analysis of applied modifications to a cone penetration

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Analysis of Applied Modifications to a Cone Penetration Test-Analysis of Applied Modifications to a Cone Penetration Test-

based Lateral Spread Displacement Prediction Model based Lateral Spread Displacement Prediction Model

Alexander Edward Corob Brigham Young University

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Analysis of Applied Modifications to a Cone Penetration Test-

Based Lateral Spread Displacement Prediction Model

Equation Chapter 2 Section 1

Alexander Edward Corob

A thesis submitted to the faculty of Brigham Young University

in partial fulfillment of the requirements for the degree of

Master of Science

Kevin W. Franke, Chair Kyle M. Rollins E. James Nelson

Department of Civil and Environmental Engineering

Brigham Young University

Copyright © 2019 Alexander Edward Corob

All Rights Reserved

ABSTRACT

Analysis of Applied Modifications to a Cone Penetration Test-Based Lateral Spread Displacement Prediction Model

Alexander Edward Corob Department of Civil and Environmental Engineering, BYU

Master of Science

This study set out to examine the effectiveness and reliability of six modifications to the Zhang et al. (2004) CPT-based lateral spread model. A regression analysis, distribution charts, and a discriminant analysis are performed to determine how effective the modifications are on the model. From the comparisons and statistical analysis performed in this study, application of these modifications reduces over-predictions from strain-based prediction methods. Unfortunately, the tendency to under-predict displacements on average is also increased.

Keywords: modifications, lateral spread, liquefaction, CPT

ACKNOWLEDGEMENTS

I would first like to thank my advisor chair Dr. Kevin Franke for his continued support,

guidance, advice, and patience. His enthusiasm helped spark my interest and fan my passion in

the field of geotechnical engineering. He provided invaluable direction and feedback throughout

my education at Brigham Young University. Dr. Franke maintained the end goal in sight and saw

me past difficulties encountered with this project. I have Dr. Franke to thank for making this

master’s degree possible which has opened many future possibilities for which I will be forever

grateful.

I would also like to thank the other professionals involved in this thesis. Drs. Youd and

Robertson have set the path and the standard in this industry and I humbly stand on their

shoulders to continue the advancement of geotechnical engineering. Drs. Rollins and Nelson

provided thoughtful correction and advice where it was most needed.

I must also acknowledge my parents, Greg and Lori Corob, for their unwavering

confidence, persistent cheerleading, and endearing perspective. With them I have been able to set

my goals higher than ever I could’ve realized without them. I must thank my older brother Brad

for his patient technical assistance.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ..................................................................................................................... viii

1 Introduction ............................................................................................................................. 1

Problem ............................................................................................................................ 1

Proposed Solution ............................................................................................................ 1

Research Objectives ......................................................................................................... 2

2 Review of Liquefaction ........................................................................................................... 3

General Overview ............................................................................................................ 3

Design Consideration of Liquefaction ............................................................................. 4

2.2.1 Liquefaction Susceptibility ....................................................................................... 4

2.2.1.1 Historical Criteria .................................................................................................. 5

2.2.1.2 Geologic Criteria ................................................................................................... 5

2.2.1.3 Compositional Criteria .......................................................................................... 6

2.2.1.4 State Criteria .......................................................................................................... 7

2.2.2 Initiation .................................................................................................................. 10

2.2.2.1 Flow Liquefaction Surface .................................................................................. 11

2.2.2.2 Cyclic Mobility ................................................................................................... 15

2.2.2.3 Triggering Procedure ........................................................................................... 17

2.2.3 Effects ..................................................................................................................... 23

2.2.3.1 Lateral Spread ..................................................................................................... 23

2.2.3.2 Settlement ............................................................................................................ 23

2.2.3.3 Loss of Bearing Capacity .................................................................................... 24

2.2.3.4 Increased Lateral Pressure on Walls ................................................................... 24

2.2.3.5 Alteration of Ground Motions ............................................................................. 25

2.2.3.6 Flow Failures ....................................................................................................... 26

3 Review of Lateral Spread Displacement ............................................................................... 27

Lateral Spread Overview ................................................................................................ 27

Laboratory Tests ............................................................................................................. 29

Lateral Spread Models ................................................................................................... 31

3.3.1 Analytical ................................................................................................................ 31

v

3.3.1.1 Numerical Models ............................................................................................... 31

3.3.1.2 Elastic Beam Model ............................................................................................ 32

3.3.1.3 Newark Sliding Block Analysis .......................................................................... 33

3.3.2 Empirical Methods .................................................................................................. 33

Zhang et al., 2004 ........................................................................................................... 37

3.4.1 Zhang et al., 2004 CPT Model ................................................................................ 38

3.4.2 Zhang et al., 2004 Model Deficiencies ................................................................... 41

4 Proposed Modifications to CPT-Based Lateral Spread Prediction Procedure ...................... 43

Selected Model ............................................................................................................... 43

Modifications ................................................................................................................. 44

4.2.1 Soil Transition Zone Modification .......................................................................... 45

4.2.2 Thin Sand Layer Modification ................................................................................ 47

4.2.3 Dilative/Contractive Behavior Modification .......................................................... 49

4.2.4 Soil Depth Modification ......................................................................................... 49

4.2.5 Fines Content Modification .................................................................................... 51

4.2.6 Eliminate Thin Sand Modification .......................................................................... 51

Case Histories ................................................................................................................. 52

4.3.1 Christchurch ............................................................................................................ 53

4.3.2 Turkey ..................................................................................................................... 54

4.3.3 Taiwan..................................................................................................................... 55

4.3.4 Northridge ............................................................................................................... 55

4.3.5 Imperial Valley ....................................................................................................... 57

4.3.6 San Fernando .......................................................................................................... 58

4.3.7 Loma Prieta ............................................................................................................. 59

Example Problem ........................................................................................................... 59

4.4.1 No Modifications .................................................................................................... 61

4.4.2 Soil Transition Zone Modification .......................................................................... 61

4.4.3 Thin Sand Layer Modification ................................................................................ 64

4.4.4 Dilative/Contractive Modification .......................................................................... 64

4.4.5 Soil Depth Modification ......................................................................................... 64

4.4.6 Fines Content Modification .................................................................................... 65

4.4.7 Eliminate Thin Sand Modification .......................................................................... 65

4.4.8 All Modifications .................................................................................................... 65

vi

5 Evaluation of Modifications to CPT-Based Lateral spread Prediction Procedure ................ 67

Results ............................................................................................................................ 67

5.1.1 Regression Analysis ................................................................................................ 68

5.1.2 Distribution Charts .................................................................................................. 83

5.1.3 Discriminant Analysis ............................................................................................. 87

5.1.4 Results summary ..................................................................................................... 91

6 Conclusions ........................................................................................................................... 93

Conclusions .................................................................................................................... 93

References ..................................................................................................................................... 94

Appendix A Site Data and Lateral Spread Displacements .................................................... 102

Appendix B Sounding Logs................................................................................................... 112

vii

LIST OF TABLES

Table 5-1: Count of Soundings by Modification and Prediction Accuracy .................................. 79

Table 5-2: Change in Count of Soundings by Modification and Prediction Accuracy ................ 79

Table 5-3: Count of Data in Each Final Accuracy Category Based on Starting (Unmodified) Accuracy Category ..................................................................................... 80

Table A-1: Site Input Data by Sounding..................................................................................... 103

Table A-2: Measured and Predicted Lateral Spread Displacements by Modifications and Sounding ......................................................................................................................... 107

viii

LIST OF FIGURES

Figure 2-1: Earthquake Damage to Marina District of San Francisco, 1989 (Page et al., 1999) ....................................................................................................................................... 5

