.00 284 269 12021 1136382.50 0 758 758 0.00 0.00 166.1 22.0 18193 5100 2990 356 2990 20538 Plugged 2990 356 2990 15367 98% 1.1182.50 0 758 758 0.00 0.00 129.7 22.0 6064 5100 2990 2990 2990 2990 0.00

17.90 0.17 0.14 22.0 1131 1069 18323 17321 1.00 1131 1069 13151 12432100.40 0 937 937 0.00 0.00 160.3 22.0 7496 5100 2990 356 2990 21669 Plugged 2990 356 2990 16498 98% 1.20

CALCULATIONS SRD

20

20

qkPa

20

25

20

0

5100

100000

5000

5100

20

25

0.47

40

100000

5100

0.47

15510022

100000

0

0

5100

5100

1000000

0

0

0.47

0

0.47

0.4725

0

0

9.0

7.5

9.0

9.0

9.0

9.5

10.0

0

25

0

25

9.020

25

30

25 9.0

8.0

8.0

8

42

42

240.47 22

C

S

C

S

22 5100

22 5100

C

C

S

S

CLAY, hard silty

SILT, dense car

CALCARENITE,

CLAY, stiff to ve

SILT, loose to m

SILT, soft carbo

SAND, medium

SILT, firm carbo

SAND, loose to

INPUT DATA

CORAL, Weak t

GRAVEL, Loose

S

S

S

0.47

22

22

0

fkPa

0

24

0

24

22

100000

100000

22

100000

22

22 5100

22 5100

22

22 5100

5100

22 5100

22 5100

K.tan��limit

K.tan�

22 5100

22 5000

22 5100

0

100000

5100SAND, dense ca S 30 9.0

22

0.47 24 22

25 10.0 0.47 8


5100 22 5100 20

22 5100 25

SILT, dense car S




mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.80 0.13 0.13 1.0 5 5 5 5 1.00 5 5 5 5

1.80 0 15 15 0.00 0.00 1.9 1.9 122 122 72 9 5 18 Cored 0.20 1.69 72 9 5 18 53% 0.001.80 0 15 15 0.00 0.00 2.6 2.1 184 184 108 5 108 5 0.00

4.20 0.17 0.14 4.8 58 55 63 59 1.00 58 55 63 596.00 0 53 53 0.00 0.00 9.1 7.4 637 637 374 44 59 167 Cored 374 44 59 167 73% 0.016.00 20 53 35 0.56 0.67 13.3 13.3 180 180 106 59 106 59 0.00

6.00 0.00 0.00 16.1 277 262 340 321 0.67 185 175 248 23412.00 20 107 71 0.28 0.94 18.9 18.9 180 180 106 13 106 458 Plugged 106 13 106 366 97% 0.0212.00 0 107 107 0.00 0.00 23.5 15.0 1285 1285 754 321 754 234 0.00

3.00 0.22 0.14 16.7 144 136 484 457 1.00 144 136 391 37015.00 0 131 131 0.00 0.00 28.7 18.4 1573 1573 922 110 457 1051 Cored 922 110 370 871 87% 0.0515.00 0 131 131 0.00 0.00 22.4 18.4 2622 2622 1537 457 1537 370 0.00

5.50 0.17 0.14 21.8 345 326 828 783 1.00 345 326 736 69620.50 0 181 181 0.00 0.00 30.9 25.3 3612 2900 1700 202 783 1814 Cored 1700 202 696 1634 88% 0.1020.50 150 181 120 1.25 0.47 71.0 71.0 1350 1350 792 783 792 696 0.00

13.50 0.00 0.00 78.9 3060 2893 3889 3676 0.67 2040 1929 2776 262434.00 150 302 201 0.74 0.58 86.9 86.9 1350 1350 792 94 792 4774 Plugged 792 94 792 3662 97% 0.2234.00 200 302 201 0.99 0.50 100.3 100.3 1800 1800 1055 1055 1055 1055 0.00

3.00 0.00 0.00 102.7 884 836 4773 4512 0.67 590 557 3366 318237.00 200 331 220 0.91 0.52 105.0 105.0 1800 1800 1055 126 1055 5954 Plugged 1055 126 1055 4547 97% 0.2837.00 90 331 220 0.41 0.78 70.4 70.4 810 810 475 475 475 475 0.00

12.00 0.00 0.00 76.3 2630 2486 7403 6998 0.67 1753 1657 5119 483949.00 90 451 300 0.30 0.91 82.2 82.2 810 810 475 57 475 7934 Plugged 475 57 475 5650 99% 0.3549.00 150 451 300 0.50 0.71 106.1 106.1 1350 1350 792 792 792 792 0.00

14.50 0.00 0.00 123.1 5125 4845 12528 11842 0.67 3417 3230 8535 806863.50 200 588 392 0.51 0.70 140.0 140.0 1800 1800 1055 126 1055 13709 Plugged 1055 126 1055 9716 99% 0.6063.50 0 588 588 0.00 0.00 159.7 82.4 23534 9600 5628 5628 5628 5628 0.00

6.00 0.27 0.14 86.4 1488 1406 14015 13249 1.00 1488 1406 10023 947569.50 0 645 645 0.00 0.00 175.1 90.3 25814 9600 5628 670 5628 20314 Plugged 5628 670 5628 16322 96% 1.0069.50 0 645 645 0.00 0.00 110.4 48.0 5163 1900 1114 1114 1114 1114 0.00

18.50 0.17 0.14 48.0 2550 2410 16565 15659 1.00 2550 2410 12573 1188588.00 0 821 821 0.00 0.00 140.5 48.0 6569 1900 1114 133 1114 17812 Plugged 1114 133 1114 13820 99% 0.8588.00 0 821 821 0.00 0.00 103.4 67.0 9853 2900 1700 1700 1700 1700 0.00

12.30 0.13 0.13 67.0 2366 2237 18932 17896 1.00 2366 2237 14939 14122100.30 0 938 938 0.00 0.00 118.1 67.0 11255 2900 1700 202 1700 20834 Plugged 1700 202 1700 16842 99% 1.03

1567 2900 67 2900medium dense t S 20 9.5 0.47 12

CALCULATIONS SRD

0

0

qkPa

0

25

0

20

100000

2900

1900

2900

15

20

0.47

0

100000

100000100000

100000

100000

81 2900

0

0.47

2529006712

8

12

00 100000

1900

30

20

100000

100000

96000.47

0.47

0.47

0

0

0

00

35

25

9.0

9.0

9.5

10.0

9.5

9.5

9.5

25

0

0

0

8.030

20

25

0 9.0

9.0

8.5

C

C

S

S

0 0

67 2900

S

S

C

C

dense carbonate

stiff carbonate s

very stiff to hard

dense siliceous f

dense silty carbo

silty fine to coars

stiff to very stiff c

very stiff to hard



very soft to soft c

INPUT DATA

very loose carbo

loose to dense c

C

S

S

0.47

48

67

8

fkPa

20

0

0

0

100000

96

48

81

0

0

48 1900

0

0 0

0 0


K.tan��limit

K.tan�

96 9600

48 1900

67 2900

40


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

eri

Shaf

t out

er fr

ictio

n fo

r eac

h la

y e

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.22 0.14 1.3 3 3 3 3 1.00 3 3 3 3

1.00 0 12 12 0.00 0.00 1.7 2.6 480 480 191 28 3 34 Cored 0.20 1.33 191 28 3 34 18% 0.001.00 0 12 12 0.00 0.00 1.5 1.5 288 288 115 3 115 3 0.00

8.00 0.13 0.13 6.0 116 108 119 111 1.00 116 108 119 1119.00 0 84 84 0.00 0.00 10.6 10.6 2016 2016 803 117 111 347 Cored 803 117 111 347 66% 0.049.00 0 84 84 0.00 0.00 14.4 11.8 1680 1680 669 111 669 111 0.00

12.00 0.17 0.14 16.9 485 453 604 564 1.00 485 453 604 56421.00 0 180 180 0.00 0.00 30.8 22.0 3600 3600 1433 208 564 1376 Cored 1433 208 564 1376 85% 0.1421.00 175 180 120 1.46 0.45 79.6 79.6 1575 1575 627 564 627 564 0.00

28.00 0.00 0.00 95.9 6431 6009 7034 6573 0.67 4287 4006 4891 457049.00 175 432 288 0.61 0.64 112.2 112.2 1575 1575 627 91 627 7753 Plugged 627 91 627 5609 98% 0.5849.00 0 432 432 0.00 0.00 94.7 22.0 17280 5100 2031 2031 2031 2031 0.00

1.00 0.22 0.14 22.0 53 49 7087 6622 1.00 53 49 4944 461950.00 0 442 442 0.00 0.00 96.9 22.0 17680 5100 2031 295 2031 9413 Plugged 2031 295 2031 7269 96% 0.7550.00 300 442 295 1.02 0.50 149.3 149.3 2700 2700 1075 1075 1075 1075 0.00

4.50 0.00 0.00 152.7 1645 1537 8732 8159 0.67 1097 1025 6040 564454.50 300 487 325 0.92 0.52 156.0 156.0 2700 2700 1075 156 1075 9963 Plugged 1075 156 1075 7271 98% 0.7554.50 0 487 487 0.00 0.00 106.7 22.0 11688 5100 2031 2031 2031 2031 0.00

6.50 0.22 0.14 22.0 342 320 9074 8479 1.00 342 320 6382 596461.00 0 552 552 0.00 0.00 121.0 22.0 13248 5100 2031 295 2031 11400 Plugged 2031 295 2031 8708 97% 0.8961.00 0 552 552 0.00 0.00 94.4 22.0 4416 4416 1758 1758 1758 1758 0.00

18.00 0.17 0.14 22.0 948 886 10022 9365 1.00 948 886 7330 684979.00 0 732 732 0.00 0.00 125.2 22.0 5856 5100 2031 295 2031 12348 Plugged 2031 295 2031 9656 97% 0.9979.00 0 732 732 0.00 0.00 160.4 22.0 17568 5100 2031 2031 2031 2031 0.00

1.80 0.22 0.14 22.0 95 89 10117 9453 1.00 95 89 7425 693880.80 0 750 750 0.00 0.00 164.4 22.0 18000 5100 2031 295 2031 12443 Plugged 2031 295 2031 9751 97% 1.00

0

0.47

SRD

20

25

qkPa

0

25

20

25

10.0 24

5100

100000

0.47

0.47

5100

5100

24

00

30

25

10.0

10.0

10.0

30

S

0.47 22

S

C

S

C

22 5100

Very Stiff Calcareous CLAY 510000 9.0 0

8.0

0.47

Silty Carbonate SAND

fkPa

9.0

12.030

150.47

22

22

0.47 40 22 5100

20

25S

S

22

very Weak CALCARENITE

Crystalline GYPSUM

Hard Silty Calcareous CLAY

Medium Dense Silica SAND

Dense carbonate SILT

Dense Carbonate SAND S

30 10.0 5100

8

22

100000

22

0

2222 5100

22 5100

22 5100

5100

22 5100

22

22

K.tan�

22 5000

INPUT DATA

CAPROCK S 255000

CALCULATIONS

40

100000100000

5100

K.tan��limit

510024

20

SOIL IDENTIFICATION: 3D07




mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 012.30 0.17 0.14 9.9 466 441 466 441 1.00 466 441 466 441

12.30 0 116 116 0.00 0.00 16.2 19.8 1387 1387 1454 166 441 1073 Cored 0.20 12.80 1454 166 441 1073 85% 0.0512.30 0 116 116 0.00 0.00 25.3 16.2 2312 2312 2423 441 2423 441 0.00

0.90 0.22 0.14 16.8 58 55 524 496 1.00 58 55 524 49613.20 0 125 125 0.00 0.00 27.4 17.5 2500 2500 2619 298 496 1319 Cored 2619 298 496 1319 77% 0.0613.20 0 125 125 0.00 0.00 21.4 17.5 1500 1500 1571 496 1571 496 0.00

7.60 0.17 0.14 19.7 575 545 1099 1041 1.00 575 545 1099 104120.80 0 204 204 0.00 0.00 34.9 22.0 2448 2448 2565 292 1041 2432 Cored 2565 292 1041 2432 88% 0.1120.80 118 204 136 0.87 0.54 63.3 63.3 1062 1062 1113 1041 1113 1041 0.00

1.40 0.00 0.00 65.0 348 330 1447 1371 0.67 232 220 1331 126122.20 123 216 144 0.85 0.54 66.6 66.6 1107 1107 1160 132 1160 2739 Plugged 1160 132 1160 2623 95% 0.1222.20 0 216 216 0.00 0.00 37.0 22.0 2598 2598 2722 1371 2722 1261 0.00

2.20 0.17 0.14 22.0 185 176 1632 1547 1.00 185 176 1516 143724.40 0 239 239 0.00 0.00 40.9 22.0 2872 2872 3009 343 1547 3522 Cored 3009 343 1437 3296 90% 0.1524.40 131 239 160 0.82 0.55 72.3 72.3 1179 1179 1235 1235 1235 1235 0.00

24.00 0.00 0.00 99.5 9150 8669 10782 10216 0.67 6100 5779 7616 721648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 229 2009 13019 Plugged 2009 229 2009 9853 98% 0.4648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 2009 2009 2009 0.00

17.60 0.00 0.00 137.0 9231 8746 20013 18962 0.67 6154 5831 13770 1304766.00 213 610 406 0.52 0.69 147.1 147.1 1917 1917 2009 229 2009 22250 Plugged 2009 229 2009 16007 99% 0.7566.00 0 610 610 0.00 0.00 133.6 22.0 12192 5100 5343 5343 5343 5343 0.00

5.50 0.22 0.14 22.0 463 439 20476 19401 1.00 463 439 14233 1348671.50 0 666 666 0.00 0.00 146.0 22.0 13325 5100 5343 609 5343 26429 Plugged 5343 609 5343 20186 97% 0.9571.50 0 666 666 0.00 0.00 114.0 22.0 7995 5100 5343 5343 5343 5343 0.00

13.00 0.17 0.14 22.0 1095 1038 21572 20439 1.00 1095 1038 15329 1452484.50 0 804 804 0.00 0.00 137.5 22.0 9649 5100 5343 609 5343 27524 Plugged 5343 609 5343 21281 97% 1.0084.50 0 804 804 0.00 0.00 176.2 22.0 16081 5100 5343 5343 5343 5343 0.00

9.20 0.22 0.14 22.0 775 734 22347 21174 1.00 775 734 16104 1525893.70 0 877 877 0.00 0.00 192.1 22.0 17535 5100 5343 609 5343 28299 Plugged 5343 609 5343 22056 97% 1.0493.70 140 877 584 0.24 1.00 140.0 140.0 1260 1260 1320 1320 1320 1320 0.00

0.80 0.00 0.00 140.0 429 406 22776 21580 0.67 286 271 16390 1552994.50 140 884 589 0.24 1.00 140.0 140.0 1260 1260 1320 150 1320 24246 Plugged 1320 150 1320 17860 99% 0.84

08.90

510022207.9 0.4730

CALCULATIONS SRD

25

20

qkPa

0

25

20

20

5100

5100

5000

5100

20

25

0

12

100000

100000

0.47

0

0

0.47

0.47

100000

0.47

01000001000000

12

20

120.47 22

100000

25

0

5100

5100

5100

0

0

25

10.4

8.9

8.9

10.3

10.6

25

0

0

30

8.90

25

30

25 10.4

10.4

9.4

C

C

S

C

S

S

S

CARBONATE CLAY

CARBONATE SAND

CARBONATE SILT

CARBONATE SILTSTONE

C

CARBONATE CLAY

CARBONATE SANDSTONE

CALCAREOUS CLAY

CALCAREOUS CLAY

CARBONATE SANDSTONE

INPUT DATA

CARBONATE SIL

CARBONATE SANDSTONE

S

S

S

0.47

22

22

fkPa

12

0

K.tan�

22 5000

22 5100

22

20

22

22

10000022 5100

22 5100

100000

22

22 5100

22 5100

22 5100

22 5100

K.tan��limit

22 5100

22 5100

SOIL IDENTIFICATION: 32A




mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.49 0.23 0.14 1.8 6 6 6 6 1.00 6 6 6 6

1.49 0 16 16 0.00 0.00 2.2 3.6 652 652 260 38 6 50 Cored 0.20 1.72 260 38 6 50 25% 0.011.49 0 16 16 0.00 0.00 3.6 2.2 652 652 260 6 260 6 0.00

1.31 0.23 0.14 3.1 10 9 16 15 1.00 10 9 16 152.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 71 15 102 Cored 488 71 15 102 31% 0.012.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 15 488 15 0.00

1.01 0.23 0.14 4.8 12 11 28 26 1.00 12 11 28 263.81 0 40 40 0.00 0.00 9.1 5.5 1664 1664 663 96 26 150 Cored 663 96 26 150 36% 0.023.81 0 40 40 0.00 0.00 7.9 5.5 951 951 379 26 379 26 0.00

8.50 0.20 0.14 10.8 220 205 247 231 1.00 220 205 247 23112.31 0 114 114 0.00 0.00 22.8 16.0 2747 2747 1094 159 231 637 Cored 1094 159 231 637 75% 0.0712.31 96 114 76 1.25 0.47 45.2 45.2 862 862 343 231 343 231 0.00

5.18 0.00 0.00 64.3 797 745 1044 976 0.67 531 496 779 72817.50 192 164 109 1.75 0.43 83.3 83.3 1724 1724 686 100 686 1830 Plugged 686 100 686 1565 94% 0.1617.50 0 164 164 0.00 0.00 32.8 22.0 3941 3941 1569 976 1569 728 0.00

6.89 0.20 0.14 22.0 363 339 1407 1315 1.00 363 339 1141 106724.38 0 230 230 0.00 0.00 46.0 22.0 5528 5100 2031 295 1315 3017 Cored 2031 295 1067 2503 88% 0.2624.38 144 230 154 0.94 0.52 74.3 74.3 1293 1293 515 515 515 515 0.00

3.05 0.00 0.00 76.6 559 522 1966 1837 0.67 373 348 1514 141527.43 144 260 173 0.83 0.55 78.9 78.9 1296 1296 516 75 516 2557 Plugged 516 75 516 2105 96% 0.2227.43 0 260 260 0.00 0.00 51.8 22.0 6230 5100 2031 1837 2031 1415 0.00

7.32 0.20 0.14 22.0 385 360 2351 2197 1.00 385 360 1899 177534.75 0 336 336 0.00 0.00 67.0 22.0 8056 5100 2031 295 2031 4677 Plugged 2031 295 1775 3969 93% 0.4134.75 192 336 224 0.86 0.54 103.5 103.5 1724 1724 686 686 686 686 0.00

23.26 0.00 0.00 126.4 7038 6577 9390 8774 0.67 4692 4384 6592 615958.00 239 559 373 0.64 0.62 149.3 149.3 2155 2155 858 125 858 10372 Plugged 858 125 858 7574 98% 0.7858.00 0 559 559 0.00 0.00 111.5 22.0 13414 5100 2031 2031 2031 2031 0.00

4.00 0.20 0.14 22.0 210 197 9600 8970 1.00 210 197 6802 635662.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 295 2031 11926 Plugged 2031 295 2031 9128 97% 0.9462.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 2031 2031 2031 0.00

11.00 0.20 0.14 22.0 579 541 10179 9511 1.00 579 541 7381 689773.00 0 685 685 0.00 0.00 136.7 22.0 16447 5100 2031 295 2031 12505 Plugged 2031 295 2031 9707 97% 1.00

K.tan��limit

K.tan�

22 5100

22 5000

22 5100

24

100000

22 5100

5100

22 5100

22 5100

22

100000

22

22 5100

22 5100

22

24

fkPa

0

24

0

24

100000

22

22

LIMESTONE

INPUT DATA

LIMESTONE

CARBONATE SAND CEMENTED

S

S

S

0.47

22

22

CARBONATE SILTY SAND

CARBONATE CLAY


SILTY CARBONATE CLAY

CARBONATE SILT

CARBONATE SAND

SILTY CALCAREOUS CLAY

CEMENTED CARBONATE SAND S

S

C

S

0.47 22

C

S

C

S

22 5100

22 510024

42

42

42

8.828

31

31

31 10.4

10.4

10.4

0

28

0

28

0

28

28

9.6

9.6

9.6

10.4

9.6

9.6

8.0

0.47

0.47

0

0.47

0

0.47

0

5100

23

23

5100

100000

5100

26

26

0.47

0

100000

5100

0.47

23510022

5100

100000

5000

5100

CALCULATIONS SRD

23

0

qkPa

23

25

26

0

SOIL IDENTIFICATION: 3




mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 09.12 0.22 0.14 9.3 243 230 243 230 1.00 243 230 243 230

9.12 0 85 85 0.00 0.00 11.9 18.6 2036 2036 1193 142 230 616 Cored 0.20 9.39 1193 142 230 616 77% 0.059.12 72 85 57 1.27 0.47 33.9 33.9 648 648 380 230 380 230 0.00

4.00 0.00 0.00 55.4 636 602 880 832 0.67 424 401 668 63113.12 190 123 82 2.33 0.40 76.9 76.9 1710 1710 1003 119 832 1831 Cored 1003 119 631 1418 92% 0.1013.12 0 123 123 0.00 0.00 33.3 17.2 2941 2941 1724 832 1724 631 0.00

8.00 0.27 0.14 19.6 450 425 1330 1257 1.00 450 425 1117 105621.12 0 198 198 0.00 0.00 53.7 22.0 4751 4751 2786 332 1257 2918 Cored 2786 332 1056 2505 87% 0.1821.12 140 198 132 1.06 0.49 69.0 69.0 1260 1260 739 739 739 739 0.00

5.04 0.00 0.00 72.3 1047 990 2376 2246 0.67 698 660 1815 171626.16 140 246 164 0.86 0.54 75.7 75.7 1260 1260 739 88 739 3203 Plugged 739 88 739 2642 97% 0.1926.16 0 246 246 0.00 0.00 66.6 22.0 5892 5100 2990 2246 2990 1716 0.00

6.48 0.27 0.14 22.0 409 387 2786 2633 1.00 409 387 2225 210332.64 0 312 312 0.00 0.00 84.6 22.0 7480 5100 2990 356 2633 5775 Cored 2990 356 2103 4684 92% 0.3432.64 120 312 208 0.58 0.66 79.0 79.0 1080 1080 633 633 633 633 0.00

10.36 0.00 0.00 96.4 2868 2711 5653 5344 0.67 1912 1807 4136 391043.00 190 409 273 0.70 0.60 113.9 113.9 1710 1710 1003 119 1003 6775 Plugged 1003 119 1003 5258 98% 0.3943.00 120 409 273 0.44 0.75 90.5 90.5 1080 1080 633 633 633 633 0.00

18.50 0.00 0.00 121.6 6462 6109 12116 11453 0.67 4308 4072 8445 798361.50 240 584 389 0.62 0.64 152.8 152.8 2160 2160 1266 151 1266 13533 Plugged 1266 151 1266 9862 98% 0.7261.50 0 584 584 0.00 0.00 158.4 22.0 14012 5100 2990 2990 2990 2990 0.00

4.05 0.27 0.14 22.0 256 242 12371 11695 1.00 256 242 8700 822465.55 0 622 622 0.00 0.00 168.8 22.0 14928 5100 2990 356 2990 15718 Plugged 2990 356 2990 12047 97% 0.8865.55 0 622 622 0.00 0.00 78.3 22.0 9952 5100 2990 2990 2990 2990 0.00

25.46 0.13 0.13 22.0 1608 1520 13980 13215 1.00 1608 1520 10309 974591.01 0 822 822 0.00 0.00 103.5 22.0 13150 5100 2990 356 2990 17326 Plugged 2990 356 2990 13655 97% 1.00

K.tan��limit

INPUT DATA

1000008 510022

0.47 22 5000

SAND

SAND

CLAY

S

C

S

0



SOIL IDENTIFICATION: 3J07

CLAY

SAND

CLAY C

S

C

240.47 22

CLAY

S

SSAND

SILTY SAND

C

22 5100

9.40

30

0

35 9.4

9.4

9.3

20

10.2

9.4

9.4

9.4

7.9

35

0

0

35

0

0.47

0.47

100000

16

8

22 5100

22 5100

24

SRDq

kPa

5000 25

f

22

kPaK.tan�

0

CALCULATIONS

5100

305100

100000

100000 00

2224

8

10000022 5100

220.47

0

5100

8

25

022 5100 100000

30

100000

100000

24

22 15

305100

510022 5100

22


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.17 0.14 0.7 3 2 3 2 1.00 3 2 3 2

1.00 0 8 8 0.00 0.00 1.1 1.4 320 320 335 38 2 43 Cored 0.20 0.89 335 38 2 43 12% 0.001.00 0 8 8 0.00 0.00 1.0 1.0 64 64 67 2 67 2 0.00

6.50 0.13 0.13 4.7 117 111 119 113 1.00 117 111 119 1137.50 0 67 67 0.00 0.00 8.4 8.4 532 532 557 63 113 296 Cored 557 63 113 296 79% 0.027.50 75 67 44 1.69 0.44 32.9 32.9 675 675 707 113 707 113 0.00

1.70 0.00 0.00 33.8 220 208 339 322 0.67 147 139 266 2529.20 75 83 55 1.36 0.46 34.7 34.7 675 675 707 81 322 742 Cored 707 81 252 599 87% 0.039.20 0 83 83 0.00 0.00 18.1 11.6 1984 1984 2078 322 2078 252 0.00

2.60 0.22 0.14 13.2 132 125 471 446 1.00 132 125 398 37711.80 0 106 106 0.00 0.00 23.2 14.8 2545 2545 2667 304 446 1221 Cored 2667 304 377 1078 72% 0.0611.80 75 106 71 1.06 0.49 37.0 37.0 675 675 707 446 707 377 0.00

2.10 0.00 0.00 38.1 306 290 777 736 0.67 204 193 602 57013.90 75 123 82 0.92 0.52 39.2 39.2 675 675 707 81 707 1565 Plugged 707 81 570 1252 94% 0.0713.90 0 123 123 0.00 0.00 21.0 17.2 4914 4914 5149 736 5149 570 0.00

1.10 0.17 0.14 17.9 75 71 852 808 1.00 75 71 677 64215.00 0 133 133 0.00 0.00 22.7 18.6 5310 5100 5343 609 808 2269 Cored 5343 609 642 1927 68% 0.1115.00 75 133 89 0.85 0.54 40.7 40.7 675 675 707 707 707 642 0.00

3.00 0.00 0.00 42.7 491 465 1343 1273 0.67 327 310 1004 95118.00 75 160 107 0.70 0.60 44.7 44.7 675 675 707 81 707 2131 Plugged 707 81 707 1792 96% 0.1018.00 0 160 160 0.00 0.00 27.3 22.0 1278 1278 1339 1273 1339 951 0.00

1.10 0.17 0.14 22.0 93 88 1436 1360 1.00 93 88 1097 103919.10 0 170 170 0.00 0.00 29.0 22.0 1357 1357 1422 162 1360 2958 Cored 1422 162 1039 2298 93% 0.1319.10 0 170 170 0.00 0.00 29.0 22.0 6786 5100 5343 1360 5343 1039 0.00

2.40 0.17 0.14 22.0 202 192 1638 1552 1.00 202 192 1299 123121.50 0 191 191 0.00 0.00 32.7 22.0 7650 5100 5343 609 1552 3799 Cored 5343 609 1231 3139 81% 0.1821.50 0 191 191 0.00 0.00 32.7 22.0 4590 4590 4809 1552 4809 1231 0.00

3.00 0.17 0.14 22.0 253 239 1891 1792 1.00 253 239 1552 147024.50 0 218 218 0.00 0.00 37.3 22.0 5238 5100 5343 609 1792 4291 Cored 5343 609 1470 3631 83% 0.2124.50 130 218 146 0.89 0.53 68.8 68.8 1170 1170 1226 1226 1226 1226 0.00

14.00 0.00 0.00 78.0 4182 3962 6073 5754 0.67 2788 2642 4340 411238.50 130 351 234 0.56 0.67 87.2 87.2 1170 1170 1226 140 1226 7438 Plugged 1226 140 1226 5705 98% 0.3338.50 0 351 351 0.00 0.00 77.0 22.0 8430 5100 5343 5343 5343 5343 0.00

8.50 0.22 0.14 22.0 716 679 6789 6433 1.00 716 679 5056 479147.00 0 419 419 0.00 0.00 91.9 22.0 10062 5100 5343 609 5343 12741 Plugged 5343 609 4791 10455 94% 0.6147.00 0 419 419 0.00 0.00 71.7 22.0 16770 5100 5343 5343 5343 4791 0.00

1.10 0.17 0.14 22.0 93 88 6882 6520 1.00 93 88 5149 487848.10 0 429 429 0.00 0.00 73.4 22.0 17166 5100 5343 609 5343 12834 Plugged 5343 609 4878 10636 94% 0.6248.10 0 429 429 0.00 0.00 73.4 22.0 3433 3433 3597 3597 3597 3597 0.00

41.90 0.17 0.14 22.0 3530 3345 10412 9865 1.00 3530 3345 8679 822390.00 0 827 827 0.00 0.00 141.5 22.0 6618 5100 5343 609 5343 16364 Plugged 5343 609 5343 14631 96% 0.8590.00 300 827 551 0.54 0.68 203.4 203.4 2700 2700 2829 2829 2829 2829 0.00

10.20 0.00 0.00 209.2 8170 7741 18582 17606 0.67 5447 5161 14126 13384100.20 300 924 616 0.49 0.72 215.0 215.0 2700 2700 2829 322 2829 21733 Plugged 2829 322 2829 17277 98% 1.00

SOIL IDENTIFICATION: 3M05

K.tan��limit

K.tan�

22

10000022 5100

22 5100

5100

8

22 5100

5100

22 5100

22 5100

510022 22

100000

22

100000

22

stiff carbonate cl

INPUT DATA

CALCARENITE,

loose to medium

C

S

S

0.47

22

22



dense carbonate

firm to stiff carbo

CALCARENITE,

stiff carbonate C

stiff calcareous s

dense carbonate

CALCARENITE,

medium dense c S

C

S

S

0

8

40

0

8

24

40

C

S

C

S

9.0

8.0

0

30

25

20

0

9.0

0

25

25

9.5

25

0

8.0

9.0

9.0

9.0

9.0

9.0

9.5

25

0.47

0

0

0.47

0

0.47

0.47

100000

20

0

5100

5100

5100

20

20

0.47

25510022

0 10000022 5100

220.47

100000

5100

22

100000

5100

5100

20

15

CALCULATIONS SRDq

kPa

fkPa

24

40

8

0

0

510022

510022

100000

25

20

medium dense t S 30 8.0 0.47 24 22 5100 22 5100 25

CALCARENITE, S 25 9.0 0.47 40 22 5100 22 5100 20

dense carbonate S 25 9.5 0.47 8 22 5100 22 5100 20

hard CLAY C 0 9.5 0 0 22 5100 100000 100000 0


mm

Depth Soil type � � K Nq c flim qlim flim qlim � s v p o ��c/po' � Q f Qp Qu Sunc Q f Qp Qu


Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

re

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssu

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

er

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r =

Shaf

t out

er fr

ictio

n fo

r eac

h la

y e

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.20 0.17 0.14 0.1 0 0 0 0 1.00 0 0 0 0

0.20 0 2 2 0.00 0.00 0.2 0.3 19 19 21 2 0 2 Cored 0.20 0.18 21 2 0 2 10% 0.000.20 0 2 2 0.00 0.00 0.3 0.2 19 19 21 0 21 0 0.00

0.70 0.17 0.14 0.6 2 2 2 2 1.00 2 2 2 20.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 8 2 12 Cored 93 8 2 12 30% 0.000.90 0 7 7 0.00 0.00 1.2 1.0 86 86 93 2 93 2 0.00

6.10 0.17 0.14 4.6 108 104 110 106 1.00 108 104 110 1067.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 66 106 282 Cored 761 66 106 282 76% 0.027.00 0 59 59 0.00 0.00 10.1 8.3 709 709 761 106 761 106 0.00

3.00 0.17 0.14 9.9 114 110 224 215 1.00 114 110 224 21510.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 93 215 533 Cored 1070 93 215 533 82% 0.0410.00 0 83 83 0.00 0.00 14.2 11.6 997 997 1070 215 1070 215 0.00

1.50 0.17 0.14 12.5 72 69 296 284 1.00 72 69 296 28411.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 108 284 688 Cored 1234 108 284 688 84% 0.0511.50 0 96 96 0.00 0.00 16.4 13.4 1150 1150 1234 284 1234 284 0.00

3.50 0.17 0.14 15.5 208 199 504 483 1.00 208 199 504 48315.00 0 126 126 0.00 0.00 21.5 17.6 1507 1507 1617 141 483 1129 Cored 1617 141 483 1129 87% 0.0915.00 0 126 126 0.00 0.00 27.5 17.6 1507 1507 1617 483 1617 483 0.00

6.40 0.22 0.14 19.8 485 465 989 948 1.00 485 465 989 94821.40 0 177 177 0.00 0.00 38.7 22.0 2121 2121 2276 199 948 2136 Cored 2276 199 948 2136 91% 0.1721.40 150 177 118 1.27 0.47 70.6 70.6 1350 1350 1449 948 1449 948 0.00

15.60 0.00 0.00 79.3 4736 4542 5725 5490 0.67 3157 3028 4146 397637.00 150 309 206 0.73 0.59 87.9 87.9 1350 1350 1449 127 1449 7301 Plugged 1449 127 1449 5722 98% 0.4437.00 0 309 309 0.00 0.00 52.9 22.0 3712 3712 3984 3984 3984 3976 0.00

1.00 0.17 0.14 22.0 84 81 5809 5571 1.00 84 81 4231 405738.00 0 319 319 0.00 0.00 54.6 22.0 3827 3827 4108 359 4108 10276 Plugged 4108 359 4057 8647 96% 0.6738.00 150 319 213 0.71 0.60 89.3 89.3 1350 1350 1449 1449 1449 1449 0.00

10.50 0.00 0.00 95.9 3855 3697 9664 9268 0.67 2570 2465 6801 652248.50 150 420 280 0.54 0.68 102.4 102.4 1350 1350 1449 127 1449 11240 Plugged 1449 127 1449 8376 98% 0.6548.50 0 420 420 0.00 0.00 71.8 22.0 5037 5037 5406 5406 5406 5406 0.00

2.00 0.17 0.14 22.0 169 162 9833 9429 1.00 169 162 6969 668350.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 478 5474 15785 Plugged 5474 478 5474 12921 96% 1.0050.50 0 437 437 0.00 0.00 74.7 22.0 5241 5100 5474 5474 5474 5474 0.00

4.50 0.17 0.14 22.0 379 364 10212 9793 1.00 379 364 7348 704755.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 478 5474 16164 Plugged 5474 478 5474 13300 96% 1.0355.00 0 473 473 0.00 0.00 80.9 22.0 5673 5100 5474 5474 5474 5474 0.00

10.00 0.17 0.14 22.0 843 808 11054 10601 1.00 843 808 8191 785565.00 0 558 558 0.00 0.00 95.4 22.0 6693 5100 5474 478 5474 17006 Plugged 5474 478 5474 14143 97% 1.0965.00 0 558 558 0.00 0.00 122.2 22.0 11155 5100 5474 5474 5474 5474 0.00

2.50 0.22 0.14 22.0 211 202 11265 10803 1.00 211 202 8401 805767.50 0 583 583 0.00 0.00 127.7 22.0 11655 5100 5474 478 5474 17217 Plugged 5474 478 5474 14353 97% 1.1167.50 150 583 389 0.39 0.80 120.7 120.7 1350 1350 1449 1449 1449 1449 0.00

6.00 0.00 0.00 123.4 2836 2720 14101 13523 0.67 1891 1813 10292 987073.50 150 637 425 0.35 0.84 126.2 126.2 1350 1350 1449 127 1449 15677 Plugged 1449 127 1449 11868 99% 0.9273.50 0 637 637 0.00 0.00 108.9 22.0 7641 5100 5474 5474 5474 5474 0.00

1.00 0.17 0.14 22.0 84 81 14185 13604 1.00 84 81 10376 995174.50 0 645 645 0.00 0.00 110.3 22.0 7737 5100 5474 478 5474 20137 Plugged 5474 478 5474 16328 97% 1.2674.50 150 645 430 0.35 0.85 127.0 127.0 1350 1350 1449 1449 1449 1449 0.00

11.50 0.00 0.00 131.6 5796 5558 19981 19162 0.67 3864 3705 14240 1365686.00 150 743 495 0.30 0.91 136.2 136.2 1350 1350 1449 127 1449 21557 Plugged 1449 127 1449 15816 99% 1.22

0 22 5100

22 5000

22 5100

22 5100

22

22

22 5100

5100

22 5100

22 5100

22

100000

5100

22

12

fkPa

12

12

12

0

22

100000

sand

INPUT DATA

sand

calc

S

S

S

0.47

22

22



SRD

SOIL IDENTIFICATION: 3N09

K.tan��limit

K.tan�

calc

sand

silt

calc

sand

clay

sand

clay C

S

S

C

22 5100

22 5100

S

S

S

S 8.025

25

25

25 8.5

8.0

8.0

25

25

25

0

25

0

25

8.5

8.5

8.0

8.5

9.6

9.6

8.5

0

0.47

0.47

0.47

0.47

0

0.47

510022

5100

0

20

100000

5100

100000

22

22

12

5100

5100

0.47

12

12

12

120.47 22

0

20

qkPa

20

25

20

20

20

20

0.47 12

calc

CALCULATIONS

5100

5100

5000

5100 20

0.47

calc S 25 8.0 22 5100 22 5100 20

silt S 25 8.5 0.47 12 22 5100 22 5100 20

gypsum S 30 10.0 0.47 20 22 5100 22 5100 25

silt C 0 9.0 0 0 22 5100 100000 100000 0

S 25 8.0 0.47 12 22 5100 22

0 0 22 5100silt C 0 8.5 100000 100000 0

5100 20


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.05 0.22 0.14 2.3 17 16 17 16 1.00 17 16 17 16

3.05 0 21 21 0.00 0.00 3.0 4.7 897 897 357 52 16 85 Cored 0.20 2.36 357 52 16 85 39% 0.013.05 45 21 14 3.16 0.37 16.9 16.9 405 405 161 16 161 16 0.00

3.35 0.00 0.00 18.9 151 141 168 157 0.67 101 94 118 1106.40 45 50 33 1.35 0.46 20.9 20.9 405 405 161 23 157 349 Cored 161 23 110 252 91% 0.046.40 0 50 50 0.00 0.00 13.5 0.0 2093 0 0 0 0 0 0.00

1.22 0.27 0.14 0.0 0 0 168 157 1.00 0 0 118 1107.62 0 61 61 0.00 0.00 16.7 0.0 2579 0 0 0 0 168 Plugged 0 0 0 118 100% 0.027.62 60 61 41 1.47 0.45 27.3 27.3 540 540 215 157 215 110 0.00

1.52 0.00 0.00 27.9 102 95 270 252 0.67 68 63 186 1749.14 60 74 50 1.21 0.48 28.6 28.6 540 540 215 31 215 516 Plugged 215 31 174 390 92% 0.069.14 0 74 74 0.00 0.00 16.3 10.4 1784 1784 710 252 710 174 0.00