Figure 2-2: Loading behavior of loose and dense soils subject to undrained and drained conditions (after Kramer, 1996). ............................................................................................. 8

Figure 2-3: CVR line as a defined boundary for liquefaction susceptibility (after Kramer, 1996). ...................................................................................................................................... 9

Figure 2-4: Steady-state line represented in three relevant axes of e, σ, and τ (after Kramer, 1996). ........................................................................................................................ 9

Figure 2-5: State criteria for flow liquefaction susceptibility (after Kramer, 1996). .................... 10

Figure 2-6: Response of isotropically consolidated specimen of loose, saturated sand: (a) stress-strain curve; (b) effective stress path; (c) excess pore pressure; (d) effective confining pressure (Kramer, 1996). ...................................................................................... 11

Figure 2-7: Response of five specimens isotropically consolidated to the same initial void ratio at different initial effective confining pressures (Kramer, 1996). ................................ 12

Figure 2-8: Orientation of the flow liquefaction surface in stress path space (Kramer, 1996). .................................................................................................................................... 13

Figure 2-9: Initiation of flow liquefaction by cyclic and monotonic loading (Kramer, 1996). .................................................................................................................................... 13

Figure 2-10: Zone of susceptibility to flow liquefaction (Kramer, 1996). ................................... 14

Figure 2-11: Zone of Susceptibility to cyclic mobility (Kramer, 1996). ...................................... 15

Figure 2-12: Three cases of cyclic mobility (after Kramer 1996). ............................................... 16

Figure 2-13: Summary of the Robertson and Wride method (after Robertson, 2009) CRR procedure. .............................................................................................................................. 20

Figure 2-14: Normalized soil behavior type chart (after Robertson & Wride, 1998). Soil types: 1 sensitive, fine grained; 2 peats; 3 silty clay to clay; 4 clayey silt to silty clay; 5 silty sand to sandy silt; 6 clean sand to silty sand; 7 gravelly sand to dense sand; 8 very stiff sand to clayey sand; 9 very stiff, fine grained. ...................................................... 21

Figure 2-15: Lateral spread-induced fissures after the 1999 Chi-Chi earthquake (after Chu et al., 2008). .......................................................................................................................... 24

Figure 2-16: Tilted apartment buildings caused by liquefaction and loss of bearing strength (USGS, 2006). ......................................................................................................... 25

Figure 3-1: Lateral Spread Visualization, after Rauch (1997) ...................................................... 27

Figure 3-2: Damaged San Francisco City Hall, 1906 (after Niekerman, 2018) ........................... 28

https://d.docs.live.net/e9ee83b2ec728e29/Research/16%20Dec%202019/Corob%20Dec%2016%202019%20Thesis%20-%20Electronic.docx#_Toc27414129

https://d.docs.live.net/e9ee83b2ec728e29/Research/16%20Dec%202019/Corob%20Dec%2016%202019%20Thesis%20-%20Electronic.docx#_Toc27414129

ix

Figure 3-3: Allowable bounds of inputs, after Youd et al., (2002). .............................................. 37

Figure 3-4: Relationship between cyclic shear strain and factor of safety at varying relative densities (after Zhang et al., 2004). .......................................................................... 38

Figure 4-1: Cone Tip Resistance influenced by the zone of influence ahead of the cone. ........... 45

Figure 4-2: Cone tip resistance transitioning between contrasting stiff/soft layers (after Ahmandi and Robertson, 2005). ........................................................................................... 46

Figure 4-3: Modifying thin sand layer cone tip resistance (after Ahmandi & Robertson, 2005). .................................................................................................................................... 48

Figure 4-4: Liquefaction potential of four different Northridge study sites (a)-(d). ..................... 56

Figure 4-5: Example problem soil profile, WCC-11, after Chu et al., (2004). ............................. 60

Figure 4-6: Predicted lateral spread displacements by modifications. ......................................... 62

Figure 4-7: Predicted displacement with all individual and total modifications. ......................... 66

Figure 5-1: No Modifications, Free Face ...................................................................................... 69

Figure 5-2: Transition Zone Modification, Free Face .................................................................. 69

Figure 5-3: Depth Modification, Free Face .................................................................................. 70

Figure 5-4: Thin Sand Modification, Free Face ............................................................................ 70

Figure 5-5: Dilative/Contractive Modification, Free Face ........................................................... 71

Figure 5-6: Fines Content Modification, Free Face ...................................................................... 71

Figure 5-7: Eliminate Thin Sand Modification, Free Face ........................................................... 72

Figure 5-8: All Modifications, Free Face ..................................................................................... 72

Figure 5-9: No Modifications, Sloping Ground ............................................................................ 73

Figure 5-10: Transition Zone Modification, Sloping Ground ...................................................... 73

Figure 5-11: Depth Modification, Sloping Ground ...................................................................... 74

Figure 5-12: Thin Sand Modification, Sloping Ground................................................................ 74

Figure 5-13: Dilative/Contractive Modification, Sloping Ground ............................................... 75

Figure 5-14: Fines Content Modification, Sloping Ground .......................................................... 75

Figure 5-15: Eliminate Thin Sand Modification, Sloping Ground ............................................... 76

Figure 5-16: All Modifications, Sloping Ground ......................................................................... 76

Figure 5-17: Distribution Plots, Free-Face (W) ............................................................................ 85

Figure 5-18: Distribution Plots, Sloping Ground (S) .................................................................... 86

Figure 5-19: Discriminant Analysis, Free Face ............................................................................ 89

Figure 5-20: Discriminant Analysis, Sloping Ground .................................................................. 90

x

Figure B-1: Christchurch Avon River 11033.............................................................................. 113

Figure B-2: Christchurch Avon River 1108 ................................................................................ 114



Figure B-5: Christchurch Avon River 1420................................................................................ 117





Figure B-10: Christchurch Avon River 15600 ............................................................................ 122











Figure B-21: Christchurch Avon River 2153 .............................................................................. 133











xi









Figure B-40: Christchurch Avon River 64 .................................................................................. 152

Figure B-41: Christchurch Avon River 71 .................................................................................. 153


Figure B-43: Imperial Valley Heber 1 ........................................................................................ 155




Figure B-47: Imperial Valley Heber 442 .................................................................................... 159




Figure B-51: Imperial Valley Heber 700-lsu006 ........................................................................ 163


Figure B-53: Imperial Valley Riverpark pqs1 ............................................................................ 165





Figure B-58: Loma Prieta Moss Landing UC-18 ....................................................................... 170

Figure B-59: Loma Prieta Moss Landing UC-2 ......................................................................... 171




xii


Figure B-64: Northridge Balboa 1 .............................................................................................. 176

Figure B-65: Northridge Balboa 10 ............................................................................................ 177




Figure B-69: Northridge Balboa 13.5 ......................................................................................... 181











Figure B-80: Northridge Malden 11 ........................................................................................... 192



Figure B-83: Northridge Malden 3 ............................................................................................. 195



Figure B-86: Northridge Potrero 1 .............................................................................................. 198

Figure B-87: Northridge Potrero 10 ............................................................................................ 199







xiii




Figure B-97: Northridge Wynne 1 .............................................................................................. 209

Figure B-98: Northridge Wynne 10 ............................................................................................ 210


Figure B-100: Northridge Wynne 12 .......................................................................................... 212









Figure B-109: San Fernando Juvenile Hall sfvjh81-2 ................................................................ 221



Figure B-112: San Fernando Juvenile Hall sfvjh9 ...................................................................... 224

Figure B-113: Taiwan MAA C9 ................................................................................................. 225

Figure B-114: Taiwan NCC 2 ..................................................................................................... 226