3.05 0.22 0.14 12.1 88 83 358 335 1.00 88 83 274 25612.19 0 99 99 0.00 0.00 21.6 13.8 2370 2370 943 137 335 831 Cored 943 137 256 667 79% 0.1012.19 0 99 99 0.00 0.00 26.8 13.8 2370 2370 943 335 943 256 0.00

3.66 0.27 0.14 16.3 142 133 501 468 1.00 142 133 417 38915.85 0 134 134 0.00 0.00 36.2 18.7 3204 3204 1276 185 468 1154 Cored 1276 185 389 991 81% 0.1415.85 0 134 134 0.00 0.00 29.3 18.7 1335 1335 532 468 532 389 0.00

0.91 0.22 0.14 19.1 42 39 543 507 1.00 42 39 458 42816.76 0 140 140 0.00 0.00 30.7 19.6 1399 1399 557 81 507 1131 Cored 557 81 428 967 92% 0.1416.76 0 140 140 0.00 0.00 30.7 19.6 3357 3357 1337 507 1337 428 0.00

4.58 0.22 0.14 20.8 228 213 771 720 1.00 228 213 686 64121.34 0 179 179 0.00 0.00 39.2 22.0 4291 4291 1709 248 720 1739 Cored 1709 248 641 1576 84% 0.2321.34 225 179 119 1.89 0.43 96.0 96.0 2025 2025 806 720 806 641 0.00

15.23 0.00 0.00 103.0 3755 3508 4525 4228 0.67 2503 2339 3189 298036.57 225 308 206 1.09 0.49 110.0 110.0 2025 2025 806 117 806 5449 Plugged 806 117 806 4113 97% 0.6036.57 0 308 308 0.00 0.00 83.6 22.0 7398 5100 2031 2031 2031 2031 0.00

7.43 0.27 0.14 22.0 391 366 4916 4594 1.00 391 366 3581 334644.00 0 379 379 0.00 0.00 102.8 22.0 9092 5100 2031 295 2031 7242 Plugged 2031 295 2031 5906 95% 0.8644.00 0 379 379 0.00 0.00 64.8 22.0 9092 5100 2031 2031 2031 2031 0.00

18.50 0.17 0.14 22.0 974 910 5891 5504 1.00 974 910 4555 425662.50 0 518 518 0.00 0.00 88.5 22.0 12422 5100 2031 295 2031 8216 Plugged 2031 295 2031 6881 96% 1.0062.50 0 518 518 0.00 0.00 140.5 22.0 12422 5100 2031 2031 2031 2031 0.00

6.50 0.27 0.14 22.0 342 320 6233 5824 1.00 342 320 4897 457669.00 0 579 579 0.00 0.00 157.2 22.0 13904 5100 2031 295 2031 8559 Plugged 2031 295 2031 7223 96% 1.0569.00 0 579 579 0.00 0.00 157.2 81.1 13904 10000 3982 3982 3982 3982 0.00

6.00 0.27 0.14 85.1 1222 1142 7455 6966 1.00 1222 1142 6120 571875.00 0 636 636 0.00 0.00 172.7 89.1 15272 10000 3982 579 3982 12016 Plugged 3982 579 3982 10680 95% 1.55

SOIL IDENTIFICATION: 3SC-K05

K.tan��limit

K.tan�

22 5100

22 5100

22 5100

24

22 5100

5100

22 5100

0 0

22

22 5100

22 5100

22

22

22

22

24

fkPa

24

24

10

24

100000

22

22

dense carbonate SILTY SAND slightly

INPUT DATA

loose carbonate SAND slightly cemen

firm carbonate CLAY with shell fragm

S

C

S

0

22

100000



firm to stiff carbonate CLAY



clayey carbonate SILT interbedded w

very weak carbonate SILTSTONE wit

dense cemented carbonate SAND wit

stiff carbonate CLAY with traces of gy

very weak fine to coarse carbonate SA S

S

C

S

0.47 0

S

S

S

C

0 0

0 00

42

42

42

8.50

30

0

35 9.5

8.5

7.0

30

35

25

30

0

35

25

8.0

9.5

7.0

8.5

8.5

9.5

7.5

0.47

0.47

0.47

0.47

0.47

0.47

0

5100

30

20

5100

100000

5100

25

0

0

0

5100

5100

0.47

0100000100000

0

5100

5100

100000

CALCULATIONS SRD

25

0

qkPa

30

25

30

25

dense carbonate SAND S 35 9.5 0.47 24 22 5100 22 5100 30

dense carbonate SAND S 35 9.5 0.47 24 100 10000 100 10000 30


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 07.50 0.22 0.14 7.1 127 119 127 119 1.00 127 119 127 119

7.50 0 65 65 0.00 0.00 9.1 14.2 2591 2591 1032 150 119 396 Cored 0.20 7.17 1032 150 119 396 62% 0.037.50 75 65 43 1.73 0.44 32.6 32.6 674 674 268 119 268 119 0.00

2.32 0.00 0.00 33.9 188 175 315 295 0.67 125 117 253 2369.81 75 87 58 1.30 0.47 35.1 35.1 674 674 268 39 268 623 Plugged 268 39 236 528 93% 0.059.81 0 87 87 0.00 0.00 23.5 12.1 3464 3464 1379 295 1379 236 0.00

4.63 0.27 0.14 15.2 168 157 484 452 1.00 168 157 421 39314.45 0 130 130 0.00 0.00 35.3 18.2 5210 5100 2031 295 452 1231 Cored 2031 295 393 1110 73% 0.0914.45 0 130 130 0.00 0.00 35.3 18.2 3126 3126 1245 452 1245 393 0.00

7.96 0.27 0.14 20.1 383 358 867 810 1.00 383 358 804 75122.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 285 810 1962 Cored 1961 285 751 1840 85% 0.1622.40 0 205 205 0.00 0.00 55.7 22.0 4924 4924 1961 810 1961 751 0.00

4.08 0.27 0.14 22.0 215 201 1082 1011 1.00 215 201 1019 95226.49 0 244 244 0.00 0.00 66.1 22.0 5848 5100 2031 295 1011 2388 Cored 2031 295 952 2267 87% 0.1926.49 0 244 244 0.00 0.00 66.1 22.0 2924 2924 1164 1011 1164 952 0.00

8.02 0.27 0.14 22.0 422 394 1504 1405 1.00 422 394 1441 134734.50 0 325 325 0.00 0.00 88.3 22.0 3905 3905 1555 226 1405 3135 Cored 1555 226 1347 3014 93% 0.2634.50 0 325 325 0.00 0.00 71.3 22.0 3905 3905 1555 1405 1555 1347 0.00

7.01 0.22 0.14 22.0 369 345 1873 1750 1.00 369 345 1811 169241.51 0 386 386 0.00 0.00 84.6 22.0 4632 4632 1844 268 1750 3892 Cored 1844 268 1692 3770 93% 0.3241.51 300 386 257 1.17 0.48 144.4 144.4 2700 2700 1075 1075 1075 1075 0.00

27.98 0.00 0.00 162.3 10870 10157 12744 11907 0.67 7247 6771 9058 846369.49 300 650 433 0.69 0.60 180.2 180.2 2700 2700 1075 156 1075 13975 Plugged 1075 156 1075 10289 98% 0.8869.49 0 650 650 0.00 0.00 176.3 22.0 15590 5100 2031 2031 2031 2031 0.00

6.49 0.27 0.14 22.0 342 319 13086 12227 1.00 342 319 9399 878375.99 0 711 711 0.00 0.00 192.9 22.0 17058 5100 2031 295 2031 15411 Plugged 2031 295 2031 11725 97% 1.00




SRD

0

30

qkPa

30

25

30

30

30

24

5100

5100

0

0.47

100000

5100

12

0.47

0.47

10000022 5100

35

30

0

10.2

8.6

9.4

SILTSTONE 510024

35

C

S

S

S

S

9.4

C

CALCULATIONS

MUD

fkPa

9.4

8.630

0

40

0

35

0

35

510022

5100

0

40

9.4

22

CORAL & SAND

SAND WITH SO

SANDSTONE

SILT WITH SOM

CLAY WITH SO

SAND S

S 9.4

35 9.4 5100

0

22

22

22

12

24 22

22 5100

22 5100

5100

22 5100

22

22

22

220.47 5000

51000.47

0.47

0.47 225100

K.tan��limit

100000 100000

K.tan�

22 5000

INPUT DATA

SANDSTONE S 25


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

de

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th t h

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 35 0 0 0.00 0.00 0.0 0.0 315 315 0 0 012.19 0.00 0.00 13.1 459 434 459 434 0.67 306 290 459 290

12.19 35 118 79 0.44 0.75 26.2 26.2 315 315 184 22 184 666 Plugged 0.20 8.73 184 22 184 666 97% 0.0512.19 0 118 118 0.00 0.00 41.9 16.6 4730 4730 2773 434 2773 290 0.00

8.53 0.35 0.14 19.3 472 447 932 881 1.00 472 447 932 73620.73 0 224 224 0.00 0.00 79.4 22.0 8964 5100 2990 356 881 2169 Cored 2990 356 736 2024 82% 0.1520.73 165 224 149 1.10 0.49 80.5 80.5 1485 1485 871 871 871 736 0.00

4.57 0.00 0.00 83.4 1095 1035 2026 1916 0.67 730 690 1662 142625.30 165 271 180 0.91 0.52 86.3 86.3 1485 1485 871 104 871 3001 Plugged 871 104 871 2636 96% 0.1925.30 0 271 271 0.00 0.00 95.9 22.0 10829 5100 2990 1916 2990 1426 0.00

3.35 0.35 0.14 22.0 212 200 2238 2116 1.00 212 200 1873 162628.65 0 312 312 0.00 0.00 110.6 22.0 12492 5100 2990 356 2116 4710 Cored 2990 356 1626 3856 91% 0.2828.65 165 312 208 0.79 0.56 92.7 92.7 1485 1485 871 871 871 871 0.00

31.39 0.00 0.00 112.3 10122 9568 12360 11684 0.67 6748 6379 8621 800560.05 165 633 422 0.39 0.80 131.9 131.9 1485 1485 871 104 871 13334 Plugged 871 104 871 9595 99% 0.7060.05 0 633 633 0.00 0.00 178.6 22.0 15181 5100 2990 2990 2990 2990 0.00

11.58 0.28 0.14 22.0 732 692 13092 12375 1.00 732 692 9353 869671.63 0 734 734 0.00 0.00 207.4 22.0 17624 5100 2990 356 2990 16438 Plugged 2990 356 2990 12699 97% 0.9371.63 0 734 734 0.00 0.00 160.9 22.0 7343 5100 2990 2990 2990 2990 0.00

16.15 0.22 0.14 22.0 1020 965 14112 13340 1.00 1020 965 10373 966187.78 0 912 912 0.00 0.00 199.8 22.0 9119 5100 2990 356 2990 17458 Plugged 2990 356 2990 13720 97% 1.00


31

25

0

0

5100

CALCULATIONS SRDq

kPa

510022

100000

1000000

0.47

0.47

0

37

0.47

5100

5100

0

37

0

36

40

10.2

8.8

11.0

12.442

0

42

0 10.2

12.4

9.7

0 100000

S

S

C

S

22 5100

22 5100SANDSTONE

CLAY

SAND

SILT



CLAY

INPUT DATA

SILT MUDDY

SANDSTONE

C

S

C

0.47

100000

22

22

fkPa

0

24

10

40

8

40

0

22 5100

100000

5100

22

22

22 5100

22 5100

K.tan��limit

K.tan�

22 5000 100000


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

eri

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 02.59 0.19 0.14 1.7 11 10 11 10 1.00 11 10 11 10

2.59 0 18 18 0.00 0.00 2.6 3.4 0 0 0 0 0 11 Plugged 0.20 2.03 0 0 0 11 100% 0.002.59 40 18 12 3.23 0.37 14.7 14.7 356 356 142 10 142 10 0.00

2.01 0.00 0.00 16.2 78 73 88 83 0.67 52 48 62 584.60 40 37 25 1.59 0.45 17.6 17.6 356 356 142 21 83 192 Cored 142 21 58 141 85% 0.024.60 50 37 25 2.00 0.42 20.9 20.9 448 448 178 83 178 58 0.00

1.58 0.00 0.00 21.7 82 77 171 160 0.67 55 51 117 1106.19 50 50 33 1.50 0.45 22.5 22.5 448 448 178 26 160 356 Cored 178 26 110 253 90% 0.046.19 0 50 50 0.00 0.00 11.5 7.0 0 0 0 0 0 0 0.00

2.62 0.23 0.14 8.7 55 51 225 211 1.00 55 51 172 1618.81 0 74 74 0.00 0.00 17.2 10.4 0 0 0 0 0 225 Plugged 0 0 0 172 100% 0.038.81 60 74 50 1.21 0.48 28.6 28.6 539 539 214 211 214 161 0.00

2.68 0.00 0.00 29.9 192 179 417 390 0.67 128 119 300 28011.49 60 98 65 0.92 0.52 31.2 31.2 539 539 214 31 214 663 Plugged 214 31 214 545 94% 0.0911.49 0 98 98 0.00 0.00 22.5 13.7 0 0 0 0 0 0 0.00

9.51 0.23 0.14 17.8 406 379 823 769 1.00 406 379 706 65921.00 0 187 187 0.00 0.00 43.2 22.0 0 0 0 0 0 823 Plugged 0 0 0 706 100% 0.1221.00 0 187 187 0.00 0.00 34.9 22.0 0 0 0 0 0 0 0.00

1.65 0.19 0.14 22.0 87 81 910 850 1.00 87 81 792 74022.65 0 199 199 0.00 0.00 37.1 22.0 0 0 0 0 0 910 Plugged 0 0 0 792 100% 0.1322.65 150 199 133 1.13 0.48 72.7 72.7 1349 1349 537 537 537 537 0.00

7.71 0.00 0.00 76.6 1414 1321 2323 2171 0.67 942 881 1735 162130.36 150 259 173 0.87 0.54 80.5 80.5 1349 1349 537 78 537 2938 Plugged 537 78 537 2350 97% 0.3930.36 300 259 173 1.74 0.44 130.7 130.7 2700 2700 1075 1075 1075 1075 0.00

11.70 0.00 0.00 136.3 3819 3568 6142 5739 0.67 2546 2379 4281 400042.06 300 360 240 1.25 0.47 141.9 141.9 2700 2700 1075 156 1075 7374 Plugged 1075 156 1075 5512 97% 0.9242.06 0 360 360 0.00 0.00 83.2 22.0 0 0 0 0 0 0 0.00

10.06 0.23 0.14 22.0 530 495 6672 6234 1.00 530 495 4810 449552.12 0 447 447 0.00 0.00 103.3 22.0 0 0 0 0 0 6672 Plugged 0 0 0 4810 100% 0.8052.12 0 447 447 0.00 0.00 65.1 22.0 0 0 0 0 0 0 0.00

22.89 0.15 0.14 22.0 1206 1126 7878 7361 1.00 1206 1126 6016 562175.01 0 663 663 0.00 0.00 96.5 22.0 0 0 0 0 0 7878 Plugged 0 0 0 6016 100% 1.00

SOIL IDENTIFICATION: 3SC

K.tan��limit

K.tan�

22 5100

22 5000

22 5100

22 5100

5100

22 5100

22 5100

100000

22 5100

22 5100

22

100000

22

22

fkPa

100000

22

22

CLAY

INPUT DATA

SAND/SAND

CLAY

C

C

S

0

22

100000



SAND/SAND

CLAY

SANDSTONE

SILT/SAND

SILTSTONE/

CLAY

CLAY

SANDSTONE S

S

C

C

0 100000

S

S

C

S

22 5100

22 51009.435

30

0

0 7.9

9.4

7.1

0

35

25

0

0

35

25

8.6

9.4

7.1

7.9

8.6

8.6

9.4

0.4

0.4

0

0.4

0.4

0

0

5100

30

20

100000

100000

5100

25

0

0.4

5100

5100

0.4

30510022

100000

100000

5000

100000

CALCULATIONS SRD

0

0

qkPa

30

25

0

0


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

g

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r ea

ch la

yer

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 03.00 0.13 0.13 1.5 17 17 17 17 1.00 17 17 17 17

3.00 0 24 24 0.00 0.00 3.0 3.0 1008 900 966 84 17 118 Cored 0.20 2.66 966 84 17 118 29% 0.013.00 0 24 24 0.00 0.00 4.1 3.4 1008 1008 1082 17 1082 17 0.00

4.20 0.17 0.14 6.0 97 93 114 109 1.00 97 93 114 1097.20 0 62 62 0.00 0.00 10.6 8.7 2596 1900 2039 178 109 401 Cored 2039 178 109 401 56% 0.027.20 70 62 41 1.70 0.44 30.7 30.7 630 630 676 109 676 109 0.00

6.80 0.00 0.00 34.3 892 856 1006 965 0.67 595 571 709 68014.00 70 123 82 0.85 0.54 37.9 37.9 630 630 676 59 676 1742 Plugged 676 59 676 1444 96% 0.0614.00 0 123 123 0.00 0.00 21.0 17.2 2952 1900 2039 965 2039 680 0.00

9.00 0.17 0.14 18.6 641 615 1648 1580 1.00 641 615 1350 129523.00 0 204 204 0.00 0.00 34.9 20.0 4896 1900 2039 178 1580 3406 Cored 2039 178 1295 2823 94% 0.1323.00 200 204 136 1.47 0.45 90.8 90.8 1800 1800 1932 1580 1932 1295 0.00

37.00 0.00 0.00 114.6 16233 15568 17881 17148 0.67 10822 10378 12173 1167360.00 200 574 383 0.52 0.69 138.3 138.3 1800 1800 1932 169 1932 19982 Plugged 1932 169 1932 14273 99% 0.6460.00 500 574 383 1.31 0.47 233.8 233.8 4500 4500 4830 4830 4830 4830 0.00

4.50 0.00 0.00 236.2 4070 3903 21951 21051 0.67 2713 2602 14886 1427564.50 500 621 414 1.21 0.48 238.5 238.5 4500 4500 4830 422 4830 27203 Plugged 4830 422 4830 20138 98% 0.9164.50 0 621 621 0.00 0.00 136.2 67.0 0 0 0 0 0 0 0.00

8.00 0.22 0.14 67.0 2053 1968 24004 23019 1.00 2053 1968 16939 1624472.50 0 701 701 0.00 0.00 153.7 67.0 0 0 0 0 0 24004 Plugged 0 0 0 16939 100% 0.7672.50 0 701 701 0.00 0.00 120.0 50.0 16830 1800 1932 1932 1932 1932 0.00

12.50 0.17 0.14 50.0 2394 2295 26397 25315 1.00 2394 2295 19332 1853985.00 0 826 826 0.00 0.00 141.3 50.0 19830 1800 1932 169 1932 28498 Plugged 1932 169 1932 21433 99% 0.9685.00 0 826 826 0.00 0.00 181.1 50.0 0 0 0 0 0 0 0.00

15.10 0.22 0.14 50.0 2891 2773 29289 28087 1.00 2891 2773 22223 21312100.10 0 977 977 0.00 0.00 214.2 50.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00

0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 0 0.00

0.00 0.00 0.00 0.0 0 0 29289 28087 0.67 0 0 22223 21312100.10 0 977 652 0.00 0.00 0.0 0.0 0 0 0 0 0 29289 Plugged 0 0 0 22223 100% 1.00


K.tan��limit

K.tan�

0 0

10 900

20 1900

24

0 0

2900

50 1800

50 3000

50

0 0

0 0

67

100000

100000

67

24

fkPa

0

24

0

24

50

100000

100000

firm to stiff claye

INPUT DATA

verysoft carbona

very weak to we

C

S

S

0.47

10

20



very weak to we

stiff to hard silty

very hard clayey

loose to very de

dense carbonate

dense silty carb

C

C

S

S

0 100000

S

C

C

S

0 0

20 190024

42

42

42

9.025

20

25

0 9.0

9.0

8.0

0

0

25

25

30

0

0

10.0

10.5

10.0

10.0

10.0

0.0

0.0

0

0

0

0

0.47

0.47

0.47

100000

0

0

1800

3000

100000

15

20

0.47

0

2900

100000

0.47

20190020

100000

100000

900

1900

CALCULATIONS SRD

20

25

qkPa

0

25

0

0

INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25 mm Ko non-cohesive 1.0 Steel cross section area 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m

mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soil

Des

crip

tion

(San

d S,

Cla

y C

)