Figure B-115: Taiwan NCC 3 ..................................................................................................... 227

Figure B-116: Taiwan RESI C7 .................................................................................................. 228

Figure B-117: Taiwan WBC 1 .................................................................................................... 229

Figure B-118: Taiwan WBC 4 .................................................................................................... 230

Figure B-119: Taiwan WCC 1 .................................................................................................... 231

Figure B-120: Taiwan WCC 11 .................................................................................................. 232





xiv





Figure B-129: Turkey Cark 24 .................................................................................................... 241

Figure B-130: Turkey Cark 25 .................................................................................................... 242

Figure B-131: Turkey Cumhuriyet 22 ........................................................................................ 243



Figure B-134: Turkey Degirmendere dn1 ................................................................................... 246



1

1 INTRODUCTION

Problem

Practicing engineers rely on current methods of lateral spread displacement model

predictions to design for roads, foundations, lifelines and more. One problem facing these current

methods is sometimes unreasonable results of CPT-based methods and practitioners don’t know

how much to trust them. Relying on their training, engineers tend to design based on a

conservative approach, which can lead to potentially calculating unlikely predicted

displacements and costing excessive amounts in over-design. Far beyond random, epistemic

results, this method produces inconsistent predictions, and it is the goal of this paper to bring

them more in line with observed in-field displacements.

Proposed Solution

It has been observed by engineers that CPT-based models tend to compute higher

displacements than SPT-based and in an attempt to remediate this disparity, they have turned to

the researchers who helped develop these methods. Drs. Youd and Robertson have provided

unofficial, in-person, modifications which have never been tested or validated. It is the goal of

this project to apply these modifications against a rigorous set of case histories to provide clear

and informative solutions for the broader geotechnical engineering community to predict lateral

2

spread displacements. This paper will explore how well the modifications improve prediction

accuracy where the methodology both performs adequately and doesn’t perform adequately.

Research Objectives

The research objective of this paper is to determine the effectiveness of applying and

analyzing several realistic, proposed modifications to existing CPT-based lateral spread

displacement prediction methods. Achieving this research objective requires collecting relevant

case histories into a comprehensible digital database, analyzing the CPT case histories with

current methods to establish a baseline, methodically applying the six proposed modifications,

analyzing prediction accuracy with the modifications applied, and providing reasonable

recommendations for future design applications. This paper will compare displacement

predictions before and after modifications are applied to the measured displacement. With the

displacement predictions, this paper will analyze how well the applications of these

modifications perform. Specifically, this paper will address two main outcomes: 1) Does the

consistent application of these modifications to non-typical soil profiles (thin/transition/clayey)

consistently reduce predicted displacements and consistently improve accuracy? 2) Does

implementation of modifications to cases of soils where existing methodology perform adequate

predictions significantly decrease predicted displacements resulting in potentially dangerous

under prediction?

3

2 REVIEW OF LIQUEFACTION

General Overview

Liquefaction is the phenomenon that describes the weakening or loss of strength in

saturated or partially-saturated soils due to loading events, such as earthquakes. The 1964

earthquakes of Niigita and Alaska brought to light the extensive amount of damage that can be

caused by the liquefying of soil (Hamada, et al., 1986; Ross, Seed, & Migliaccio, 1969). These

calamities sparked a major effort to understand liquefaction, which continues today. Soil strength

is derived from interparticle contact. When an earthquake is introduced to the soil, loading pulses

increase the pore water pressure and subsequently reduce inner granular soil contact shear

strength. When the water cannot drain quick enough, excess amounts of pore water pressure are

developed, the soil-on-soil contact is replaced by soil-on-water contact, and particles will begin

to slip past one another, or “hydroplane”. Major negative effects can include lateral spread or

settlement. The driving force behind liquefaction is the push/pull from the earthquake, causing

excess pore water pressure to build up faster than it can dissipate (Kramer, 1996).

Flow liquefaction and cyclic mobility are two key types of liquefaction that describe the

process by which liquefaction is initiated. They will be described here briefly and later in this

chapter under the context of initiation. Flow liquefaction occurs less frequently but can lead to

profound damage. Flow liquefaction occurs when static shear stress is greater than liquefied

shear stress (Kramer, 1996). The liquefied shear stress state can be brought about by cyclic

4

stresses. Cyclic mobility is more frequent and has varied intensity of damage. This mode occurs

when static shear stress is less than liquefied shear strength. Driven by both cyclic and static

shear stresses, cyclic mobility also exhibits incremental deformations (Kramer, 1996). Since the

1964 earthquakes, understanding of and designing against liquefaction has evolved

tremendously.

Design Consideration of Liquefaction

In locations with faults and seismic activity, structures and lifelines need to be resilient to

inevitable earthquake loading. In addition to stability of structures, the soil on which the

structures reside must also be resilient. Liquefied soil, slope failure, and loss of strength can lead

to catastrophic damage. For example, the 1989 Loma Prieta earthquake caused building collapse

and sidewalk buckling due to ground motions and liquefied soil, as seen in Figure 2-1.

Considerations for liquefaction design include proximity to fault, fault activity, soil conditions,

weight of the structure, and the importance of the structure in event of an earthquake-caused

damage. To methodically evaluate soil hazard potential, one must examine susceptibility,

initiation, and effects of liquefaction.

2.2.1 Liquefaction Susceptibility

Not every site or soil layer will liquefy. Liquefaction susceptibility considers the soil

properties that might make it prone to liquefaction. If it’s not susceptible, one can halt the

evaluation and conclude that there is no potential liquefaction hazard. If a site is deemed

susceptible, it must further be considered for initiation and effects. To determine susceptibility,

one can look at four different criteria: historical, geologic, compositional, and state criteria.

5

Figure 2-1: Earthquake Damage to Marina District of San Francisco, 1989 (Page et al., 1999)

2.2.1.1 Historical Criteria

Previous examples of liquefaction help to direct researchers to understand conditions that

contribute to liquefaction. Post-earthquake field investigations provide valuable insight to

liquefaction behavior. There is a continuing added benefit of more available recorded data to

refine liquefaction susceptibility as earthquakes occur and their data published. One key study

(Youd, 1984) showed repeated liquefaction at the same location with unchanged soil and

groundwater conditions. Case histories help distinguish specific sites or general conditions that

may contribute to liquefaction susceptibility in future events.

2.2.1.2 Geologic Criteria

A major factor of liquefaction susceptibility is how the soil formed. Age of soil deposition,

depositional environment, and hydrological environment all factor into liquefaction susceptibility

(Youd & Hoose, 1977). Loose deposit states and soils with uniform grain size distribution tend

to have high liquefaction susceptibility. Fluvial, colluvial, and aeolian deposit mechanisms typify

6

these characteristics and lend to being susceptible to liquefaction, when saturated. Older soil

deposits tend to be less susceptible than newer ones. For example, Holocene age soils are

typically more vulnerable than Pleistocene age soils. Man-made deposits carry potential for

liquefaction susceptibility. Loosely filled deposits, such as hydraulic fill dams or poor

compaction sites, present a risk of liquefaction susceptibility.

One condition for susceptibility is saturated or partially-saturated soils. Saturation is

determined by the height of the water table. Partially saturated soils include how far up from the

water table soils retain water. The water is essential for liquefaction because without it, pore

water pressures cannot build and the soil will not liquefy.

2.2.1.3 Compositional Criteria

Particle size, gradation, and shape all factor into how much a soil is susceptible to

liquefaction. Fine soils are typically not susceptible to liquefaction because of the

interconnectedness of the pore water pressure, as well as the amount of pore water pressure

required to break the chemical and electrical attraction between the fine particles. The exception

for liquefaction-susceptible fine-grained soils is when they lack the properties to prevent

liquefaction: coarse fines with little to no plasticity and low cohesion (Ishihara, 1984; 1985).