� =

pile

fric

tion

angl

e

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

N q =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

f lim =

lim

it un

it sk

in fr

ictio

n

q lim =

lim

it un

it be

arin

g pr

essu

re

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r ea

ch la

ye

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

Plas

ticity

Inde

x

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adju

stm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r ea

ch la

ye

Shaf

t inn

er fr

ictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fr

ictio

n

End

Bea

ring

of t

he s

oil b

enea

th

End

Bea

ring

on

annu

lus

Min

imum

Qsi

or

Ap

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SRD

for G

RLW

EAP

INPU

T

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 00.25 0.17 0.14 0.2 0 0 0 0 1.00 0 0 0 0

0.25 0 2 2 0.00 0.00 0.3 0.3 84 84 49 6 0 6 Cored 0.20 0.22 49 6 0 6 4% 0.000.25 0 2 2 0.00 0.00 0.4 0.3 84 84 49 0 49 0 0.00

1.95 0.22 0.14 1.4 8 7 8 7 1.00 8 7 8 72.20 0 18 18 0.00 0.00 3.9 2.5 739 739 433 52 7 67 Cored 433 52 7 67 23% 0.002.20 0 18 18 0.00 0.00 3.0 2.5 422 422 248 7 248 7 0.00

8.80 0.17 0.14 8.0 202 191 210 199 1.00 202 191 210 19911.00 0 97 97 0.00 0.00 16.6 13.6 2323 2323 1362 162 199 571 Cored 1362 162 199 571 72% 0.0411.00 0 97 97 0.00 0.00 12.2 12.2 774 774 454 199 454 199 0.00

4.00 0.13 0.13 14.5 166 157 376 356 1.00 166 157 376 35615.00 0 133 133 0.00 0.00 16.7 16.7 1062 1062 623 74 356 806 Cored 623 74 356 806 91% 0.0615.00 30 133 89 0.34 0.86 25.8 25.8 270 270 158 158 158 158 0.00

3.50 0.00 0.00 27.2 274 259 650 614 0.67 182 172 559 52818.50 30 164 110 0.27 0.96 28.7 28.7 270 270 158 19 158 827 Plugged 158 19 158 736 97% 0.0518.50 0 164 164 0.00 0.00 28.1 22.0 3943 3943 2312 614 2312 528 0.00

3.50 0.17 0.14 22.0 221 209 871 823 1.00 221 209 780 73722.00 0 191 191 0.00 0.00 32.6 22.0 4573 4573 2681 319 823 2013 Cored 2681 319 737 1836 83% 0.1322.00 70 191 127 0.55 0.67 47.1 47.1 630 630 369 369 369 369 0.00

11.40 0.00 0.00 52.8 1729 1634 2600 2457 0.67 1153 1090 1932 182733.40 70 293 195 0.36 0.84 58.5 58.5 630 630 369 44 369 3013 Plugged 369 44 369 2346 98% 0.1733.40 0 293 293 0.00 0.00 50.1 22.0 7036 5100 2990 2457 2990 1827 0.00

2.30 0.17 0.14 22.0 145 137 2745 2595 1.00 145 137 2078 196435.70 0 314 314 0.00 0.00 53.7 22.0 7532 5100 2990 356 2595 5696 Cored 2990 356 1964 4397 92% 0.3235.70 0 314 314 0.00 0.00 53.7 22.0 12554 5100 2990 2595 2990 1964 0.00

10.30 0.17 0.14 22.0 651 615 3396 3210 1.00 651 615 2728 257946.00 0 407 407 0.00 0.00 69.5 22.0 16262 5100 2990 356 2990 6742 Plugged 2990 356 2579 5663 94% 0.4146.00 150 407 271 0.55 0.67 100.8 100.8 1350 1350 792 792 792 792 0.00

18.00 0.00 0.00 110.5 5711 5398 9106 8608 0.67 3807 3599 6535 617864.00 150 578 385 0.39 0.80 120.2 120.2 1350 1350 792 94 792 9992 Plugged 792 94 792 7421 99% 0.5464.00 350 578 385 0.91 0.52 183.5 183.5 3150 3150 1847 1847 1847 1847 0.00

14.00 0.00 0.00 194.1 7802 7375 16908 15983 0.67 5201 4917 11736 1109478.00 350 718 478 0.73 0.58 204.6 204.6 3150 3150 1847 220 1847 18975 Plugged 1847 220 1847 13803 98% 1.0078.00 0 718 718 0.00 0.00 157.3 22.0 17221 5100 2990 2990 2990 2990 0.00

4.50 0.22 0.14 22.0 284 269 17192 16252 1.00 284 269 12021 1136382.50 0 758 758 0.00 0.00 166.1 22.0 18193 5100 2990 356 2990 20538 Plugged 2990 356 2990 15367 98% 1.1182.50 0 758 758 0.00 0.00 129.7 22.0 6064 5100 2990 2990 2990 2990 0.00

17.90 0.17 0.14 22.0 1131 1069 18323 17321 1.00 1131 1069 13151 12432100.40 0 937 937 0.00 0.00 160.3 22.0 7496 5100 2990 356 2990 21669 Plugged 2990 356 2990 16498 98% 1.20




5100 22 5100 20

22 5100 25

SILT, dense car S 25 10.0 0.47 8 22

0.47 24 22 5100SAND, dense ca S 30 9.0

K.tan��limit

K.tan�

22 5100

22 5000

22 5100

0

100000

22 5100

5100

22 5100

22 5100

22

100000

22

22 5100

22 5100

22

0

fkPa

0

24

0

24

22

100000

100000

SAND, loose to

INPUT DATA

CORAL, Weak t

GRAVEL, Loose

S

S

S

0.47

22

22

SILT, loose to m

SILT, soft carbo

SAND, medium

SILT, firm carbo

CLAY, hard silty

SILT, dense car

CALCARENITE,

CLAY, stiff to ve C

C

S

S

0.47 22

C

S

C

S

22 5100

22 51008

42

42

24

9.020

25

30

25 9.0

8.0

8.0

0

25

0

25

25

0

0

9.0

7.5

9.0

9.0

9.0

9.5

10.0

0

0

0

0.47

0

0.47

0.47

100000

0

0

5100

5100

100000

20

25

0.47

40

100000

5100

0.47

15510022

5100

100000

5000

5100

CALCULATIONS SRD

20

20

qkPa

20

25

20

0


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res s

Nq =

bea

ring

capa

city

fact

or

c or

Su =

und

rain

ed s

hear

stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

c

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

.

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu =

Ulti

mat

e Pi

le C

apac

ity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.80 0.13 0.13 1.0 5 5 5 5 1.00 5 5 5 5

1.80 0 15 15 0.00 0.00 1.9 1.9 122 122 72 9 5 18 Cored 0.20 1.69 72 9 5 18 53% 0.001.80 0 15 15 0.00 0.00 2.6 2.1 184 184 108 5 108 5 0.00

4.20 0.17 0.14 4.8 58 55 63 59 1.00 58 55 63 596.00 0 53 53 0.00 0.00 9.1 7.4 637 637 374 44 59 167 Cored 374 44 59 167 73% 0.016.00 20 53 35 0.56 0.67 13.3 13.3 180 180 106 59 106 59 0.00

6.00 0.00 0.00 16.1 277 262 340 321 0.67 185 175 248 23412.00 20 107 71 0.28 0.94 18.9 18.9 180 180 106 13 106 458 Plugged 106 13 106 366 97% 0.0212.00 0 107 107 0.00 0.00 23.5 15.0 1285 1285 754 321 754 234 0.00

3.00 0.22 0.14 16.7 144 136 484 457 1.00 144 136 391 37015.00 0 131 131 0.00 0.00 28.7 18.4 1573 1573 922 110 457 1051 Cored 922 110 370 871 87% 0.0515.00 0 131 131 0.00 0.00 22.4 18.4 2622 2622 1537 457 1537 370 0.00

5.50 0.17 0.14 21.8 345 326 828 783 1.00 345 326 736 69620.50 0 181 181 0.00 0.00 30.9 25.3 3612 2900 1700 202 783 1814 Cored 1700 202 696 1634 88% 0.1020.50 150 181 120 1.25 0.47 71.0 71.0 1350 1350 792 783 792 696 0.00

13.50 0.00 0.00 78.9 3060 2893 3889 3676 0.67 2040 1929 2776 262434.00 150 302 201 0.74 0.58 86.9 86.9 1350 1350 792 94 792 4774 Plugged 792 94 792 3662 97% 0.2234.00 200 302 201 0.99 0.50 100.3 100.3 1800 1800 1055 1055 1055 1055 0.00

3.00 0.00 0.00 102.7 884 836 4773 4512 0.67 590 557 3366 318237.00 200 331 220 0.91 0.52 105.0 105.0 1800 1800 1055 126 1055 5954 Plugged 1055 126 1055 4547 97% 0.2837.00 90 331 220 0.41 0.78 70.4 70.4 810 810 475 475 475 475 0.00

12.00 0.00 0.00 76.3 2630 2486 7403 6998 0.67 1753 1657 5119 483949.00 90 451 300 0.30 0.91 82.2 82.2 810 810 475 57 475 7934 Plugged 475 57 475 5650 99% 0.3549.00 150 451 300 0.50 0.71 106.1 106.1 1350 1350 792 792 792 792 0.00

14.50 0.00 0.00 123.1 5125 4845 12528 11842 0.67 3417 3230 8535 806863.50 200 588 392 0.51 0.70 140.0 140.0 1800 1800 1055 126 1055 13709 Plugged 1055 126 1055 9716 99% 0.6063.50 0 588 588 0.00 0.00 159.7 82.4 23534 9600 5628 5628 5628 5628 0.00

6.00 0.27 0.14 86.4 1488 1406 14015 13249 1.00 1488 1406 10023 947569.50 0 645 645 0.00 0.00 175.1 90.3 25814 9600 5628 670 5628 20314 Plugged 5628 670 5628 16322 96% 1.0069.50 0 645 645 0.00 0.00 110.4 48.0 5163 1900 1114 1114 1114 1114 0.00

18.50 0.17 0.14 48.0 2550 2410 16565 15659 1.00 2550 2410 12573 1188588.00 0 821 821 0.00 0.00 140.5 48.0 6569 1900 1114 133 1114 17812 Plugged 1114 133 1114 13820 99% 0.8588.00 0 821 821 0.00 0.00 103.4 67.0 9853 2900 1700 1700 1700 1700 0.00

12.30 0.13 0.13 67.0 2366 2237 18932 17896 1.00 2366 2237 14939 14122100.30 0 938 938 0.00 0.00 118.1 67.0 11255 2900 1700 202 1700 20834 Plugged 1700 202 1700 16842 99% 1.03


K.tan��limit

K.tan�

96 9600

48 1900

67 2900

40

81

0

0

48 1900

0

0 0

0 0

8

fkPa

20

0

0

0

100000

96

48

very soft to soft c

INPUT DATA

very loose carbo

loose to dense c

C

S

S

0.47

48

67



dense silty carbo

silty fine to coars

stiff to very stiff c

very stiff to hard

dense carbonate

stiff carbonate s

very stiff to hard

dense siliceous f S

S

C

C

0 0

67 2900

C

C

S

S 8.030

20

25

0 9.0

9.0

8.5

25

0

0

0

0

35

25

9.0

9.0

9.5

10.0

9.5

9.5

9.5

0.47

0.47

0.47

0

0

0

0

1900

30

20

100000

100000

9600

0.47

2529006712

8

12

00 100000

0.47

0

100000

100000100000

100000

100000

81 2900

0

1900

2900

15

20

0

20

100000

2900

0.47 12

CALCULATIONS SRD

0

0

qkPa

0

25

medium dense t S 20 9.5 1567 2900 67 2900


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t c

q= u

nit b

earin

g pr

essu

re c

onsd

eri

Shaf

t out

er fr

ictio

n fo

r eac

h la

y e

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

ye

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.00 0.22 0.14 1.3 3 3 3 3 1.00 3 3 3 3

1.00 0 12 12 0.00 0.00 1.7 2.6 480 480 191 28 3 34 Cored 0.20 1.33 191 28 3 34 18% 0.001.00 0 12 12 0.00 0.00 1.5 1.5 288 288 115 3 115 3 0.00

8.00 0.13 0.13 6.0 116 108 119 111 1.00 116 108 119 1119.00 0 84 84 0.00 0.00 10.6 10.6 2016 2016 803 117 111 347 Cored 803 117 111 347 66% 0.049.00 0 84 84 0.00 0.00 14.4 11.8 1680 1680 669 111 669 111 0.00

12.00 0.17 0.14 16.9 485 453 604 564 1.00 485 453 604 56421.00 0 180 180 0.00 0.00 30.8 22.0 3600 3600 1433 208 564 1376 Cored 1433 208 564 1376 85% 0.1421.00 175 180 120 1.46 0.45 79.6 79.6 1575 1575 627 564 627 564 0.00

28.00 0.00 0.00 95.9 6431 6009 7034 6573 0.67 4287 4006 4891 457049.00 175 432 288 0.61 0.64 112.2 112.2 1575 1575 627 91 627 7753 Plugged 627 91 627 5609 98% 0.5849.00 0 432 432 0.00 0.00 94.7 22.0 17280 5100 2031 2031 2031 2031 0.00

1.00 0.22 0.14 22.0 53 49 7087 6622 1.00 53 49 4944 461950.00 0 442 442 0.00 0.00 96.9 22.0 17680 5100 2031 295 2031 9413 Plugged 2031 295 2031 7269 96% 0.7550.00 300 442 295 1.02 0.50 149.3 149.3 2700 2700 1075 1075 1075 1075 0.00

4.50 0.00 0.00 152.7 1645 1537 8732 8159 0.67 1097 1025 6040 564454.50 300 487 325 0.92 0.52 156.0 156.0 2700 2700 1075 156 1075 9963 Plugged 1075 156 1075 7271 98% 0.7554.50 0 487 487 0.00 0.00 106.7 22.0 11688 5100 2031 2031 2031 2031 0.00

6.50 0.22 0.14 22.0 342 320 9074 8479 1.00 342 320 6382 596461.00 0 552 552 0.00 0.00 121.0 22.0 13248 5100 2031 295 2031 11400 Plugged 2031 295 2031 8708 97% 0.8961.00 0 552 552 0.00 0.00 94.4 22.0 4416 4416 1758 1758 1758 1758 0.00

18.00 0.17 0.14 22.0 948 886 10022 9365 1.00 948 886 7330 684979.00 0 732 732 0.00 0.00 125.2 22.0 5856 5100 2031 295 2031 12348 Plugged 2031 295 2031 9656 97% 0.9979.00 0 732 732 0.00 0.00 160.4 22.0 17568 5100 2031 2031 2031 2031 0.00

1.80 0.22 0.14 22.0 95 89 10117 9453 1.00 95 89 7425 693880.80 0 750 750 0.00 0.00 164.4 22.0 18000 5100 2031 295 2031 12443 Plugged 2031 295 2031 9751 97% 1.00

SOIL IDENTIFICATION: 3D07



K.tan��limit

510024

20

CALCULATIONS

40

100000100000

5100

22 5000

INPUT DATA

CAPROCK S 5000

22 5100

22 5100

5100

22 5100

22

22

5100

8

22

100000

22

0

2222 5100

22

very Weak CALCARENITE

Crystalline GYPSUM

Hard Silty Calcareous CLAY

Medium Dense Silica SAND

Dense carbonate SILT

Dense Carbonate SAND S

30 10.0

20

25S

S

8.0

0.47

Silty Carbonate SAND

fkPa

9.0

12.030

150.47

Very Stiff Calcareous CLAY 510000 9.0 0

30

S

0.47 22

S

C

S

C

22 5100

0

30

25

10.0

10.0

10.0

10.0 24

5100

100000

0.47

0.47

5100

5100

24

0

25

qkPa

0

25

20

25

K.tan�

25

0

0.47

SRD

20

22

22

0.47 40 22 5100


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 012.30 0.17 0.14 9.9 466 441 466 441 1.00 466 441 466 441

12.30 0 116 116 0.00 0.00 16.2 19.8 1387 1387 1454 166 441 1073 Cored 0.20 12.80 1454 166 441 1073 85% 0.0512.30 0 116 116 0.00 0.00 25.3 16.2 2312 2312 2423 441 2423 441 0.00

0.90 0.22 0.14 16.8 58 55 524 496 1.00 58 55 524 49613.20 0 125 125 0.00 0.00 27.4 17.5 2500 2500 2619 298 496 1319 Cored 2619 298 496 1319 77% 0.0613.20 0 125 125 0.00 0.00 21.4 17.5 1500 1500 1571 496 1571 496 0.00

7.60 0.17 0.14 19.7 575 545 1099 1041 1.00 575 545 1099 104120.80 0 204 204 0.00 0.00 34.9 22.0 2448 2448 2565 292 1041 2432 Cored 2565 292 1041 2432 88% 0.1120.80 118 204 136 0.87 0.54 63.3 63.3 1062 1062 1113 1041 1113 1041 0.00

1.40 0.00 0.00 65.0 348 330 1447 1371 0.67 232 220 1331 126122.20 123 216 144 0.85 0.54 66.6 66.6 1107 1107 1160 132 1160 2739 Plugged 1160 132 1160 2623 95% 0.1222.20 0 216 216 0.00 0.00 37.0 22.0 2598 2598 2722 1371 2722 1261 0.00

2.20 0.17 0.14 22.0 185 176 1632 1547 1.00 185 176 1516 143724.40 0 239 239 0.00 0.00 40.9 22.0 2872 2872 3009 343 1547 3522 Cored 3009 343 1437 3296 90% 0.1524.40 131 239 160 0.82 0.55 72.3 72.3 1179 1179 1235 1235 1235 1235 0.00

24.00 0.00 0.00 99.5 9150 8669 10782 10216 0.67 6100 5779 7616 721648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 229 2009 13019 Plugged 2009 229 2009 9853 98% 0.4648.40 213 453 302 0.71 0.60 126.8 126.8 1917 1917 2009 2009 2009 2009 0.00

17.60 0.00 0.00 137.0 9231 8746 20013 18962 0.67 6154 5831 13770 1304766.00 213 610 406 0.52 0.69 147.1 147.1 1917 1917 2009 229 2009 22250 Plugged 2009 229 2009 16007 99% 0.7566.00 0 610 610 0.00 0.00 133.6 22.0 12192 5100 5343 5343 5343 5343 0.00

5.50 0.22 0.14 22.0 463 439 20476 19401 1.00 463 439 14233 1348671.50 0 666 666 0.00 0.00 146.0 22.0 13325 5100 5343 609 5343 26429 Plugged 5343 609 5343 20186 97% 0.9571.50 0 666 666 0.00 0.00 114.0 22.0 7995 5100 5343 5343 5343 5343 0.00

13.00 0.17 0.14 22.0 1095 1038 21572 20439 1.00 1095 1038 15329 1452484.50 0 804 804 0.00 0.00 137.5 22.0 9649 5100 5343 609 5343 27524 Plugged 5343 609 5343 21281 97% 1.0084.50 0 804 804 0.00 0.00 176.2 22.0 16081 5100 5343 5343 5343 5343 0.00

9.20 0.22 0.14 22.0 775 734 22347 21174 1.00 775 734 16104 1525893.70 0 877 877 0.00 0.00 192.1 22.0 17535 5100 5343 609 5343 28299 Plugged 5343 609 5343 22056 97% 1.0493.70 140 877 584 0.24 1.00 140.0 140.0 1260 1260 1320 1320 1320 1320 0.00

0.80 0.00 0.00 140.0 429 406 22776 21580 0.67 286 271 16390 1552994.50 140 884 589 0.24 1.00 140.0 140.0 1260 1260 1320 150 1320 24246 Plugged 1320 150 1320 17860 99% 0.84

SOIL IDENTIFICATION: 32A



K.tan��limit

22 5100

22 5100

100000

22

22 5100

22 5100

22 5100

22 510020

22

22

10000022 5100

22 5100

fkPa

12

0

K.tan�

22 5000

22 5100

22

CARBONATE SANDSTONE

INPUT DATA

CARBONATE SIL

CARBONATE SANDSTONE

S

S

S

0.47

22

22

CARBONATE CLAY

CARBONATE SANDSTONE

CALCAREOUS CLAY

CALCAREOUS CLAY

S

S

S

CARBONATE CLAY

CARBONATE SAND

CARBONATE SILT

CARBONATE SILTSTONE

C

C

C

S

C 8.90

25

30

25 10.4

10.4

9.4

25

10.4

8.9

8.9

10.3

10.6

25

0

0

30

0.47 22

100000

25

0

5100

5100

5100

0

0

0.47

100000

0.47

01000001000000

12

20

12

20

25

0

12

100000

100000

0.47

0

0

0.47

5100

5100

5000

5100

CALCULATIONS SRD

25

20

qkPa

0

25

20

20

08.90

510022207.9 0.4730


mm



Dep

th b

elow

mud

line

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(San

d S

, Cla

y C

)

f = p

ile fr

ictio

n an

gle

� =

subm

erge

d U

nit W

eigh

t

K =

coe

ffici

ent o

f lat

eral

ear

th p

res

Nq

= be

arin

g ca

paci

ty fa

ctor

c or

Su

= un

drai

ned

shea

r stre

ngth

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

f lim =

lim

it un

it sk

in fr

ictio

n

q lim

= li

mit

unit

bear

ing

pres

sure

� =

soil-

pile

fric

tion

angl

e

p o =

effe

ctiv

e ov

erbu

rden

pre

ssur

e

�=c

/po'

� =

dim

ensi

onle

ss fa

ctor

f = u

nit s

kin

frict

ion

with

out c

onsi

d e

f = u

nit s

kin

frict

ion

incl

udin

g fli

m

q= u

nit b

earin

g pr

essu

re w

ithou

t co

q= u

nit b

earin

g pr

essu

re c

onsd

erin

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

Pla

stic

ity In

dex

Und

rain

ed s

hear

stre

ngth

of s

ame

OC

R

Fp =

Fric

tion

Adj

ustm

ent f

acto

r = 0

Shaf

t out

er fr

ictio

n fo

r eac

h la

yer

Sha

ft in

ner f

rictio

n fo

r eac

h la

yer

Acc

umul

ated

out

er fr

ictio

n

Acc

umul

ated

inne

r fric

tion

End

Bea

ring

of th

e so

il be

neat

h t

End

Bea

ring

on a

nnul

us

Min

imum

Qsi

or A

p

Qu

= U

ltim

ate

Pile

Cap

acity

%ge

Sha

ft R

esis

tanc

e

SR

D fo

r GR

LWE

AP

INP

UT

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 01.49 0.23 0.14 1.8 6 6 6 6 1.00 6 6 6 6

1.49 0 16 16 0.00 0.00 2.2 3.6 652 652 260 38 6 50 Cored 0.20 1.72 260 38 6 50 25% 0.011.49 0 16 16 0.00 0.00 3.6 2.2 652 652 260 6 260 6 0.00

1.31 0.23 0.14 3.1 10 9 16 15 1.00 10 9 16 152.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 71 15 102 Cored 488 71 15 102 31% 0.012.80 0 29 29 0.00 0.00 6.7 4.1 1225 1225 488 15 488 15 0.00

1.01 0.23 0.14 4.8 12 11 28 26 1.00 12 11 28 263.81 0 40 40 0.00 0.00 9.1 5.5 1664 1664 663 96 26 150 Cored 663 96 26 150 36% 0.023.81 0 40 40 0.00 0.00 7.9 5.5 951 951 379 26 379 26 0.00

8.50 0.20 0.14 10.8 220 205 247 231 1.00 220 205 247 23112.31 0 114 114 0.00 0.00 22.8 16.0 2747 2747 1094 159 231 637 Cored 1094 159 231 637 75% 0.0712.31 96 114 76 1.25 0.47 45.2 45.2 862 862 343 231 343 231 0.00

5.18 0.00 0.00 64.3 797 745 1044 976 0.67 531 496 779 72817.50 192 164 109 1.75 0.43 83.3 83.3 1724 1724 686 100 686 1830 Plugged 686 100 686 1565 94% 0.1617.50 0 164 164 0.00 0.00 32.8 22.0 3941 3941 1569 976 1569 728 0.00

6.89 0.20 0.14 22.0 363 339 1407 1315 1.00 363 339 1141 106724.38 0 230 230 0.00 0.00 46.0 22.0 5528 5100 2031 295 1315 3017 Cored 2031 295 1067 2503 88% 0.2624.38 144 230 154 0.94 0.52 74.3 74.3 1293 1293 515 515 515 515 0.00

3.05 0.00 0.00 76.6 559 522 1966 1837 0.67 373 348 1514 141527.43 144 260 173 0.83 0.55 78.9 78.9 1296 1296 516 75 516 2557 Plugged 516 75 516 2105 96% 0.2227.43 0 260 260 0.00 0.00 51.8 22.0 6230 5100 2031 1837 2031 1415 0.00

7.32 0.20 0.14 22.0 385 360 2351 2197 1.00 385 360 1899 177534.75 0 336 336 0.00 0.00 67.0 22.0 8056 5100 2031 295 2031 4677 Plugged 2031 295 1775 3969 93% 0.4134.75 192 336 224 0.86 0.54 103.5 103.5 1724 1724 686 686 686 686 0.00

23.26 0.00 0.00 126.4 7038 6577 9390 8774 0.67 4692 4384 6592 615958.00 239 559 373 0.64 0.62 149.3 149.3 2155 2155 858 125 858 10372 Plugged 858 125 858 7574 98% 0.7858.00 0 559 559 0.00 0.00 111.5 22.0 13414 5100 2031 2031 2031 2031 0.00

4.00 0.20 0.14 22.0 210 197 9600 8970 1.00 210 197 6802 635662.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 295 2031 11926 Plugged 2031 295 2031 9128 97% 0.9462.00 0 597 597 0.00 0.00 119.2 22.0 14335 5100 2031 2031 2031 2031 0.00

11.00 0.20 0.14 22.0 579 541 10179 9511 1.00 579 541 7381 689773.00 0 685 685 0.00 0.00 136.7 22.0 16447 5100 2031 295 2031 12505 Plugged 2031 295 2031 9707 97% 1.00

SOIL IDENTIFICATION: 3



CALCULATIONS SRD

23

0

qkPa

23

25

26

0

5100

100000

5000

5100

26

26

0.47

0

100000

5100

0.47

23510022

5100

23

23

5100

100000

51000.47

0.47

0

0.47

0

0.47

00

28

28

9.6

9.6

9.6

10.4

9.6

9.6

8.0

0

28

0

28

8.828

31

31

31 10.4

10.4

10.4

24

42

42

420.47 22

C

S

C

S

22 5100

22 5100

S

S

C

S

CARBONATE SILT

CARBONATE SAND

SILTY CALCAREOUS CLAY

CEMENTED CARBONATE SAND


CARBONATE CLAY


SILTY CARBONATE CLAY

LIMESTONE

INPUT DATA

LIMESTONE

CARBONATE SAND CEMENTED

S

S

S

0.47

22

22

24

fkPa

0

24

0

24

100000

22

22

22

100000

22

22 5100

22 5100

22

22 5100

5100

22 5100

22 5100

K.tan��limit

K.tan�

22 5100

22 5000

22 5100

24

100000

THE UNIVERSITY OF WESTERN AUSTRALIARELIABILITY OF EXISTING OFFSHORE PLATFORMS IN THE ARABIAN GULFSOIL & FOUNDATION ENGINEERING - PILE CAPACITY CALCULATIONSPrepared By: Hassan Zaghloul

SOIL IDENTIFICATION: 3J07

INPUT PILE PARAMETERS FOR PILE SOIL PARAMETERSPile outside diameter 914 mm Ko cohesive 0.5Pile Wall Thickness 25.0 mm Ko non-cohesive 1.0 Steel cross section are 0.070 m2 mm/mOutside Radius 457 0.14 Soil tip area 0.586 m2 mm/m

mm



Dep

th b

elow

mud

l

Dep

th o

f Lay

er

Soi

l Des

crip

tion

(S

f = p

ile fr

ictio

n an

g

� =

subm

erge

d U

n

K =

coe

ffici

ent o

f l

Nq =

bea

ring

capa

c or

Su =

und

rain

ed

f lim =

lim

it un

it sk

in

q lim

= li

mit

unit

bea

f lim =

lim

it un

it sk

in

q lim

= li

mit

unit

bea

Laye

r Num

ber

� =

soil-

pile

fric

tion

p o =

effe

ctiv

e ov

er

�=c

/po'

� =

dim

ensi

onle

ss

f = u

nit s

kin

frict

ion

f = u

nit s

kin

frict

ion

q= u

nit b

earin

g pr

e

q= u

nit b

earin

g pr

e

Shaf

t out

er fr

ictio

n

Sha

ft in

ner f

rictio

n

Acc

umul

ated

out

e

Acc

umul

ated

inne

End

Bea

ring

of th

e

End

Bea

ring

on a

n

Min

imum

Qsi

or A

Qu =

Ulti

mat

e Pi

le

Laye

r Num

ber

Pla

stic

ity In

dex

Und

rain

ed s

hear

s

OC

R

Fp =

Fric

tion

Adju

s

Shaf

t out

er fr

ictio

n

Sha

ft in

ner f

rictio

n

Acc

umul

ated

out

e

Acc

umul

ated

inne

End

Bea

ring

of th

e

End

Bea

ring

on a

n

Min

imum

Qsi

or A

Qu =

Ulti

mat

e Pi

le

%ge

Sha

ft R

esis

ta

SR

D fo

r GR

LWE

A

0 0 0 0 0.00 0.00 0.0 0.0 0 0 0 0 09.12 I 0.22 0.14 9.3 243 230 243 230 I 1.00 243 230 243 230

9.12 0 85 85 0.00 0.00 11.9 18.6 2036 2036 1193 142 230 616 Cored 0.20 9.39 1193 142 230 616 77% 0.059.12 72 85 57 1.27 0.47 33.9 33.9 648 648 380 230 380 230 0.00

4.00 II 0.00 0.00 55.4 636 602 880 832 II 0.67 424 401 668 63113.12 190 123 82 2.33 0.40 76.9 76.9 1710 1710 1003 119 832 1831 Cored 1003 119 631 1418 92% 0.1013.12 0 123 123 0.00 0.00 33.3 17.2 2941 2941 1724 832 1724 631 0.00

8.00 III 0.27 0.14 19.6 450 425 1330 1257 III 1.00 450 425 1117 105621.12 0 198 198 0.00 0.00 53.7 22.0 4751 4751 2786 332 1257 2918 Cored 2786 332 1056 2505 87% 0.1821.12 140 198 132 1.06 0.49 69.0 69.0 1260 1260 739 739 739 739 0.00

5.04 IV 0.00 0.00 72.3 1047 990 2376 2246 IV 0.67 698 660 1815 171626.16 140 246 164 0.86 0.54 75.7 75.7 1260 1260 739 88 739 3203 Plugged 739 88 739 2642 97% 0.1926.16 0 246 246 0.00 0.00 66.6 22.0 5892 5100 2990 2246 2990 1716 0.00

6.48 V 0.27 0.14 22.0 409 387 2786 2633 V 1.00 409 387 2225 210332.64 0 312 312 0.00 0.00 84.6 22.0 7480 5100 2990 356 2633 5775 Cored 2990 356 2103 4684 92% 0.3432.64 120 312 208 0.58 0.66 79.0 79.0 1080 1080 633 633 633 633 0.00

10.36 VI 0.00 0.00 96.4 2868 2711 5653 5344 VI 0.67 1912 1807 4136 391043.00 190 409 273 0.70 0.60 113.9 113.9 1710 1710 1003 119 1003 6775 Plugged 1003 119 1003 5258 98% 0.3943.00 120 409 273 0.44 0.75 90.5 90.5 1080 1080 633 633 633 633 0.00

18.50 VII 0.00 0.00 121.6 6462 6109 12116 11453 VII 0.67 4308 4072 8445 798361.50 240 584 389 0.62 0.64 152.8 152.8 2160 2160 1266 151 1266 13533 Plugged 1266 151 1266 9862 98% 0.7261.50 0 584 584 0.00 0.00 158.4 22.0 14012 5100 2990 2990 2990 2990 0.00

4.05 VIII 0.27 0.14 22.0 256 242 12371 11695 VIII 1.00 256 242 8700 822465.55 0 622 622 0.00 0.00 168.8 22.0 14928 5100 2990 356 2990 15718 Plugged 2990 356 2990 12047 97% 0.8865.55 0 622 622 0.00 0.00 78.3 22.0 9952 5100 2990 2990 2990 2990 0.00

25.46 IX 0.13 0.13 22.0 1608 1520 13980 13215 IX 1.00 1608 1520 10309 974591.01 0 822 822 0.00 0.00 103.5 22.0 13150 5100 2990 356 2990 17326 Plugged 2990 356 2990 13655 97% 1.00

25

0

24

22 15

305100

5100

22 5100 100000

30

0

2224

8

10000022 5100

22

0

CALCULATIONS

5100

305100

100000

100000 0

SRDq

kPa

5000 25

fINPUT DATA

kPa

0.47

0

0

0.47

0.47

100000

16

8 22 5100

22 510020

10.2

9.4

9.4

9.4

7.9

35

0

0

35

9.40

30

0

35 9.4

9.4

9.3

0.47 2222 5100

C

C

S

C

S

SSAND

SILTY SA

CLAY

SAND

CLAY

CLAY

0 1000008 510022

SAND

SAND

CLAY

S

C

S

K.tan�

22 50000.47 24 22

100000

100000

22

K.tan��limit

22 5100

24

5100

8

Appendix E

“ACTUAL” CAPACITY OF PILES USING BACK ANALYSIS PROCEDURE

Pile above mudline Penetration Total Hammer Blowcount Bias FactorS.N. Dia. L1 L2 L1+L2 Type

mm m m kN BpF NO SETUP With SETUP API

1 762 39.6 42.7 82.3 Vulcan 140-C 6096 125 2198 4396 0.7212 762 39.6 42.7 82.3 Vulcan 140-C 6096 136 1158 2316 0.3803 762 39.6 42.7 82.3 Vulcan 140-C 6096 136 1158 2316 0.3804 762 39.6 42.7 82.3 Vulcan 140-C 6096 147 2051 4102 0.6735 762 39.6 44.2 83.8 Vulcan 140-C 6487 106 1158 2316 0.3576 762 39.6 42.4 82.0 Vulcan 140-C 6018 143 2051 4102 0.6827 762 39.6 42.7 82.3 Vulcan 140-C 6096 107 1158 2316 0.3808 762 39.6 42.7 82.3 Vulcan 140-C 6096 145 2051 4102 0.6739 914 41.0 82.9 123.9 Vulcan 560 19420 53 6873 13745 0.70810 914 41.0 82.6 123.6 Vulcan 560 19401 44 4725 9449 0.48711 914 41.0 82.3 123.2 Vulcan 560 19382 82 6873 13745 0.70912 914 41.0 82.9 123.9 Vulcan 560 19420 89 6873 13745 0.70813 914 41.0 82.3 123.2 Vulcan 560 19382 45 4725 9449 0.48814 914 41.0 83.2 124.2 Vulcan 560 19438 67 6873 13745 0.70715 914.4 41.1 76.9 118.0 Menck 3000 19044 89 8021 16041 0.84216 914.4 41.1 87.7 128.8 Menck 4600 19732 292 10015 20030 1.01517 914.4 41.1 85.7 126.8 Menck 4600 19596 295 10015 20030 1.02218 914.4 41.1 76.4 117.5 Menck 3000 19009 60 6003 12005 0.63219 914.4 41.1 84.7 125.8 Menck 4600 19533 177 10015 20030 1.02520 914.4 41.1 85.4 126.5 Menck 4600 19577 201 10015 20030 1.02321 914.4 41.1 84.9 126.0 Menck 4600 19546 300 10015 20030 1.02522 914.4 41.1 84.8 125.9 Menck 4600 19540 292 10015 20030 1.02523 762 41.1 62.5 103.6 MRBS 3000/150 12173 26 4770 9541 0.78424 762 41.1 62.6 103.7 MRBS 3000/150 12207 23 2592 5183 0.42525 762 41.1 62.6 103.7 MRBS 3000/150 12207 30 4770 9541 0.78226 762 41.1 62.6 103.7 MRBS 3000/150 12207 20 2592 5183 0.42527 762 41.1 65.5 106.6 Menck 3000 13120 62 6978 13955 1.06428 762 41.1 65.6 106.7 Menck 3000 13244 64 6978 13955 1.05429 762 41.1 70.8 111.9 Menck 3000 15089 40 6430 12859 0.85230 762 41.1 65.7 106.8 Menck 3000 13270 55 6978 13955 1.05231 762 40.7 61.0 101.7 Vulcan 040 11663 111 3583 7167 0.61432 762 40.7 61.7 102.4 Vulcan 040 11900 89 3583 7167 0.60233 762 40.7 61.4 102.1 Vulcan 040 11798 169 3583 7167 0.60734 1219 41.5 73.0 114.5 MHU500T 26555 45 14681 29361 1.10635 1219 41.5 72.2 113.7 MHU500T 26488 64 15997 31995 1.20836 1219 41.5 73.2 114.7 MHU500T 26572 46 14785 29569 1.11337 1219 41.5 72.2 113.7 MHU500T 26488 65 16045 32090 1.21138 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01139 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01140 1219 24.1 51.8 75.9 Menck3000 14265 23 7579 15158 1.06341 1219 24.1 51.8 75.9 Menck3000 14265 18 6372 12744 0.89342 1219 24.1 51.8 75.9 Menck3000 14265 21 7210 14419 1.01143 1219 24.1 51.8 75.9 Menck3000 14265 23 7579 15158 1.06344 1219.2 25.3 79.9 105.2 4600/150 22818 26 11023 22047 0.96645 1219.2 25.3 79.2 104.5 4600/150 22759 34 12472 24944 1.09646 1219.2 25.3 86.0 111.3 4600/150 23332 37 13348 26696 1.14447 1219.2 25.3 79.6 104.9 4600/150 22793 29 11636 23273 1.02148 1219.2 25.3 98.8 124.1 4600/150 24410 287 17319 34637 1.41949 1219.2 25.3 98.1 123.4 4600/150 24351 160 16582 33164 1.36250 1219.2 25.3 98.8 124.1 4600/150 24410 264 17295 34589 1.41751 1219.2 25.3 78.9 104.2 4600/150 22734 31 12045 24090 1.06052 762 25.3 76.8 102.1 3000/150 12705 26 9353 18705 1.47253 762 25.3 69.5 94.8 3000/150 12321 28 9394 18788 1.52554 762 25.3 60.0 85.3 3000/150 11821 18 6710 13420 1.13555 762 25.3 60.0 85.3 3000/150 11821 18 6710 13420 1.13556 762 25.3 80.2 105.5 3000/150 12884 24 9003 18006 1.39857 762 25.3 73.8 99.1 3000/150 12547 27 9635 19270 1.53658 762 25.3 78.0 103.3 3000/150 12769 25 9187 18374 1.43959 762 25.3 71.3 96.6 3000/150 12416 26 9434 18868 1.52060 914.4 35.2 63.5 98.7 Vulcan 530 14956 60 7822 15643 1.046