Dense soils tend to have greater resistance to liquefaction, requiring more or longer forces to

adequately disturb the soil towards liquefaction. Well-graded soils tend to be less liquefaction

susceptible because of the smaller particles that fill in the voids. Soils well-graded prevents

volume change, and consequently liquefaction susceptibility, more than poorly-graded soils.

Particles that are rounded tend to densify easier than angular soils. Rounded particles can’t be

7

locked by friction and are more likely to slip past each other. Due to this, rounded particles are

likely more susceptible to liquefaction.

2.2.1.4 State Criteria

Despite being regarded as susceptible by all the previous indicators, it is still possible that

a soil is not wholly susceptible to liquefaction. The stress and density of a soil, or its state, plays

an important role in determining liquefaction susceptibility. The ability of a soil to generate

excess pore pressures is heavily influenced by the initial confining pressure and the initial

density. The initial state of a soil determines whether a soil will dilate or contract under cyclic

loading, a factor in liquefaction susceptibility. It is worth noting that the state criteria for

liquefaction susceptibility differs for flow liquefaction and cyclic mobility. To describe the

relevance of the state of a soil, two key concepts will be introduced, critical void ratio and steady

state of deformation.

In experiments performed by Casagrande (1936) on drained sand triaxial tests, it was

observed that the same effective confining pressure would derive the same void ratio at large

strains regardless of whether the sample was initially loosely or densely compacted. Initially

loose samples would contract or densify. Initially dense samples would have a short period of

contracting before dilating. The same density that samples approached at large strains correlates

to a given critical void ratio. Utilizing different confining pressures, Casagrande realized and

named the relationship between that critical void ratio and the effective confining pressure as the

critical void ratio (CVR) line. The CVR establishes a boundary between loose soils that tend to

contract and dense soils that tend to dilate, as seen in Figure 2-2.

8

Figure 2-2: Loading behavior of loose and dense soils subject to undrained and drained conditions (after Kramer, 1996).

Casagrande postulated that undrained samples, without a change in volume or void ratio,

would develop positive excess pore pressures in loose soil and negative excess pore pressures in

dense soil, until they reached the CVR line. This led to a conclusion that the CVR line acted as a

boundary between samples that would be susceptible to flow liquefaction or not. Soils that fell

above the CVR line, due to higher initial void ratios, would be considered susceptible to flow

liquefaction. Soils with initial void ratios that plotted below the CVR line were considered not

susceptible to flow liquefaction, as seen in Figure 2-2. This reasoning, however, proved

incomplete after the failure of the Fort Peck Dam (Middlebrooks, 1942). The initial state of the

soil was later shown to plot as nonsusceptible but experienced a static flow liquefaction failure.

Casagrande thought this discrepancy was tied to the inability of strain-controlled drained tests to

perfectly replicate all relevant factors relating to flow liquefaction failure. This example showed

a need for a refined liquefaction state criteria.

Castro (1969) performed several key tests that increased understanding of liquefaction and

the behavior of steady state conditions. These stress-controlled triaxial tests, now with the option

of undrained loading, were able to simulate flow liquefaction and a property called steady state

deformation. Steady state of deformation refers to the condition of a constant volume, constant

9

velocity, constant effective confining pressure and a constant shear stress in which the soil flows

continuously (Castro & Poulos, 1977; Poulos, 1981). To visually describe the steady state of

deformation with its relationship to void ratio and effective confining pressure, a steady-state line

(SSL) is formed in e - σ’- τ space, as seen in Figure 2-4. This graphic allows users to understand

the trend of the SSL with a constant effective confining pressure or a constant density, as seen

with the projections on to the relevant planes.

Figure 2-4: Steady-state line represented in three relevant axes of e, σ, and τ (after Kramer, 1996).

Due to the shearing resistance of soil being directly proportional to the effective confining

pressure, the SSL can be further described in terms of steady-state strength, Ssu. This strength-

Figure 2-3: CVR line as a defined boundary for liquefaction susceptibility (after Kramer, 1996).

10

based line plots parallel to and slightly lower than the CVR line. Under these parameters, flow

liquefaction susceptibility can be defined by the SSL, per Figure 2-5. Soils with an initial state

below the SSL are not susceptible to flow liquefaction. Ones that plot above the SSL are

considered susceptible only if the static shears stress is greater than the steady state strength.

Figure 2-5: State criteria for flow liquefaction susceptibility (after Kramer, 1996).

This metric for susceptibility evaluation is only valid for flow liquefaction. Cyclic mobility

can occur in soils with initial states above or below the SSL, i.e. both loose and dense soils. If

soils are considered susceptible, then one must evaluate if the liquefaction will be initiated.

2.2.2 Initiation

Where a soil might be susceptible to liquefaction, this alone is not a strong enough

indicator that a given earthquake will initiate liquefaction. After considering liquefaction

susceptibility, one must next consider initiation, or triggering, of liquefaction. This section will

detail some methods for how to account for liquefaction initiation. Where susceptibility includes

characteristics of geologic setting, soil structure and composition, and its initial state, initiation

considers the loading mechanisms and how the soil will respond when acted upon by outside

sources. How a soil responds to specific loading mechanisms greatly depends on many of the

11

above listed soil properties and even the type of mechanism itself. Since flow liquefaction and

cyclic mobility relate to different driving mechanisms, they will be considered separately.

2.2.2.1 Flow Liquefaction Surface

A good starting point for describing the flow liquefaction surface is to examine a clean,

saturated, loose sand, isotropically consolidated in an undrained triaxial test with monotonic

loading. Figure 2-6 demonstrates the stress path of monotonic loading on these soil conditions.

Hanzawa et al. (1979) first demonstrated the utility of the stress path space to simply present

stress conditions of strain-softening. Point A describes the initial conditions of no shear stress

and a given effective confining pressure in drained equilibrium. The sample initially resists

straining as it compacts until a maximum shear strength is reached, point B, followed by a rapid

decrease in shear strength and large increases in strain and excess pore water pressure, point C.

Point C is also the state at which the soil reached the SSL. Flow liquefaction was reached at

point B and the soil became irreversibly unstable.

Figure 2-6: Response of isotropically consolidated specimen of loose, saturated sand: (a) stress-strain curve; (b) effective stress path; (c) excess pore pressure; (d) effective confining pressure (Kramer, 1996).

12

An evaluation of samples with the same void ratio, but different effective confining

pressures will now be considered. Equal void ratios means all these samples will eventually

reach the same point on the steady state line (SSL), but following different paths. Figure 2-7

demonstrates these differences. Sample A and B don’t reach flow liquefaction because they are

initially plotted below the SSL, merely dilating toward and settling upon the same steady state

point. Samples C, D, and E contract and experience flow liquefaction. They reach their

respective peak undrained shear strengths before a sharp decline in shear strength and eventual

settlement at the steady-state point.

Figure 2-7: Response of five specimens isotropically consolidated to the same initial void ratio at different initial effective confining pressures (Kramer, 1996).

Researchers found that by tracing initiation points, seen with the dashed line in Figure 2-7,

a boundary could be defined (Hanzawa et al., 1979). This line is curtailed by a horizontal line

extending from the steady state point to initiation points boundary. The horizontal line represents

the inability of flow liquefaction to occur if the steady state path is below the steady-state point.

13

These two bounds create the flow liquefaction surface (FLS) (Vaid et al., 1990). Figure 2-8

shows this defined boundary in p-q space.

Figure 2-8: Orientation of the flow liquefaction surface in stress path space (Kramer, 1996).

With the FLS defined, it is now easier to detail different loading mechanisms, including

cyclic loading. Regardless of the loading type, monotonic or cyclic loading, two identical

samples will initiate flow liquefaction upon reaching the FLS. Figure 2-9 illustrates this pattern.

Figure 2-9: Initiation of flow liquefaction by cyclic and monotonic loading (Kramer, 1996).

14

The monotonically loaded sample will display behavior as described earlier, following path

ABC. A cyclically loaded sample will follow a slightly different path, ADC. Positive excess pore

water pressure increases with each cycle and strains accumulate with each loading until the

sample reaches the FLS, point D. Once it gets to this point, the sample experiences a sharp

decline in shear stress until eventually settling at the steady state point. Despite having different

effective stress paths, both exhibited flow liquefaction at the FLS. This suggests a boundary

between stable and unstable soil conditions.

Flow liquefaction is unique from cyclic mobility because it requires shear stresses higher

than the steady state strength. Gravity is the driving force behind most of the shear stresses and

will typically remain consistent until large deformations disturb the soil. When a soil sample

plots initially on the shaded surface of Figure 2-10, it will be susceptible to flow liquefaction.

Figure 2-10: Zone of susceptibility to flow liquefaction (Kramer, 1996).

15

2.2.2.2 Cyclic Mobility

Where an initial shear stress plots below the steady state line, extended horizontally out

from the steady state point, flow liquefaction cannot occur, but cyclic mobility can take place.

The initial states area where cyclic mobility can be triggered is shaded by the dark region in

Figure 2-11. This area captures both low and high confining stresses, an indicator that both loose

and dense soils are susceptible to cyclic mobility.

Figure 2-11: Zone of Susceptibility to cyclic mobility (Kramer, 1996).

Three different scenarios, as shown in Figure 2-12, describe the possibilities for cyclic

mobility: a) no stress reversal and no exceedance of steady state strength, b) no stress reversal

with brief exceedance of steady state strength, and c) stress reversal with no exceedance of

steady state strength. With each of these scenarios, there is an incremental loss of strength and

deformations as it approaches the steady state point.

The first condition (Figure 2-12 (a)) of cyclic mobility is described by no stress reversal

(τstatic – τcyc > 0) and no exceedance of steady-state strength (τstatic + τcyc < Ssu). This means the

stress path moves left and reaches the drained failure envelope without making contact with the

FLS. The stress conditions stabilize and flow deformations won’t develop because any additional

16

strains would lead to more dilation. The effective confining pressure decreases and the resultant

low stiffness generates permanent strains with each load cycle.

The second condition (Figure 2-12 (b)) happens under parameters of no shear stress

reversal (τstatic – τcyc > 0) and a momentary exceedance of steady state-strength (τstatic + τcyc > Ssu).

The stress path makes contact with the FLS, creating temporary periods of instability, allowing

permanent strains to develop at these points. This straining stops after the cyclic loading stops.

Under the third condition, Figure 2-12 (c), there is a stress reversal (τstatic – τcyc < 0) and no

exceedance of steady-state strength (τstatic + τcyc < Ssu). Tests were able to show that the

increasing stress reversal correlates to an increase in the rate of excess pore pressure generation

(Dobry et al., 1982; Mohamad & Dobry, 1986). This leads to an oscillation around the

compressive and extensive parts of the failure envelope (Kramer, 1996).

Figure 2-12: Three cases of cyclic mobility (after Kramer 1996).

Sloped ground is the most susceptible to cyclic mobility when experiencing long durations

of earth motions. Level sites with short durations might expect small strains. Unlike flow

liquefaction, there’s no exact stage at which cyclic mobility is initiated, as it tends to strain

gradually with each cyclic pulse.

17

2.2.2.3 Triggering Procedure

Principles like flow liquefaction and cyclic mobility can be further characterized by

liquefaction triggering methods, a means by which to quantify a soils vulnerability to

liquefaction. Among the methods exists one of the most common approaches: cyclic stress

approach. This approach condenses a soil’s potential to liquefy into a single variable: a factor of

safety against liquefaction (FSL). FSL represents a ratio of a soil’s capacity to resist stresses over

driving forces demand. Several models have been created to best describe both aspects of the

FSL ratio. The models used in the analysis for this paper will be described in this section.

The foundation for many of the liquefaction triggering models (Seed, 1979; Seed & Idriss,

1982) define FSL ratio into cyclic resistance ratio (CRR) and cyclic stress ratio (CSR), per

Equation (2-1). Both of these components will be described in detail separately. When this ratio

is less than one, liquefaction triggering occurs. This means that the resistance of the soil is less

than the seismic demand. CRR is a measure of the soil’s ability to resist seismic demand. Soils

with properties of a higher CRR will require more intense earthquake loads to reach a state of

liquefaction. CRR is determined by the amount of cyclic shear stress necessary to initiate

liquefaction ( ,cyc Lτ ). CSR is determined by the amount of cyclic shear stress imposed by an

earthquake loading ( cycτ ).

,cyc LL

cyc

Capacity CRRFSDemand CSR

ττ

= = = (2-1)

CSR is a measure of the shear stress of an earthquake loading, or demand, and is

approximated using a simplified method (Seed & Idriss, 1971) with Equation (2-2):

18

( )max 1 10.65'v

dv

aCSR rg k MSFσ

σσ

= ∗ ∗ (2-2)

with the terms amax as peak ground surface acceleration as a percent of gravity, σv as the total

vertical stress of the soil layer, σv’ as vertical effective stress, rd as stress reduction factor, Kσ as

the overburden correction factor and MSF as magnitude scaling factor. The reduction,

correction, and scaling factors are calculated uniquely by different proposed methods. The

Robertson (2009) method relies on some of the research of others to establish best fitting

variables. Correction and scaling factors are not unique to this method as they carry an

applicability to a large variety of methods. Youd et al. (2001) defines the MSF in Equation (2-3)

as:

2.24

2.56

10

w

MSFM

= (2-3)

where wM is earthquake moment magnitude. Liao and Whitman (1986), Robertson and Wride

(1998), Seed and Idriss (1971) define rd in Equation (2-4) as:

1.0 0.00765 9.151.174 0.0267 9.15 230.744 0.008 23 30

0.5 30

d

z z mz m z m

rz m z m

z m

− ≤ − < ≤= − < ≤ >

(2-4)

where z is depth in meters to the soil layer. Finally Youd et al. (2001) defines Kσ in Equation

(2-5) as:

( 1)

0'f

v

a

KPσσ

−

=

(2-5)

19

where 0'vσ is the effective overburdened stress, aP is the atmospheric pressure approximated by

100 kPa, and f is an exponent that is a function of site conditions.

The CRR derivation is a more intense, iterative process that requires detailed steps and

calculations. The Robertson & Wride (1998), in correlation with the updated Robertson (2009)

method (jointly referred to as the Robertson and Wride method in this paper), has been chosen as

the CRR derivation in this paper because of the widespread acceptance and usage for CPT-based

liquefaction resistance method. In this method, they established a way to quantify CPT input

values into a single, catch-all output variable, tncsQ . This output variable represents a corrected

cone tip resistance value, normalized to a clean sand equivalent. tncsQ is then used to calculate a

CRR value. Classifying soils and their properties into tncsQ allows soils that may contain

properties like cohesion to be treated as an equivalent sand sample exhibiting similar resiliency

against liquefaction. See the summary of steps in Figure 2-13 below for a visual overview the

following procedure.