API Capacity GRLWEAP Results




61 914.4 35.2 63.5 98.7 Vulcan 530 14596 45 7601 15202 1.04262 914.4 35.2 63.5 98.7 Vulcan 530 14596 45 7601 15202 1.04263 914.4 35.2 63.5 98.7 Vulcan 530 14596 41 7546 15092 1.03464 762 23.3 66.1 89.4 Vulcan 040 12142 242 6333 12665 1.04365 762 23.3 76.4 99.7 Vulcan 040 12684 10 1703 3406 0.26866 762 23.3 68.9 92.2 Vulcan 040 12289 249 6351 12703 1.03467 762 36.9 61.0 97.9 Vulcan 340 8137 98 5192 10385 1.27668 762 36.9 61.0 97.9 Vulcan 340 8137 132 5518 11036 1.35669 762 36.9 61.0 97.9 Vulcan 340 8137 59 4819 9637 1.18470 762 25.6 84.7 110.3 Vulcan 340 14197 80 4061 8122 0.57271 762 25.6 83.5 109.1 Vulcan 340 13937 48 3606 7212 0.51772 762 25.6 84.1 109.7 Vulcan 340 14067 75 3990 7980 0.56773 762 43.4 57.3 100.7 MENCK 1500 11205 188 6873 13746 1.22774 762 43.4 56.4 99.8 MENCK 1500 11158 218 7240 14480 1.29875 762 43.4 57.0 100.4 MENCK 1500 11189 255 7432 14864 1.32876 914.4 35.1 50.3 85.4 Vulcan 530 7888 47 3944 7888 1.00077 914.4 35.1 58.7 93.8 Vulcan 530 8419 37 4210 8419 1.00078 914.4 35.1 59.1 94.2 Vulcan 530 8444 47 4222 8444 1.00079 914.4 35.1 59.1 94.2 Vulcan 530 8444 49 4222 8444 1.00080 762 27.6 60.1 87.7 Menck3000 11821 32 6442 12884 1.09081 762 27.6 59.7 87.3 Menck3000 11805 37 6918 13837 1.17282 762 27.6 60.2 87.8 Menck3000 11831 38 7038 14075 1.19083 914.4 28.7 66.2 94.9 70M 15127 12 6040 12080 0.79984 914.4 28.7 73.2 101.9 70M 15556 15 7093 14185 0.91285 914.4 28.7 66.2 94.9 70M 15127 10 5338 10676 0.70686 914.4 28.7 73.2 101.9 70M 15556 12 6040 12080 0.77787 914.4 20.5 62.0 82.5 Vulcan-560 18003 10 3362 6725 0.37488 914.4 20.5 70.0 90.5 Vulcan-560 18593 14 4857 9714 0.52289 914.4 20.5 62.0 82.5 Vulcan-560 18003 8 2452 4904 0.27290 914.4 20.5 70.0 90.5 Vulcan-560 18593 12 4267 8535 0.45991 1219 36.3 44.8 81.1 Menck 3000 13817 24 5941 11881 0.86092 1219 36.3 44.3 80.6 Vulcan 560 13817 23 5502 11005 0.79693 1219 36.3 44.3 80.6 Menck 3000 13817 26 6044 12088 0.87594 1219 36.3 44.3 80.6 Vulcan 560 13817 20 5061 10122 0.73395 1066.8 46.6 53.3 99.9 MENCK 3000 14016 37 8361 16722 1.19396 1066.8 46.6 50.5 97.1 Vulcan 560 12589 30 7323 14647 1.16397 1066.8 46.6 53.3 99.9 Menck 3000 14016 32 7885 15769 1.12598 1066.8 46.6 50.5 97.1 Menck 3000 12589 49 8568 17135 1.36199 1219.2 33.2 65.0 98.2 Vulcan 560 13811 38 9869 12829 0.929100 1219.2 33.2 80.0 113.2 Menck 3900 15521 95 14409 18731 1.207101 1219.2 33.2 65.0 98.2 Vulcan 560 13811 48 10633 13823 1.001102 1219.2 33.2 78.5 111.7 Menck 3900 15395 371 16038 20850 1.354103 914 22.0 70.0 92.0 Vulcan 530 14415 46 5586 11171 0.775104 914 22.0 70.0 92.0 Vulcan 530 14415 43 5299 10597 0.735105 914 22.0 70.0 92.0 Vulcan 530 14415 51 5968 11936 0.828106 914 22.0 70.0 92.0 Vulcan 530 14415 44 5394 10789 0.748107 914.4 28.9 56.1 85.0 Delmag D55 10429 92 4271 8543 0.819108 914.4 28.9 56.1 85.0 Delmag D55 10426 84 4083 8166 0.783109 914.4 28.9 57.0 85.9 Delmag D55 10854 128 4537 9074 0.836110 914.4 28.9 57.0 85.9 Delmag D55 10854 160 4741 9482 0.874111 914 35.1 55.2 90.2 Menck 3000 9951 41 7438 14876 1.495112 914 35.1 55.2 90.2 Menck 3000 9951 49 7718 15435 1.551113 914 35.1 55.6 90.7 Menck 3000 9976 28 6637 13274 1.331114 914 35.1 43.0 78.0 Menck 3000 9180 83 10081 20163 2.196115 1219 34.4 55.8 90.2 MHU600 16231 24 7759 15518 0.956116 1219 34.4 56.1 90.5 MHU600 16257 30 8621 17242 1.061117 1219 34.4 56.0 90.4 MHU600 16248 24 7759 15518 0.955118 1219 34.4 56.0 90.4 MHU600 16248 24 7759 15518 0.955119 762 40.0 83.6 123.6 Vulcan 530 13489 117 6745 13489 1.000120 762 40.0 83.6 123.6 Vulcan 530 16852 466 8426 16852 1.000121 762 40.0 83.6 123.6 Vulcan 530 13489 115 6745 13489 1.000




122 914 41.4 61.6 103.0 Menck MRBS 1500 15811 60 4997 9995 0.632123 914 41.4 61.7 103.1 Menck MRBS 1500 15811 77 4997 9995 0.632124 914 41.4 61.6 103.0 Menck MRBS 1500 15811 46 4997 9995 0.632125 914 41.4 61.6 103.0 Menck MRBS 1500 15811 58 4997 9995 0.632126 1219 41.3 50.5 91.8 Vulcan 530 15184 10 517 1033 0.068127 1219 41.3 50.5 91.8 Vulcan 530 15184 43 5875 11751 0.774128 1219 41.3 52.8 94.1 Vulcan 530 16287 29 4533 9066 0.557129 1219 41.3 50.5 91.8 Vulcan 530 15184 9 1880 3760 0.248130 762 30.6 58.8 89.4 Vulcan 020 9519 56 2978 5955 0.626131 762 30.6 58.8 89.4 Vulcan 020 9519 66 3063 6126 0.644132 762 30.6 57.9 88.5 Vulcan 020 9160 66 3063 6126 0.669133 762 27.6 51.8 79.4 Vulcan 040 8551 319 5867 11733 1.372134 762 27.6 61.0 88.6 Vulcan 040 9025 20 2793 5586 0.619135 762 27.6 50.4 78.0 Vulcan 040 8551 302 5806 11612 1.358136 762 34.8 65.9 100.7 Vulcan 040 8396 42 4433 8866 1.056137 762 34.8 82.8 117.6 Vulcan 040 13776 38 3884 7767 0.564138 762 34.8 65.8 100.6 Vulcan 040 8396 43 4485 8971 1.068

Appendix F

COMMON STATISTICAL DISTRIBUTIONS USED IN THIS RESEARCH

From the databases collated in this research, the histograms and basic statistics of the

results are obtained. Then, several competing probability distributions were fitted to

the data by the method of moments. Finally, the observed data and models were

plotted and quantitative tests on the goodness-of-fit were conducted. This section

presents a number of commonly used statistical distributions and defines their

parameters.

F.1 Normal Distribution The normal probability distribution was investigated because historically it has been

favored by researchers more than any other distribution. The versatility of the

normal distribution arises from its very convenient property that if two normally

distributed variables are added, their sum also has a normal distribution. Corotis and

Doshi (1977) suggested that the normal model is often adopted with little or no

physical justification due to its ability to serve as a good approximation to many

other distributions. Frequently, it has been used simply because an observed

histogram is roughly bell-shaped and approximately symmetric.

In some situations this may be justified on the basis that it is the limiting form of

several other distributions (Lewis, 1996). For example, if a random variable x can be

expressed as a sum of the random variables, xi, i =1, 2,…., N where no one of them is

dominant, then x can be described as a normal distribution, even though xi is

described by non-normal distributions that may not even be the same for different

values of i (Lewis, 1996). More importantly, if the general characteristics, rather

than the details of the shape, are of interest, the normal distribution may serve as a

widely tabulated, if rough, approximation to empirical data.

While the normal distribution may often provide a reasonable approximation, it may

not be appropriate to represent situations such as the case when the data exhibit

significant skewness. Moreover, if the interest is in the “tails” of the distribution, the

use of a normal distribution is likely to lead to large errors. Due to the importance of

the tail regions in structural reliability, it was appropriate to test alternative

distributions.

F.2 Lognormal Distribution The lognormal distribution was investigated in this research to model skewness in

the observed data. A variable that is described by the lognormal distribution will

have logarithms that follow a normal distribution. While the lognormal distribution

is not quite as convenient a mathematical form as the normal, the simple relationship

between the two retains numerical tractability.

The lognormal distribution defines a variable that is limited to positive values and

exhibits a parameter-determined positive skewness. It may be derived as the limiting

distribution of the product of a large number of relatively independent variables, and

as such it exhibits regeneration under multiplication. These characteristics support

the investigation of the lognormal distribution.

A variable Y has a lognormal distribution if �YZ ln� has a normal distribution with

mean Z� and standard deviation Z .

For a variable Y which is lognormally distributed with �YZ ln� , the probability

density function (PDF) for Z is defined as:

� ��

��

� ��

2

2

ln21exp

21

Z

Z

Zy

yy

yf

��

Equation F- 1

Where z� = mean value of the normal variable Z

2z = variance of the distribution in Z

The median of the variable Y is

)exp(~ZY �� Equation F- 2

The mean of the lognormally distributed variable is:

��

�� 22

21exp)

21exp(~

ZZZYY � Equation F- 3

The variance of the lognormally distributed variable Y is:

� �� 1exp2exp 22 �� ZzzVARIANCE � Equation F- 4

The coefficient of variation of the lognormally distributed variable Y is:

� ZZYCOV �� 1exp 2 Equation F- 5

F.3 Weibull Distribution A third distribution commonly encountered is the Weibull distribution. For a random

variable X that has a Weibull distribution (2 parameters), the CDF with respect to X

is given by:

��

�

��

��

��

mxXF!

exp1 Equation F- 6

The distribution is put in a form for probability plotting by first solving for �F�11

and then taking the logarithm twice, which leads to (Lewis, 1996):

��

��

��

�XF

y1

1lnln Equation F- 7

and

�Xx ln� Equation F- 8

The mean X of the Weibull distributed variable is:

��

�� "��

mX 11! Equation F- 9

The standard deviation of the variable X is:

21121 ��

�� "��

��

�� "��

mmX ! Equation F- 10

where m is the shape parameter and! is the scale parameter.

� � 53 12601

3601

121ln

212ln

21ln

zzzzzzz ��

��

�� " � Equation F- 11

The shape parameter is equal to the slope of the line fitting the data and the scale

parameter is estimated in terms of the slope and intercept as follows: (Lewis, 1996):

�dataSLOPEam �� Equation F- 12

� � ��

�

��

��

��

��

dataSLOPEdataINTERCEPT

ab expexp! Equation F- 13

Where a = Slope of the line fitting the data

b = Intercept of the line fitting the data

m = Shape parameter

! = Scale parameter

F.4 Extreme Value (Gumbell Distribution) A fourth distribution type is the Gumbell distribution. For extreme value

distributions, or more precisely asymptotic extreme value distributions, the CDF is

given by:

�#$%

&'(

��

��

��

��

��!

uxXF expexp Equation F- 14

The mean is given by:

!�� u Equation F- 15

and

22

2

6!� � Equation F- 16

where 5772157.0��

� !!ux

XFy ��

�

��

��

11lnln Equation F- 17

The distribution parameters may be estimated in terms of the slope and intercept to

be (Lewis, 1996):

�dataSLOPEa11

��! Equation F- 18

� �dataSLOPE

dataINTERCEPTabu �� Equation F- 19

Appendix G

STRUCTURAL RELIABILITY ANALYSIS (SRA)

The traditional formulation of SRA is element-based in that it starts from a single failure

mode of a structural element. Consideration of randomness (including time-dependent

randomness) is confined to load variables (including environmental variables), geometric

variables, and material properties and in the mathematical models (e.g., models relating

loads to load-effects). Uncertainties in the failure criteria itself are sometimes

considered. However, those arising from gross human errors are not.

The SRA results obtained are often referred to as notional reliability estimates.

Depending on the degree of approximation and on the format of application, reliability

analysis methods can be categorized as Level 1, Level 2 and Level 3. In addition, the term

Level 4 signifies methods that incorporate economic as well as social data (Madsen et al.,

1986). However, the boundaries separating the different “levels” are not distinct, the

terminology has become archaic.

Level 1 is a semi-probabilistic design process in which partial factors are defined and

applied to characteristic values of loads and resistance. A level 1 structural design is

commonly called limit state design. Level 1 is used to incorporate the results of

reliability methods to engineering practice, although the reliability aspects are not

transparent to the designer.

Level 2 is known as First Order Reliability Method (FORM). The measure of reliability

is based on the reliability index. In level 2 methods, design variables can have any form

of probability distributions. Typically, Level 2 methods use two values to describe each

uncertain variable, i.e. mean and variance. FORM has been widely adopted in the

structural community for RBD (e.g., Allen, 1975; Ellingwood et al., 1980; ACI, 1983;

AISC, 1996) and it appears in many of the recently proposed RBD codes for foundations

(e.g., Barker et al., 1991; Berger and Goble, 1992; Becker et al., 1993).

The Second-order methods (SORM) improve the accuracy of first-order probability

estimates. The presence of significant differences between the results of the two methods

may suggest the use of Monte Carlo simulations to confirm the probability of failure

estimate. For most practical reliability applications, there is usually little difference

between FORM and SORM estimates. Moses (1990) concluded that FORM and SORM

provided comparable results for structural problems and Lacasse (1996) confirmed the

similarity in results between FORM and SORM for Geotechnical problems.

Level 3 has multi-dimensional joint probability distributions which enables the

determination of the “exact” probability of failure for a structure or structural component.

Level 3 makes use of a full probabilistic description of the joint occurrence of the various

quantities which affect the response of the structure and takes account of the true nature

of the failure domain. System effects and time-variance may be incorporated. Level 3

methods include numerical integration methods such as Monte Carlo simulation. The

application of Monte Carlo simulation employs commercial software such as @RISK

software.

The Monte-Carlo simulation (MCS) offers a direct method for estimating the failure

probability. In essence, the technique involves sampling a set of values of the basic

variables at random from the probability density function and evaluating the failure

function for the values to see if failure occurs. By generating a large number of samples

or trials, the probability density function is simulated and the ratio of the number of trials

leading to failure to the total number of trials tends to the exact probability of failure.

The drawback with crude Monte Carlo simulation is the computational effort involved.

To produce a reasonably accurate estimate of the failure probability at least 100/Pf trials

are required. For probability of failure around 10-4, this requires that at least one million

trials be generated. It can be made more efficient with Latin Hypercube Sampling (LHS)

which is a Monte-Carlo simulation optimized by “organized” sampling. It reduces the

number of simulations required for a reliable distribution of the response. If used

intelligently, Monte Carlo methods are a readily understood and easily applied tool and

can be used to produce ‘exact’ answers to problems that cannot be accurately modeled

using FORM or SORM such as load combinations and time-varying problems.

Level 4 includes any of the above together with economic data for prediction of

maximum benefit or minimum cost. DNV (1992) defines level 4 as a method that

compares a structural prospect with a reference prospect according to the principles of

engineering economic analysis under uncertainty. Such decision analysis considers costs

and benefits of construction, maintenance, repair and consequences of failure.

Appendix H

APPROACHES FOR PREDICTING AXIAL PILE CAPACITY

H.1 EMPIRICAL APPROACH – STATIC METHOD

The empirical approach was derived from industry review of static loading tests carried

out on onshore piles and forms the basis of the pile design method in API RP2A-LRFD

(1993). Evaluation of the ultimate capacity of piled foundation using API RP2A-LRFD

(1993) formulation typically uses soil parameters obtained from laboratory tests

performed on “undisturbed” samples. For an open-ended pipe pile, the capacity is

generally taken as the sum of shaft resistance and end bearing values computed from unit

skin friction and unit toe resistance applied to the pile size and length as per the following

equation:

wpiissult AqAfAfQ �)�)� Equation H- 1

Where, ultQ = Ultimate static capacity

sf = Unit outside shaft friction

sA = Outside shaft area of the pile

if = Unit inside shaft friction

iA = Inside shaft area of pile

pq = Unit end bearing capacity

wA = Cross-sectional area of steel wall at toe of pile

The API RP2A method assumes that the shaft resistance and end bearing are

simultaneously fully activated. Basically, this approach computes “long-term” static pile

capacity since it utilizes soil parameters that represent natural ground conditions

unaffected by the pile driving process.

In Equation 1, the skin (shaft) friction of the piled foundation is integrated over the

respective pile lengths. The effects of local fluctuations of the skin friction from point to

point along each pile average out over the length of the pile. The axial capacity of piles

is thus calculated from average skin friction properties only without considering any local

variability.

The values assigned to the various parameters shown in Equation 1 depend on the soil

type, behavior of soil plug and interpretation of nominal values. API RP2A-LRFD (1993)

defines two types of soils, granular and cohesive soils.

For granular soils, the unit skin friction, sf , is a function of the effective overburden

pressure, * the coefficient of lateral earth pressure, K, and the angle of skin friction, �,

and is expressed as:

limtan fKf s +� � Equation H- 2

For a constant K, the above equation suggests that the unit skin friction increases linearly

with depth. However, many investigators (e.g. Vesic, 1967; Tavenas, 1971) observed

that the unit skin friction reaches a limiting value and thereafter remains essentially

constant with further increase in depth. Hence, usually a limiting value of unit skin

friction, flim, is specified. API RP2A-LRFD (1993) specifies limits for the engineering

parameters for cohesionless siliceous soils as shown in Table H- 1.

The observation that sf does not increase indefinitely with depth but reaches a limiting

value was explained by many investigators in terms of the ‘arching effect’ (e.g. Vesic,

1969; Reese and Cox, 1976). This effect curtails the increase of lateral earth pressure

with depth.

Further, for granular soils, the unit end bearing pq is expressed in terms of the overburden

pressure and a bearing capacity factor Nq as follows

qp Nq � Equation H- 3

�,fNq � Equation H- 4

Where , = Frictional angle of shearing resistance

Arching has also been cited as one of the factors causing the end bearing to attain limiting

value at large depths. In addition, Ranganatham and Kaniraj (1978) observed that sand

grains crush under the pile tip, even in non-carbonate sands. Vesic (1977) postulated that

Nq is a function of the rigidity of the sand. At high stress, crushing causes a decrease in

the rigidity index which leads to a reduction in Nq and hence pq .

For cohesive soils, the unit shaft friction f and the unit end bearing uq are expressed as

follows:

ucf .�� Equation H- 5

uu cq .9� Equation H- 6

Where � = A dimensionless factor derived as outlined below

uc = Undrained shear strength of the soil at the point in question

0.1+��

��

uc Equation H- 7

21

.5.0 ��

��

� uc Equation H- 8

0.1-��

��

uc Equation H- 9

21

.5.0 ��

��

� uc Equation H- 10

Under certain conditions, the accumulated internal skin friction may exceed the ultimate

static capacity of the soil below the toe of the pile. The pile then behaves as if it is close-

ended or plugged. Its static capacity is then given by:

wpssult AqAfQ �)� Equation H- 11

Static pile capacity calculation must determine whether an open pile section will exhibit

plugged or unplugged behavior. O’Neill and Raines (1991) and Paikowsky and Whitman

(1990) suggested that plugging of an open pile in medium dense to dense sands generally

begins at a pile penetration to pile diameter ratio of 20, but can be as high as 35 for piles

in soft to stiff clays.

When an open pile section is driven it may behave as low displacement piles and “cookie

cut” through the soil, but may also act as a displacement pile if a soil plug forms near the

pile toe. The behavior of the soil plug is complex and is different under dynamic and

static loading. Stevens (1988) reported that plugging of pipe piles in clay does not occur

during driving if pile accelerations (along the plug zone) are greater than 22g. Holloway

and Beddard (1995) reported that hammer blow size (impact force and energy) influences

the dynamic response of the soil plug. With a large hammer blow, the plug “slipped’

under the dynamic event whereas under a lesser hammer blow the pile encountered low

resistance typical of a plugged condition.

The key soil parameters required for the application of the static method are the

submerged unit weight, the friction angle of granular soil and the undrained shear

strength of cohesive soil. In engineering practice, these values are usually provided in

geotechnical reports. The application of the LRFD method requires definition of nominal

values in those geotechnical reports. Unfortunately, the importance of defining nominal

soil strengths has frequently been overlooked (CIRIA, 1977; Been and Jefferies, 1993;

Dahlberg and Ronald, 1993).

Existing procedures for the selection of nominal soil strengths are neither well defined

nor followed uniformly by all engineers (Whitman, 1984). Goble (1999) observed a

similar trend and found that different calculation methods are preferred in different

localities or even by different geotechnical engineers in the same locality. The manner in

which the resistance factor is incorporated with the nominal values in the prediction

equation is also highly varied (Kulhawy 1984, 1996).

Despite the confusion in the consistent selection of soil parameters, the definition of

nominal soil strengths for the reliability-based design (RBD) format should be consistent

with those that are used in traditional foundation engineering practice. Terzaghi and Peck

(1948) recommended the use of an average value of the measured strength within a

significant depth from each boring, and then using the smallest average for design. The

Norwegian Petroleum Directorate (NPD) prescribes the use of a “conservatively assessed

mean value” for the nominal shear strength (Dahlberg and Ronald, 1993). The

probability-based Swedish Building Code (SC-89) also defines the nominal value of soil

strength as the mean of the measurements (Bengtson et al., 1993). Moses and Larrabee

(1988) suggested that nominal values of the soil parameters be close to the mean in their

calibration of the API RP2A-LRFD (1993). Criswell and Vanderbilt (1987) used the

mean value for the RBD of transmission structures.

A number of Authors, including DiGioia and Rojas (1990), European Committee for

Standardization (1993) and Been and Jefferies (1993), advocated an alternative definition

for the nominal value that is based on the concept of the exclusion limit. However, the

use of a small exclusion limit may not be appropriate for foundation design for various

reasons. First, the amount of data required for the reliable determination of a 5 to 10%

exclusion limit is typically much larger than the number of measurements taken during

project execution (Bengtsson, et al., 1993; Been and Jefferies, 1993; Lo and Li, 1993).

For example, Bengtsson et al. (1993) estimated that approximately 200 measurements

were required to establish the 5% exclusion limit on soil strength, while the determination

of the mean only required about 20 measurements. Another shortcoming of the exclusion

limit was the requirement for probability computations which are currently not performed

in engineering practice. The main purpose of probability-based codes is to relieve

practicing engineers from unfamiliar probability calculations so that focus can be placed

on the geotechnical aspects of the problem. Use of the exclusion limit introduces

unnecessary complications and partially undermines the objective of calibrating

resistance factors. In addition, the exclusion limit concept is less intuitive than that for

the mean value. Hence, it would be reasonable to say that most foundation engineers feel

more comfortable using the mean value. The use of a mean value provided a physical

feel from past experience of working with realistic soil strength parameters (Olson, et al.,

1989; Bengtsson, et al., 1993).

Regardless of the choice, it is important to emphasize that the definition of nominal

values cannot be left to the judgment of the engineer, because the load and resistance

factors are not independent of the nominal values.

In this research, nominal parameters of soil strength were defined at the mean for reasons

of simplicity and compatibility with foundation design practice. The use of the mean

value in this research is also consistent with the approach used by Moses and Larrabee

(1983) in the calibration of API RP2A-LRFD (1993).

H.2 EMPIRICAL APPROACH – DYNAMIC METHOD

McVay et al. (2000) presented several dynamic methods to predict pile capacity

including ENR Formula (ENR, 1965), Gates Formula (Gates, 1957), FDOT Formula

(FDOT, 1991), Paikowsky’s method (Paikowsky, 1994) and the Japanese method (1996).

The use of each method is associated with a specific factor of safety, ranging from 1.0

(for the FDOT Formula) to 6.0 (for the ENR Formula).

Pile capacities determined from dynamic formulae have shown poor correlations and

wide scatter when statistically compared with static loading test results. According to

Hannigan et al. (1997), the simple dynamic formula is fundamentally incorrect due to the

modeling of each component within the pile driving process (driving system, soil and

pile). Dynamic formulae poorly represent the driving system and the energy losses of its

components. The implicit assumption of rigid pile in the dynamic formula neglects pile

axial stiffness and length effects on driveability, and further assumes that soil resistance

is constant and instantaneous at the impact force. Consequently, the dynamic empirical

method was not used in this research.

H.3 ENGINEERING MECHANICS APPROACH – WAVE EQUATION ANALYSIS (WEA)

Taking advantage of wave propagation theories developed in the 1950s, Smith (1960)

developed a discrete numerical solution with realistic hammer, pile and soil models,

which became known as the “Wave Equation Method”. The wave equation method

solves the one-dimensional partial differential equation for an idealized hammer-pile-soil

system.

The one-dimensional WEA offered the only analytical tool which incorporates so many

important pile-driving variables into a rational framework to arrive at a realistic estimate

of the pile capacity using back-analysis procedure. The one-dimensional WEA of pile

driving is based on the discrete element idealization of the hammer-pile-soil system. A

schematic of the wave equation hammer-pile-soil model is shown in Figure H- 1.

As the WEA commences, a calculated or estimated ultimate capacity ultR from user

specified values is distributed among the elastic-plastic springs along the shaft and toe.

Similarly, user specified damping factors are assigned to shaft and toe to represent the

dynamic soil resistance. The analysis then proceeds by calculating a ram velocity using

the input hammer efficiency and stroke. The ram movement causes displacements of

helmet and pile head springs and therefore compressions (or extensions) and related

forces acting at the top and bottom of the segments. Furthermore, the movement of a pile

segment causes soil resistance forces. A summation of all forces acting on a segment

divided by its mass yields the acceleration of the segment. The product of acceleration

and time step summed over time is the segment velocity. The velocity multiplied by the

time step yields a change of segment displacement which then results in new spring

forces. These forces divided by the pile cross sectional area at the corresponding section

equal the stress at that point.

Similar calculations are made for each segment until the accelerations, velocities and

displacements of all segments are calculated using the time step. The analysis then

repeats for the next time step using the updated motion of the segments from the previous

time step. From this process, the accelerations, velocities, displacements, forces and

stresses of each segment are computed over time. Additional time steps are analyzed

until the pile toe begins to rebound.

The permanent set (mm) of the pile toe is calculated by subtracting a weighted average of

the shaft and toe quakes from the maximum pile toe displacement. The inverse of the

permanent set is the driving resistance (blow count) that corresponds to the input ultimate

capacity. By performing wave equation analyses over a wide range of ultimate

capacities, a curve or “bearing graph” can be plotted which relates ultimate capacity to

driving resistance. The wave equation bearing graph is associated with a single driving

system, hammer stroke, pile type, soil profile and a particular pile length. If any of the

above items is changed, the bearing graph will also change.

Table H- 1: API RP2A recommended values of limiting skin friction and end bearing of piled foundations in cohesionless siliceous soils

Limiting Skin

Friction

Limiting Unit

End Bearing

Density Soil Description

kPa MPa

Very Loose Sand

Loose Sand-Silt

Medium Silt

47.8 1.9

Medium Sand

Dense Sand-Silt 81.3 4.8

Dense Sand

Very Dense Sand-Silt 95.7 9.6

Dense Gravel

Very Dense Sand 114.8 12.0

Figure H- 1: Typical mathematical model of wave equation analysis (GRLWEAP Manual, 1995). The hammer, helmet and pile are modeled by a series of segments each consisting of a concentrated mass and a weightless spring

Appendix I

APIPILE MANUAL

Parameter Description

Outer Diameter, OD Outside diameter that was used in the computations

Inner Diameter, ID Equal the outside diameter minus 2 x wall thickness.

The spreadsheet computes skin friction on both outside

and inside area for pipe piles. In clay layers, the

remoulded shear strength was used to compute the inside

skin friction.

Total Length, L Maximum pile penetration that is expected for the pile.

The spreadsheet computes side resistance and end

bearing at every depth interval until reaching the total

length.

Pile Material Only applicable to steel piles.

Modulus of Elasticity, E Elastic modulus of the pile. The elastic modulus of the

pile is used for load versus settlement analysis.

Soil Layers This column allows the different types of soil and

material properties to be specified for the computations.

Layer This is a sequential number that is provided to each soil

layer.

Soil Type Two types of soils may be specified for internal

generation of soil response under axial loading in the

spreadsheet, namely sand or clay. These values

correspond to the vertical coordinate at the bottom of

each soil layer starting from the top layer. The origin of

coordinates for these values is located at the ground

surface. As a minimum requirement, the bottom of the

last soil layer must be two pile diameters deeper than the

depth of the modeled pile.


Layer Data This allows the definition of soil properties for each

soil stratum. The spreadsheet requires different input

parameters for each type of soil. A detailed description

of the parameters needed for sand and clay layers are

described below.

Maximum Side Friction In this column entry the maximum permissible value of

skin-friction transfer for a given stratum is specified. If

a value for the maximum-permissible and skin friction

is entered, the spreadsheet compares the internally-

computed value with the maximum provided and uses

the smaller of the two for the final computation. If no

restriction on the computed value is required, a very

large figure can be entered to suppress this option so

that the internally-computed values are used. The

limits on side friction transfers are identified in this

thesis for carbonate soils.

Maximum End Bearing In this column entry the maximum permissible value of

transfer in end bearing for a given stratum is specified.

If values for the maximum-permissible and end bearing

are specified, the spreadsheet compares the internally-

computed value with the maximum provided and use

the smaller of these two for the final computation. If

restriction on the computed values is required, a large

amount can be entered to suppress this option and

always use the internally-computed values.

Sand Layer Data This column is used to describe properties of the sand

layers. The user inputs soil properties for the top and

bottom of each soil layer. The spreadsheet interpolates

the data linearly between those two depths.


Unit Weight This column entry corresponds to values of effective unit

weight of the soil. Values for the top and bottom of the

sand layer are entered in standard units of force per unit

volume (kN/m3).

Friction Angle This column entry corresponds to values of the internal

angle of friction (also known as angle) for the top and

bottom of the sand layer. The values of internal angle of

friction are entered in standard units of degrees.

Lateral Earth Pressure, K This column reflects values for the lateral earth pressure

coefficient for the top and bottom of the sand layer. The

earth pressure coefficient, Ko, is used to calculate the

skin friction in granular soils. A Ko of 0.8 is

recommended for open-ended pipe piles that are driven

unplugged for loadings in both tension and compression.

A K of 1.0 is recommended for full-displacement piles.

Bearing Capacity Factor,

Nq

This factor is used to calculate the end-bearing capacity

of piles in granular soils.

Clay Layer Data This column is used to describe the properties of clay

layers. The required properties for clay layers are

explained below.

Location The user inputs soil properties for the top and bottom of

each soil layer. Different parameters for the top and the

bottom of each layer may be entered.

Unit Weight This column entry corresponds to values of total unit

weight of the soil. Values for the top and bottom of the

sand layer were entered in standard units of force per

unit volume (kN/m3).


Undrained Shear

Strength

This column represents the input values for the

unconfined-undrained shear strength, cu, at the top and

bottom of the clay layer. These values were taken as one

half of the compression strength of samples obtained

from unconsolidated-unconfined triaxial tests.

Remoulded Shear

Strength

When a pipe pile is driven into clay soils, the clay inside

the pipe forms a plug. The plugged clay may be

remoulded during the driving process. This column

represents the values for the shear strength used for

computing the side friction from the remoulded soil plug

inside a steel pipe.

Blow counts (Optional) This entry corresponds to the number of blow counts

obtained at the top and bottom of the sand layer while

performing a Standard Penetration Test (SPT). An

optional input blow counts obtained from SPT tests in

cases when the vsalues of internal friction angle are not

readily available. When a value different than zero for

the friction angle is specified, the values for blow count

are not used in the computations and may be entered as

zero. Gibbs and Holtz (1957) investigated the

relationship between the number of blow counts and soil

internal friction angle for various overburden pressures

as shown in Table I-1, which can be used to convert

between the values of blow counts from SPT to

equivalent values of internal friction angle.

Table I-1: Relationship between number of blow counts N and soil internal friction angle � (Gibbs and Holtz, 1957)

N Overburden stress, lb/in2

(SPT) 0 20 40 Blows/ft Value of �, degrees

0 0 2 32 4 34 6 36 30

10 38 32 31 15 42 34 32 20 45 36 34 25 37 35 30 39 36 35 40 36 40 41 37 45 42 38 50 44 39 55 45 39 60 40 65 41 70 42 75 42 80 43 85 43 90 44

Appendix J

SACS INPUT FILES IN ASCII FORMAT

LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS – 1 YEAR INPUT DATA OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1 MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4

MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01 MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01

MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02 MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04

MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05 MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01

MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02 MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATE PLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164. JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000

JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75. JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132

JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 23.29261.00 8.50 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000 CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 23.29261.00 8.50 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 23.29261.00 8.50 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000

CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 23.29261.00 8.50 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 23.29261.00 8.50 180.00 D 0.00 5.00 72MS10 1 0 LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 23.29261.00 8.50 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 23.29261.00 8.50 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000

CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 23.29261.00 8.50 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA ***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2

LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000 ***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2

LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END

LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS 5 year INPUT DATA OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1

MEMBER 107 207 LG1 MEMBER 201 301 LG2 MEMBER 203 303 LG2 MEMBER 205 305 LG2 MEMBER 207 307 LG2 MEMBER 301 401 LG3 MEMBER 303 403 LG3 MEMBER 305 405 LG3 MEMBER 307 407 LG3 MEMBER 401 501 LG4 MEMBER 403 503 LG4 MEMBER 405 505 LG4 MEMBER 407 507 LG4 MEMBER 501 601 LG5 MEMBER 503 603 LG5 MEMBER 505 605 LG5 MEMBER 507 607 LG5 MEMBER 601 701 LG6 MEMBER 603 703 LG6 MEMBER 605 705 LG6 MEMBER 607 707 LG6 MEMBER 701 801 LG7 MEMBER 703 803 LG7 MEMBER 705 805 LG7 MEMBER 707 807 LG7 MEMBER 102 202 PL1 MEMBER 104 204 PL1 MEMBER 106 206 PL1 MEMBER 108 208 PL1 MEMBER 202 302 PL2 MEMBER 204 304 PL2 MEMBER 206 306 PL2 MEMBER 208 308 PL2 MEMBER 302 402 PL3 MEMBER 304 404 PL3 MEMBER 306 406 PL3 MEMBER 308 408 PL3 MEMBER1 402 501 PL4 MEMBER OFFSETS MEMBER1 404 503 PL4 MEMBER OFFSETS MEMBER1 406 505 PL4 MEMBER OFFSETS MEMBER1 408 507 PL4 MEMBER OFFSETS MEMBER1 209 212 T01

MEMBER OFFSETS 10.00-13.32 -11.56 10.00 MEMBER1 210 209 T01 MEMBER OFFSETS -11.56-10.00 10.00 13.32 MEMBER1 211 210 T01 MEMBER OFFSETS -10.00 13.32 11.55-10.00 MEMBER1 212 211 T01 MEMBER OFFSETS 11.55 10.00 -10.00-13.32 MEMBER1 301 303 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 301 305 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 301 309 T01 MEMBER OFFSETS 15.40 14.39 MEMBER1 303 307 T01 MEMBER OFFSETS 21.16 -21.16 MEMBER1 303 309 T01 MEMBER OFFSETS -15.54 14.51 MEMBER1 303 401 T01 MEMBER OFFSETS -24.38 4.09 32.75 21.00 -3.53-28.21 MEMBER1 305 307 T01 MEMBER OFFSETS 21.00 -21.10 MEMBER1 305 309 T01 MEMBER OFFSETS 15.40-14.39 MEMBER1 307 309 T01 MEMBER OFFSETS -15.54-14.51 MEMBER1 307 403 T01 MEMBER OFFSETS -4.75-27.10 47.52 3.04 17.36-30.43 MEMBER1 405 301 T01 MEMBER OFFSETS -17.36-30.43 27.10 47.52 MEMBER1 407 305 T01 MEMBER OFFSETS -18.27 3.54-28.35 21.00 -4.07 32.59 MEMBER1 201 209 T02 MEMBER OFFSETS 21.16 MEMBER1 201 212 T02 MEMBER OFFSETS 21.00 MEMBER1 201 303 T02 MEMBER OFFSETS 21.00 4.44 35.50-18.05 -3.81-30.51 MEMBER1 203 211 T02 MEMBER OFFSETS 21.16 MEMBER1 203 307 T02 MEMBER OFFSETS -3.83 25.95 38.29 2.64-17.87-26.37 MEMBER1 205 210 T02 MEMBER OFFSETS 21.00 MEMBER1 209 205 T02 MEMBER OFFSETS -21.16 MEMBER1 210 207 T02

MEMBER OFFSETS -21.10 MEMBER1 211 207 T02 MEMBER OFFSETS -21.16 MEMBER1 212 203 T02 MEMBER OFFSETS -21.10 MEMBER1 301 205 T02 MEMBER OFFSETS 17.87-26.37 -25.95 38.30 MEMBER1 305 207 T02 MEMBER OFFSETS 21.00 3.80-30.37-24.67 -4.46 35.67 MEMBER1 401 403 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 401 405 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 401 409 T03 MEMBER OFFSETS 17.32 11.97 MEMBER1 403 407 T03 MEMBER OFFSETS 21.16 -21.16 MEMBER1 403 409 T03 MEMBER OFFSETS -17.48 12.08 MEMBER1 405 407 T03 MEMBER OFFSETS 21.00 -21.10 MEMBER1 405 409 T03 MEMBER OFFSETS 17.32-11.97 MEMBER1 407 409 T03 MEMBER OFFSETS -17.48-12.08 MEMBER1 101 109 T04 MEMBER OFFSETS 12.67 16.88 MEMBER1 103 109 T04 MEMBER OFFSETS -12.76 17.01 MEMBER1 105 109 T04 MEMBER OFFSETS 12.67-16.88 MEMBER1 107 109 T04 MEMBER OFFSETS -12.76-17.01 MEMBER1 101 103 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 101 105 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 101 205 T05 MEMBER OFFSETS 25.80 37.10 -17.56-28.84 MEMBER1 103 107 T05 MEMBER OFFSETS 21.16 -21.16 MEMBER1 103 201 T05 MEMBER OFFSETS -24.99 4.86 38.90 21.00 -4.11-32.88 MEMBER1 105 107 T05 MEMBER OFFSETS 21.00 -21.10 MEMBER1 105 207 T05