Soil behavior type index is a measure of how much a soil will exhibit behavior tending

toward a fine-grained or coarse-grained soil. Robertson (1990) published a correlation between

cI from cq and sf . This correlation continues to be updated (Jefferies & Davies, 1993; Robertson

& Wride, 1998; Robertson, 2009). A chart such as Figure 2-14 can be used to determine a soil’s

behavior type index. These charts establish zones of soil type which are often correlated to soil

behavior, including liquefaction susceptibility. To calculate the soil behavior type, the Robertson

and Wride method uses Equation (2-6) as:

( ) ( )0.52 23.47 log 1.22 logc tn rI Q F = − + + (2-6)

20

Figure 2-13: Summary of the Robertson and Wride method (after Robertson, 2009) CRR procedure.

21

With tnQ as the dimensionless corrected cone tip resistance (sometimes denoted as Q , like

Figure 2-14), and rF as the normalized friction ratio (sometimes denoted as F , like Figure

2-14).

The equations for tnQ and rF are show in Equations (2-7) and (2-8) as:

'

n

t v atn

a vo

q pQpσ

σ −

=

(2-7)

*100sr

t vo

fFq σ

= −

(2-8)

where tq is a corrected cq value, often generated after the soil investigation, although the

difference is often small.

Figure 2-14: Normalized soil behavior type chart (after Robertson & Wride, 1998). Soil types: 1 sensitive, fine grained; 2 peats; 3 silty clay to clay; 4 clayey silt to silty clay; 5 silty sand to sandy silt; 6 clean sand to silty sand; 7 gravelly sand to dense sand; 8 very stiff sand to clayey sand; 9 very stiff, fine grained.

22

However, because Equation (2-7) includes the stress exponent, n , as a part of the

calculation for tnQ , tnQ is used to calculate cI , and cI is used to calculate n , the process is

iterative. The iteration is begun with a seed value of n =1.0. Calculation of n continues until a

change in n is less than 0.01. Once a change in n is negligible, the cI value will reflect the true

soil behavior type.

With known values of cI and tnQ , tncsQ can be calculated using Equation (2-9) as:

tncs c tnQ K Q= ⋅ (2-9)

where cK is a function of cI , per Equation (2-10):

( )

4 3 2

16.767

1.0 if 1.641.0 if 1.64 < 2.36 and 0.5%

0.403 5.58 21.63 33.75 17.88 if 1.64 < 2.50

6 10 if 2.50 < 2.70

c

c rc

c c c c c

c c

II F

K I I I I I

I I−

≤ < <= − + − + − < × <

(2-10)

With a tncsQ evaluated for the layer, a CRR can now be determined using Equations (2-11) and

(2-12):

3

7.5 93 0.08 for 2.701000

tncsc

QCRR I = + < (2-11)

7.5 0.053 for 2.70tncs cCRR Q I= > (2-12)

With CRR and CSR calculated, a factor of safety against liquefaction can be established as

per Equation (2-1) for a single soil layer. This calculation can be applied to all the soil intervals

in a profile sounding. The FSL can further be applied to remediate against effects of liquefaction.

23

2.2.3 Effects

The effects of liquefaction can range from minor to catastrophic levels of damage. These

effects can vitiate key infrastructure elements such as roads, utilities, bridges, ports, buildings

and more. Several of the most common effect will be discussed in this section, including: lateral

spread, settlement, loss of bearing capacity, increased lateral pressure on walls, alteration of

ground motions, and flow failures.

2.2.3.1 Lateral Spread

Lateral spread is a major effect of liquefaction and will be explained in further detail in the

next chapter. A shear plane of liquefied soil allows blocks of surface soil to shift down a gentle

slope or towards the toe of a sharper slope. The deformed ground can exhibit cracking, fissures,

side margin shear deformation, and buckling at the toe of the slope. A parking lot in Wufeng,

Taiwan experienced lateral spread during the 1999 Chi-Chi earthquake as in Figure 2-15. Lateral

spread can develop anywhere from a few centimeters to several meters.

2.2.3.2 Settlement

Saturated, loose sands can cause settlement after liquefaction. As water and sand particles

are forced upward from excess pore pressure generation, the voids are filled in with surrounding

soils. The filled-in voids create denser soil layers. This compaction of liquefied layers will cause

settlement, even up to the surface. Where settlement occurs unevenly, namely differential

settlement, pipe lines can be severed or building foundations damaged. Settlement from

liquefaction can create large economic strains on affected areas.

24

Figure 2-15: Lateral spread-induced fissures after the 1999 Chi-Chi earthquake (after Chu et al., 2008).

2.2.3.3 Loss of Bearing Capacity

Loss of bearing capacity is a term used to describe soils weakened below the point of

supporting above soils or structures. The weakening is caused by a loss of shear strength from

liquefaction. A loss of bearing capacity can damage footings or embankments. Famously, several

apartment buildings tipped in the aftermath of the 1964 Niigata earthquake due to a loss in

bearing strength (Figure 2-16).

2.2.3.4 Increased Lateral Pressure on Walls

Liquefaction can also increase pressure on walls, potentially beyond their design strength.

Excess pore water pressure generation tends to dissipate with a rise in groundwater towards the

25

surface. Imposed hydrostatic forces add to the static lateral pressures soil retaining walls might

feel from the backfill. Earthquake-imposed motions, with the added increase of lateral pressure,

can be enough to deform or lead to a failure of a wall.

Figure 2-16: Tilted apartment buildings caused by liquefaction and loss of bearing strength (USGS, 2006).

2.2.3.5 Alteration of Ground Motions

Ground motions that reach the surface can be altered by liquefaction, potentially

dramatically. A change in motion occurs after a soil has begun to liquefy. Liquefaction-generated

excess pore pressures cause a soil to become less stiff. Softer layers transmit waves differently,

specifically, allowing more low-frequency waves to reach the surface. Low-frequency waves can

generate large displacements. This poses a risk to structures with low natural frequencies and

buried utilities.

26

2.2.3.6 Flow Failures

One final effect of liquefaction is flow failure. As previously mentioned, flow liquefaction

is one of the most damaging effects of liquefaction. Flow failures can produce rapid, large

displacements with little warning. Existing static stresses drive flow failures on sloping ground.

Large masses can display fluid-like flow, moving quickly downslope. The speed can create

sufficient momentum to destroy structures and displace large volumes of soil.

Equation Chapter (Next) Section 1

27

3 REVIEW OF LATERAL SPREAD DISPLACEMENT

Lateral Spread Overview

Lateral spread is a major component of liquefaction. Its discovery, how to predict it, and

some model deficiencies will be discussed in this chapter. Lateral spread is liquefaction-induced

horizontal movement of blocks of earth toward the base of a sloped face. This movement is

caused by a loss of soil strength due to loading mechanisms described in the previous chapter.

Weakened, liquefied layers will shift and relax to find a more stable position to support the

above soil, potentially creating large disturbances, even up the surface, as visualized by Figure

3-1.

Figure 3-1: Lateral Spread Visualization, after Rauch (1997)

28

The severity of lateral spread has been observed to vary from a few centimeters to a few meters,

even at different locations within the same earthquake event. (Bartlett & Youd, 1995) It can

damage roadways, structures, canals, retaining walls and, as in the case of the 1906 San

Francisco earthquake and fire, even fatally disrupt lifelines. (Youd & Hoose, 1978) The inability

to access water from the damaged network in this disaster lead to an increased damage to the city

(Figure 3-2), as the fire was not able to be contained.

Figure 3-2: Damaged San Francisco City Hall, 1906 (after Niekerman, 2018)

Major contributing factors to lateral spread not only include earthquake loading

mechanisms, but also soil geometry and soil composition. Element specifics were not always

29

known, or their relevance understood. Laboratory tests were conducted to better understand the

role of driving mechanisms. Many analytical and empirical models discriminate the relevance of

different factors on ground displacement. Several key tests and models will be discussed in this

chapter.

Laboratory Tests

An understanding of earthquake-derived lateral spread mechanisms can lead to better

quantification of lateral spread displacement predictions. To better understand how soil responds

to earthquake motions, researchers created shake table models and centrifuge models. Shake

table models are physical pulse simulations that apply a target acceleration value to a soil sample

to gauge the level of response in a representative soil column. Centrifuge models increase the g-

force of a soil sample to mimic stresses experienced at deeper layers. The size of both of these

methods can vary from small to large scale, allowing for a wide range of experiments.

Researchers have tested varying thicknesses and slopes to understand how average displacement,

duration, velocity, and thickness of susceptible layers contribute to lateral spread. (Kramer,

1996)

Sasaki et al. (1991) experimented with a tri-layered shake-table system, dense gravel over

loose sand over dense sand. Running eight tests, researchers found the largest displacements

were at the bottom of the liquefied layer and that lateral spread was only observed during

shaking. Yasuda et al. (1992) conducted shake table tests on 24 soil models, a variety of different

slopes, layer thicknesses and densities. They also conducted vane shear and cyclic torsional tests

to measure the rate of reduction of shear strength. These researchers found that critical variables

included layer thickness and ground slope.

30

Where shake table tests typically don’t induce more than 1g applications, centrifuge

models were created to impose >1g conditions. Centrifuge models simulate gravity-induced

stresses, modeling the stress at deeper layers. Encapsulated samples experience both in-situ stress

conditions and increased stresses from simulated loading events.

Centrifuge tests by Toboada-Urtuzuastegui & Dobry (1998) focused on loose sand and

implemented a flexible wall laminar container, allowing the shear strains to be felt on the

interface of the soil. These researchers noticed several key trends. An increase in surface slope

correlated both to a decrease, or leveling out, of pore pressures and an increase in shear strain

and permanent lateral deformation. When analyzing maximum acceleration, an increase of PGA

correlated to an increase in shear strains and permanent lateral deformation. Researchers also

observed negative spikes in pore water pressures, ones that related to positive spikes in

acceleration and strain deformations. Under these accumulated shear strains, the sand would

dilate, and a resultant drop of pore water pressure would follow. This would lead to a sharp

increase in accelerations because the ground began to densify, creating a stiffer medium for the

pulses to travel through more easily.

Between shake-tables and centrifuge models, researchers began to hone in on important

factors and characteristics that contribute to lateral spread. Such key factors include ground

slope, thickness of susceptible layers, shaking intensity and duration, and more. Where lab tests

attempt to draw conclusions from recreated field conditions, analytical and empirical models

aggregate understanding and field data to generate predictive equations.

31

Lateral Spread Models

Lateral spread models are processes that interpret site data to predict lateral spread

behavior, by means of analytical or numerical models. Analytical models seek to describe

patterns of lateral spread by reliance on principle understanding of soil behavior and mechanics.

Empirical methods utilize observations and measurements to quantify evidence. Both methods

have evolved as new information and understanding has come to light. An introduction to both

types will be presented in this section.

3.3.1 Analytical

Analytical relationships are based off current understandings of soil mechanics and basic

physics. These models typically express predictions using closed-form functions or stepwise

iterations. They describe changes in a system with an analytic function. Three relevant models

include: Numerical Models, Elastic Beam Model, and the Newmark Sliding Block Analysis.

3.3.1.1 Numerical Models

Numerical models build a framework of nodes and elements in a simulated soil structure

to calculate forces or displacements at each component. These models include two different

approaches: finite element and finite difference. The finite element method relies on partial

differential equations to approximate boundary value problem solutions. Discreet nodes are

solved directly. The second approach, finite difference, solves differential equations with

difference equations, where terms are defined as a function of the previous terms. (Hadush, et al.,

2001) Both methods can handle complex systems with the capacity to include a wide variety of

soil parameters. These procedures rely on constitutive models between soil mechanics and stress-

32

strain behavior in soils. The inherent challenge for creating the models lies in complexity of soil

mechanics and uncertainty of liquefied soil behavior and its residual strengths.

The capability to generate and calculate increasingly complex equations and solutions has

been facilitated by the advent and evolution of computers. Early models in the late 1970,

(Zienkiewics et al., 1978; Zienkiewics & Shiomi, 1984; Finn et al., 1986) (Shiomi, et al., 1987)

have been improved upon to more sophisticated and accurate models (Gu, et al., 1994); (Yang,

2000); (Yang et al., 2003); (Arduino, et al., 2006); (Valsamis, et al., 2010); (Zhang & Wang,

2012). This method will continue to advance with the increased ability of computers and future

research.

3.3.1.2 Elastic Beam Model

Another predictive model is the elastic beam model. Created as a simplified method to be

applied to a large area, Hamada et al. (1987) sought to better understand the displacements

measured after the 1983 Japan 7.7 earthquake. The study treated the unsaturated surface layers as

a board floating on water, the liquefied soil layer. It also assumed no friction between these two

elements. Once liquefaction has been reached, the upper layer is subject to gravitation forces and

the amount of deformation is approximated with the model. Aside from developing this method,

they also came to the conclusion that, in certain areas, ground slope caused displacement. Some

further research was published on the model showing minimum potential energy as a governing

principle (Towhata, et al., 1991; Towhata, et al., 1992; Yasuda et al., 1992). Deficiencies in the

elastic beam model include the amount of uncertainty grounded to the simplifying assumptions

of a 2-D model, a continuous elastic beam, soil beam elastic moduli, and liquefied soil shear

33

moduli. These limiting deficiencies, although producing a simplified model, deter widespread

applicability and usage.

3.3.1.3 Newark Sliding Block Analysis

Newmark (1965) presented the Newark sliding block analysis as a method to subject an

idealized rigid block of soil, i.e. sliding block, to earthquake motions. This approach was meant

as an improvement upon the pseudo-static model, a method that would only detail collapse state

information. The sliding action occurs when the loading accelerations cause enough inertia to

overcome the friction between the two elements. This idea was improved upon to predict lateral

spread displacements (Dobry & Baziar (1991); Byrne (1991); Byrne et al. (1992); Bazier et al.

(1992); Taboada et al. (1996)). Others have created semi-empirical methods to predict lateral

spread displacements based off predicted seismic slope displacements (Saygili & Rathje, 2008)

(Bray & Travasarou, 2007). These, as with any methods, have their limitations and shouldn’t be

extrapolated to outside their specified bounds.

3.3.2 Empirical Methods

Empirical methods have been developed using lateral spread displacement case history

databases to predict future displacements from statistical regression analysis. Rather than using

analytical or physical models to formulate predictions, regression equations analyze historic data

to draw conclusions. One specific type of empirical model is the multiple linear regression

(MLR) model which seeks to associate two or more explanatory variables with a response

variable, tying the observed data to a linear equation. Empirical methods came about because of

readily accessible information such as standard field tests, common soil properties, and

topographical information. Despite their lack of direct basis in theoretical soil mechanics,

34

practitioners have widely accepted these methods because of the large amounts of recorded case

histories used to create the models.

There are inherent strengths and weaknesses associated empirical models. One weakness

of includes a reliance on accurate information to create strong models. Detailed reconnaissance

information is difficult to obtain. Inaccurate or incomplete data will lead to weakened

relationships. Another weakness of many current empirical models is their overwhelming

reliance on Japanese and Western U.S. data. One final limitation is that there is an acceptable

range of input variables that may not capture every potential site. The use of extreme or

unsuggested values for analysis is extrapolation and may lead to unrealistic results. In spite of

these weaknesses, empirical methods are recognized and used widely. The methods are easy to

apply with a spreadsheet, not requiring experienced judgement of soil mechanics or a deep

technical knowledge of the methods. The inherent liability with accessibility is the potential for

misuse, treating the system as a black box, but this can be satiated with proper training and

experience.