MEMBER OFFSETS 21.00 -4.73 37.83-17.91 3.99-31.92 MEMBER1 107 203 T05 MEMBER OFFSETS -3.70-25.78 36.98 2.87 17.57-28.74 MEMBER1 201 202 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 203 204 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 205 206 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 207 208 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 301 302 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 303 304 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 305 306 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 307 308 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER1 401 402 W.BSK 000000100111 F MEMBER OFFSETS 0.74 5.95 MEMBER1 403 404 W.BSK 000000100111 F MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 405 406 W.BSK 000000100111 F MEMBER OFFSETS -0.74 5.95 MEMBER1 407 408 W.BSK 000000100111 F MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 701 714 W01 MEMBER 705 717 W01 MEMBER 714 715 W01 MEMBER 715 703 W01 MEMBER 717 718 W01 MEMBER 718 707 W01 MEMBER 801 834 W01 MEMBER 803 836 W01 MEMBER 805 837 W01 MEMBER 807 839 W01 MEMBER 834 835 W01 MEMBER 835 803 W01 MEMBER 837 838 W01 MEMBER 838 807 W01 MEMBER 701 705 W02 MEMBER 703 707 W02 MEMBER 705 720 W02 MEMBER 707 723 W02 MEMBER 709 701 W02

MEMBER 710 714 W02 MEMBER 711 715 W02 MEMBER 712 703 W02 MEMBER 714 717 W02 MEMBER 715 718 W02 MEMBER 717 721 W02 MEMBER 718 722 W02 MEMBER 801 805 W02 MEMBER 803 807 W02 MEMBER 805 840 W02 MEMBER 807 843 W02 MEMBER 829 801 W02 MEMBER 830 834 W02 MEMBER 831 835 W02 MEMBER 832 803 W02 MEMBER 833 836 W02 MEMBER 834 837 W02 MEMBER 835 838 W02 MEMBER 836 839 W02 MEMBER 837 841 W02 MEMBER 838 842 W02 MEMBER 839 844 W02 PGRUPPGRUP P01 0.3750I29.000 0.25036.000 490.000 PLATEPLATE AAAC 801 834 805 837P01 0 PLATE AAAD 834 835 837 838P01 0 JOINTJOINT 101 -24. -50. -261. -3.000 JOINT 102 -24. -50. -261. -3.000 PILEHD JOINT 103 51. -50. -261. 4.800 -3.000 JOINT 104 51. -50. -261. 4.800 -3.000 PILEHD JOINT 105 -24. 50. -261. 3.000 JOINT 106 -24. 50. -261. 3.000 PILEHD JOINT 107 51. 50. -261. 4.800 3.000 JOINT 108 51. 50. -261. 4.800 3.000 PILEHD JOINT 109 13. 0. -261. 8.400 JOINT 201 -24. -38. -164. -1.500 JOINT 202 -24. -38. -164. -1.500 JOINT 203 41. -38. -164. 8.400 -1.500 JOINT 204 41. -38. -164. 8.400 -1.500 JOINT 205 -24. 38. -164. 1.500 JOINT 206 -24. 38. -164. 1.500 JOINT 207 41. 38. -164. 8.400 1.500 JOINT 208 41. 38. -164. 8.400 1.500 JOINT 209 -24. 0. -164.

JOINT 210 8. 38. -164. 10.296 1.500 JOINT 211 41. 0. -164. 8.400 JOINT 212 8. -38. -164. 10.296 -1.500 JOINT 301 -24. -26. -69. -3.000 JOINT 302 -24. -26. -69. -3.000 JOINT 303 32. -26. -69. 2.400 -3.000 JOINT 304 32. -26. -69. 2.400 -3.000 JOINT 305 -24. 26. -69. 3.000 JOINT 306 -24. 26. -69. 3.000 JOINT 307 32. 26. -69. 2.400 3.000 JOINT 308 32. 26. -69. 2.400 3.000 JOINT 309 4. 0. -69. 1.200 JOINT 401 -24. -16. 6. -9.756 6.000 JOINT 402 -24. -16. 6. -9.756 6.000 JOINT 403 24. -16. 6. 7.800 -9.756 6.000 JOINT 404 24. -16. 6. 7.800 -9.756 6.000 JOINT 405 -24. 16. 6. 9.756 6.000 JOINT 406 -24. 16. 6. 9.756 6.000 JOINT 407 24. 16. 6. 7.800 9.756 6.000 JOINT 408 24. 16. 6. 7.800 9.756 6.000 JOINT 409 0. 0. 6. 3.900 6.000 JOINT 501 -24. -16. 10. -4.500 JOINT 503 24. -16. 10. 3.600 -4.500 JOINT 505 -24. 16. 10. 4.500 JOINT 507 24. 16. 10. 3.600 4.500 JOINT 601 -24. -16. 13. JOINT 603 24. -16. 13. JOINT 605 -24. 16. 13. JOINT 607 24. 16. 13. JOINT 701 -24. -16. 50. JOINT 703 24. -16. 50. JOINT 705 -24. 16. 50. JOINT 707 24. 16. 50. JOINT 709 -24. -26. 50. -2.964 JOINT 710 -8. -26. 50. -2.424 -2.964 JOINT 711 8. -26. 50. 2.424 -2.964 JOINT 712 24. -26. 50. -2.964 JOINT 714 -8. -16. 50. -2.424 JOINT 715 8. -16. 50. 2.424 JOINT 717 -8. 16. 50. -2.424 JOINT 718 8. 16. 50. 2.424 JOINT 720 -24. 26. 50. 2.964 JOINT 721 -8. 26. 50. -2.424 2.964 JOINT 722 8. 26. 50. 2.424 2.964 JOINT 723 24. 26. 50. 2.964 JOINT 801 -24. -16. 75.

JOINT 803 24. -16. 75. JOINT 805 -24. 16. 75. JOINT 807 24. 16. 75. JOINT 829 -24. -26. 75. -2.964 JOINT 830 -8. -26. 75. -2.424 -2.964 JOINT 831 8. -26. 75. 2.424 -2.964 JOINT 832 24. -26. 75. -2.964 JOINT 833 41. -26. 75. 0.132 -2.964 JOINT 834 -8. -16. 75. -2.424 JOINT 835 8. -16. 75. 2.424 JOINT 836 41. -16. 75. 0.132 JOINT 837 -8. 16. 75. -2.424 JOINT 838 8. 16. 75. 2.424 JOINT 839 41. 16. 75. 0.132 JOINT 840 -24. 26. 75. 2.964 JOINT 841 -8. 26. 75. -2.424 2.964 JOINT 842 8. 26. 75. 2.424 2.964 JOINT 843 24. 26. 75. 2.964 JOINT 844 41. 26. 75. 0.132 2.964 CDMCDM AP LOADLOADCN 31 CURRCURR 0.000 0.000 0.000 0.800 US NL FPS AWP CURR 30.000 2.410 0.000 CURR 60.000 2.650 0.000 CURR 80.000 2.770 0.000 CURR 100.000 2.860 0.000 CURR 120.000 2.950 0.000 CURR 150.000 3.030 0.000 CURR 180.000 3.110 0.000 CURR 200.000 3.150 0.000 CURR 261.000 3.280 0.000 WAVE WAVE0.95STOK 26.24261.00 9.10 0.00 D 0.00 5.00 72MS10 1 0 LOADCN 32 CURRCURR 0.000 0.000 45.000 0.850 US NL FPS AWP CURR 30.000 2.410 45.000 CURR 60.000 2.650 45.000 CURR 80.000 2.770 45.000 CURR 100.000 2.860 45.000 CURR 120.000 2.950 45.000 CURR 150.000 3.030 45.000 CURR 180.000 3.110 45.000

CURR 200.000 3.150 45.000 CURR 261.000 3.280 45.000 WAVE WAVE0.95STOK 26.24261.00 9.10 0.00 D 0.00 5.00 72MM10 1 0 LOADCN 33 CURRCURR 0.000 0.000 90.000 0.800 US NL FPS AWP CURR 30.000 2.410 90.000 CURR 60.000 2.650 90.000 CURR 80.000 2.770 90.000 CURR 100.000 2.860 90.000 CURR 120.000 2.950 90.000 CURR 150.000 3.030 90.000 CURR 180.000 3.110 90.000 CURR 200.000 3.150 90.000 CURR 261.000 3.280 90.000 WAVE WAVE0.95STOK 26.24261.00 9.10 90.00 D 0.00 5.00 72MS10 1 0 LOADCN 34 CURRCURR 0.000 0.000 135.000 0.850 US NL FPS AWP CURR 30.000 2.410 135.000 CURR 60.000 2.650 135.000 CURR 80.000 2.770 135.000 CURR 100.000 2.860 135.000 CURR 120.000 2.950 135.000 CURR 150.000 3.030 135.000 CURR 180.000 3.110 135.000 CURR 200.000 3.150 135.000 CURR 261.000 3.280 135.000 WAVE WAVE0.95STOK 26.24261.00 9.10 135.00 D 0.00 5.00 72MM10 1 0 LOADCN 35 CURRCURR 0.000 0.000 180.000 0.800 US NL FPS AWP CURR 30.000 2.410 180.000 CURR 60.000 2.650 180.000 CURR 80.000 2.770 180.000 CURR 100.000 2.860 180.000 CURR 120.000 2.950 180.000 CURR 150.000 3.030 180.000 CURR 180.000 3.110 180.000 CURR 200.000 3.150 180.000 CURR 261.000 3.280 180.000 WAVE WAVE0.95STOK 26.24261.00 9.10 180.00 D 0.00 5.00 72MS10 1 0

LOADCN 36 CURRCURR 0.000 0.000 225.000 0.850 US NL FPS AWP CURR 30.000 2.410 225.000 CURR 60.000 2.650 225.000 CURR 80.000 2.770 225.000 CURR 100.000 2.860 225.000 CURR 120.000 2.950 225.000 CURR 150.000 3.030 225.000 CURR 180.000 3.110 225.000 CURR 200.000 3.150 225.000 CURR 261.000 3.280 225.000 WAVE WAVE0.95STOK 26.24261.00 9.10 225.00 D 0.00 5.00 72MM10 1 0 LOADCN 37 CURRCURR 0.000 0.000 270.000 0.800 US CN FPS AWP CURR 30.000 2.410 270.000 CURR 60.000 2.650 270.000 CURR 80.000 2.770 270.000 CURR 100.000 2.860 270.000 CURR 120.000 2.950 270.000 CURR 150.000 3.030 270.000 CURR 180.000 3.110 270.000 CURR 200.000 3.150 270.000 CURR 261.000 3.280 270.000 WAVE WAVE0.95STOK 26.24261.00 9.10 270.00 D 0.00 5.00 72MS10 1 0 LOADCN 38 CURRCURR 0.000 0.000 315.000 0.850 US NL FPS AWP CURR 30.000 2.410 315.000 CURR 60.000 2.650 315.000 CURR 80.000 2.770 315.000 CURR 100.000 2.860 315.000 CURR 120.000 2.950 315.000 CURR 150.000 3.030 315.000 CURR 180.000 3.110 315.000 CURR 200.000 3.150 315.000 CURR 261.000 3.280 315.000 WAVE WAVE0.95STOK 26.24261.00 9.10 315.00 D 0.00 5.00 72MM10 1 0 LOADCNAREA***LDS1** 24.000 -26.247 50.000 24.000 26.247 50.000 -24.000 ***LDS2** -26.247 50.000 -24.000 26.247 50.000 -10.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES10PSFL

LOAD Z 701 705 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 703 707 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 705 720 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 707 723 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 709 701 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 710 714 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 711 715 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 712 703 -0.0790 -0.0790 GLOB UNIF 10PSFL LOAD Z 714 717 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 715 718 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 717 721 -0.1610 -0.1610 GLOB UNIF 10PSFL LOAD Z 718 722 -0.1610 -0.1610 GLOB UNIF 10PSFL ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.000 ***LDS2** -26.247 75.000 -24.000 26.247 75.000 -15.000 ***LDS3** 0 1 3 0 0AREA -2EQUPPRES15PSFU LOAD Z 801 805 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 803 807 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 805 840 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 807 843 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 829 801 -0.1180 -0.1180 GLOB UNIF 15PSFU LOAD Z 830 834 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 831 835 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 832 803 -0.2460 -0.2460 GLOB UNIF 15PSFU LOAD Z 833 836 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 834 837 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 835 838 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 836 839 -0.1280 -0.1280 GLOB UNIF 15PSFU LOAD Z 837 841 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 838 842 -0.2420 -0.2420 GLOB UNIF 15PSFU LOAD Z 839 844 -0.1280 -0.1280 GLOB UNIF 15PSFU LOADCNEQPT ***LDS1** 16.000 6.000 75.000 16.000 6.000 75.000 ***LDS2** -250.000 20.000 10.000 ***LDS3** 10.000 1 2 2 0 0EQPT -1EQUPSKIDSKID1 X LOAD Z 835 838 17.4040-65.579 GLOB CONC SKID1 LOAD Z 835 838 27.4040-65.579 GLOB CONC SKID1 LOAD Z 803 807 17.4040-59.421 GLOB CONC SKID1 LOAD Z 803 807 27.4040-59.421 GLOB CONC SKID1 ***LDS1** -16.000 -16.000 75.000 -16.000 -16.000 75.000 ***LDS2** -150.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID2 X LOAD Z 829 801 6.49700-35.653 GLOB CONC SKID2 LOAD Z 830 834 6.49700-39.347 GLOB CONC SKID2 LOAD Z 834 837 4.15400-39.347 GLOB CONC SKID2 LOAD Z 801 805 4.15400-35.653 GLOB CONC SKID2 ***LDS1** -16.000 50.000 -16.000 50.000

***LDS2** -100.000 20.000 7.500 ***LDS3** 7.500 1 2 2 0 0EQPT -1EQUPSKIDSKID3 X LOAD Z 701 705 12.6540-23.769 GLOB CONC SKID3 LOAD Z 701 705 20.1540-23.769 GLOB CONC SKID3 LOAD Z 714 717 12.6540-26.231 GLOB CONC SKID3 LOAD Z 714 717 20.1540-26.231 GLOB CONC SKID3 ***LDS1** 32.000 19.000 75.000 32.000 19.000 75.000 ***LDS2** -35.000 20.000 7.000 ***LDS3** 7.000 1 2 3 0 0EQPT -1EQUPSKIDSKID4 X LOAD Z 803 807 31.5000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 3.00000-6.1800 GLOB CONC SKID4 LOAD Z 807 843 6.50000-6.1800 GLOB CONC SKID4 LOAD Z 836 839 31.5000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 3.00000-5.4866 GLOB CONC SKID4 LOAD Z 839 844 6.50000-5.4866 GLOB CONC SKID4 LOADCNLIVE ***LDS1** 41.011 -26.247 75.000 41.011 26.247 75.000 -24.606 ***LDS2** 26.247 75.000 -24.606 -26.247 75.000 -100.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES100PSFU Z LOAD Z 829 801 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 830 834 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 831 835 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 832 803 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 833 836 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 834 837 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 835 838 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 836 839 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 837 841 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 838 842 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 839 844 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 801 805 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 803 807 -1.6400 -1.6400 GLOB UNIF 100PSFU LOAD Z 805 840 -0.8200 -0.8200 GLOB UNIF 100PSFU LOAD Z 807 843 -1.6400 -1.6400 GLOB UNIF 100PSFU ***LDS1** 24.606 -26.247 50.000 24.606 26.247 50.000 -24.606 ***LDS2** 26.247 50.000 -24.606 -26.247 50.000 -50.000 ***LDS3** 0 1 3 0 0LIVE -2EQUPPRES50PSFL Z LOAD Z 701 705 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 703 707 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 705 720 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 707 723 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 709 701 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 710 714 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 711 715 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 712 703 -0.4100 -0.4100 GLOB UNIF 50PSFL LOAD Z 714 717 -0.8200 -0.8200 GLOB UNIF 50PSFL

LOAD Z 715 718 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 717 721 -0.8200 -0.8200 GLOB UNIF 50PSFL LOAD Z 718 722 -0.8200 -0.8200 GLOB UNIF 50PSFL LOADCNMISC LOAD Z 712 703 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 703 707 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 707 723 -0.1900 -0.1900 GLOB UNIF WALK1 LOAD Z 833 836 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 836 839 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD Z 839 844 -0.1900 -0.1900 GLOB UNIF WALK2 LOAD 807 -20.000 GLOB JOIN CRANE ***LDS1** -8.000 20.000 50.000 -8.000 20.000 50.000 ***LDS2** -10.000 34.000 0.100 ***LDS3** 0.100 1 2 2 0 0MISC -1EQUPSKIDFIREWALLX LOAD Z 705 720 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 705 720 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 717 721 4.05000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 3.95000-1.6667 GLOB CONC FIREWALL LOAD Z 718 722 4.05000-1.6667 GLOB CONC FIREWALL LCOMBLCOMB 1001 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 31 1.000 LCOMB 1002 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 32 1.000 LCOMB 1003 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 33 1.000 LCOMB 1004 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 34 1.000 LCOMB 1005 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 35 1.000 LCOMB 1006 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 36 1.000 LCOMB 1007 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 37 1.000 LCOMB 1008 AREA 1.000EQPT 1.000LIVE 1.000MISC 1.000 38 1.000 END

LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1

LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYSIS INPUT DATA 50 YEARS OPTIONS EN SDUCJT 2 1 C PT PTPT PTPT LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1

LDOPT NF+Z 64.200 490.00 -261.00 261.00 HYD MPT * PhD RELIABILITY ANALYIS INPUT DATA 100 YEAR LCSEL ST 1001 1002 1003 1004 1005 1006 1007 1008 OPTIONS EN SDUCJT 2 1 PTPTPTPTPTPTPTPTPTPT PT SECTSECT CONE CON 36.000.750 26.00 GRUPGRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 5. GRUP LG1 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG1 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.005. GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.15 GRUP LG2 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG2 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.90 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.006.75 GRUP LG3 41.250 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG3 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.004.35 GRUP LG4 42.000 1.375 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG5 36.000 1.000 29.0011.6050.00 1 1.001.00 0.50F490.00 GRUP LG6 36.000 0.750 29.0011.0036.00 1 1.001.00 0.50F490.003.25 GRUP LG6 CONE 29.0011.6036.00 1 1.001.00 0.50F490.004.95 GRUP LG6 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP LG7 26.000 0.750 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL1 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL2 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL3 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP PL4 36.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP T01 16.000 0.625 29.0111.2035.00 1 1.001.00 0.50N490.00 GRUP T02 20.000 0.750 29.0011.6035.00 1 1.001.00 0.50N490.00 GRUP T03 12.750 0.500 29.0111.6035.00 1 1.001.00 0.50N490.00 GRUP T04 24.000 0.750 29.0011.6036.00 1 1.001.00 0.50N490.00 GRUP T05 26.000 1.000 29.0011.6036.00 1 1.001.00 0.50F490.00 GRUP W.B 36.433 1.000 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W01 W24X162 29.0111.2035.97 1 1.001.00 0.50 489.99 GRUP W02 W24X131 29.0111.2035.97 1 1.001.00 0.50 489.99 MEMBERMEMBER1 101 102 W.BSK 000000100111 MEMBER OFFSETS 0.74 5.95 MEMBER1 103 104 W.BSK 000000100111 MEMBER OFFSETS -0.59 0.74 5.92 MEMBER1 105 106 W.BSK 000000100111 MEMBER OFFSETS -0.74 5.95 MEMBER1 107 108 W.BSK 000000100111 MEMBER OFFSETS -0.59 -0.74 5.92 MEMBER 101 201 LG1 MEMBER 103 203 LG1 MEMBER 105 205 LG1