Empirical models have evolved as researchers have clued into relevant contributing

factors. Although early models relied upon a few simple parameters, predictive models have

since become more complex, often requiring detailed site and event information. The wrestle for

accessibility in these models has been centered on accurate data being readily available.

One of the first models was generated by Hamada et al. (1987). Pulling deformations

from the 1983 Nihonkai-Chubu, 1964 Niigata, and 1971 San Fernando earthquakes, input

parameters included ground surface slope, soil stratigraphy orientation, and thickness of

liquefiable layers. Although able to predict 80% of the displacements within a factor of two, this

35

model was only robust for the earthquakes included in the regression. With only a few inputs,

one might see how there might be more relevant factors weighing on lateral spread.

Utilizing more variables, Youd and Perkins (1987) altered the scope of how to predict

lateral spread. The researchers targeted not only lateral spread but attempted to characterize

entire hazard regions. Building off several Western US case histories, a liquefaction severity

index (LSI) was created. This term defines a maximum possible lateral spread that could occur in

conditions of wide, active flood plains, deltas, or other gently sloping Holocene fluvial deposit

area. LSI was defined by source-site distance and magnitude. Slightly more accurate than the

previous model, it still lacked factoring in soil conditions.

Pulling from a much wider database of lateral spread, Bartlett and Youd (1992), (1995)

created a MLR empirical relationship. With 476 displacements, a range of source-site distances

up to 90 km, and magnitudes ranging from 6.1 to 9.2, these researchers honed in on source and

site factors as a key contributor in lateral spread. They also distinguished between free-face and

sloping ground conditions, creating different equations for each type. This approach landed

accuracy of 90% of predicted-to-observed by a factor of two. The strong ability to reasonably

predict deformations has led to a widespread acceptance of this method. This imperfect model

tends to over-predict displacements close to the source and has an added detriment of uncertainty

from undetailed-stemmed estimated values. This model was later improved upon with more case

histories, more detailed information, and more relevant variables. (Youd et al., (1999); Youd et

al., (2002))

The Youd et al. (2002) model introduced several improvements over the Bartlett and

Youd (1995) model. This model used a wide database of SPT borings. Youd, Hansen, and

Bartlett added corrections and improvements to generate a more robust regression. Such changes

36

included removing erroneous estimates of displacements, additional case histories, more

precisely factoring in mean grain size, and a modification to better predict displacements near the

source. The mean-grain size alteration allowed the model to account for site-specific soil

characteristics, specifically course-grained soil displacement predictions. The model was updated

to prevent unrealistically high predictions with small source-site distances. This update became

magnitude dependent and set boundaries to prevent the model from generating unrealistic results.

Terms included: nearest site-source distance (R), earthquake moment magnitude (M), modified

source distance (R*), and a function of magnitude to calculate R* (Ro). These terms are

described in the following equations:

*0R R R= + (3-1)

( )0.89 5.640 10 MR −= (3-2)

and apply to both free face and sloping ground conditions.

Other key terms to the regression equation that were continued from the first iteration

include: free face ratio (W, %), defined as the height of the free face (H) divided by the distance

from the toe of the face to the location of interest (L), the cumulative thickness of saturated

granular layers with corrected blow counts less than 15 (T15), average fines content of T15 layers

(F15), average mean grain size of T15 layers (D5015), and ground slope (S, %). These terms are

used to create both the free-face and sloping ground prediction displacement equations as

detailed in Equation (3-3) and Equation (3-4) where DH is the estimated lateral ground

displacement. The free-face model is given as:

37

*

15 15 15

log( ) 16.713 1.532 1.406log 0.012 0.592log0.540log 3.413log(100 ) 0.795log( 50 0.1mm)

HD M R R WT F D

= − + − − ++ + − − +

(3-3)

and the sloping ground model is given as:

*

15 15 15

log 16.213 1.532 1.406log 0.012 0.338log0.540log 3.413log(100 ) 0.795log( 50 0.1mm)

HD M R R ST F D

= − + − − ++ + − − +

(3-4)

With these regressions, however, there are certain limits. To limit extrapolation from outside the

analyzed terms, boundaries were set on the equation input values, as shown in Figure 3-3.

Figure 3-3: Allowable bounds of inputs, after Youd et al., (2002).

The MLR case histories included valuable data to better understand and quantify lateral spread,

however, this data only encompasses a fixed range of variables. One should only apply these

equations within their recommended bounds.

Zhang et al., 2004

Zhang et al. (2004) developed a semi-empirical approach for predicting lateral spread

displacement using CPT-based data. Having become a robust and repeatable method, CPT

soundings were an obvious untapped field of data investigation to include in lateral spread model

prediction. The theory and application of the Zhang model is presented here. Any further

38

reference Zhang or Zhang model will refer to the Zhang et al. (2004) paper and the model

presented therein.

3.4.1 Zhang et al., 2004 CPT Model

Previous studies built the foundation for principles utilized in predicting lateral spread in

the Zhang model. Ishihara and Yoshimine (1992) established a relationship between max cyclic

shear strains (γmax) and factor of safety (FS) against liquefaction for a range of relative densities

(Dr) of clean sands. Their research relied on cyclic simple shear laboratory tests conducted by

Nagase and Ishihara (1988). Seed (1979) proposed the idea that a maximum amount of shear

strain could develop at a given relative density, regardless of the number of loading cycles. Any

increased strains would be difficult to develop, unless the loading surpassed the complete

undrained resistance of the soil. The Zhang model utilizes this principle to limit the max shear

strain on the Ishihara and Yoshimine (1992) correlations, creating a bounded relationship, as

seen in Figure 3-4.

Figure 3-4: Relationship between cyclic shear strain and factor of safety at varying relative densities (after Zhang et al., 2004).

39

Zhang implemented the Tatsuoka et al. (1990) correlation to translate cone tip resistance

(qc) to Dr as described in:

185 76log( )r c ND q= − + (3-5)

where qc is corrected for effective overburden stress at 100 kPa to provide qc1N and qc1N is

capped at 200. To correct for the presence of fine grained materials in these layers, (qc1N)cs values

are generated to then be used to calculate Dr. This allows layers that might better resist lateral

movements, or have an increase in CRR, to be represented as an equivalent clean sand q value.

Robertson and Wride (1998) proposed the clean sand equivalent relationship as Equation

(3-6):

1 1( )c N cs c c Nq K q= (3-6)

where Kc is the grain characteristic correction factor. This factor is defined in Equation (3-7) and

Equation (3-8) as:

1.0 for 1.64c cK I= ≤ (3-7)

4 3 20.403 5.581 21.63 33.75 17.88 for 1.64c c c c c cK I I I I I= − + − + − > (3-8)

With a cone tip value corrected for grain characteristic and effective overburdened stress,

one can determine Dr following Equation (3-5). Using this relative density with the chart

40

developed by Zhang, Figure 3-4, and a FS value as detailed in Chapter 2, a γmax value can be

generated for each reading interval.

As a penultimate step, a term of lateral displacement index (LDI) describes the

cumulative contributions of lateral spread by layer as in Equation (3-9):

max

max0

z

LDI dzγ= ∫ (3-9)

where Zmax is the maximum depth beneath all layers with liquefaction potential. This term is a

summation of each calculated lateral spread, by layer. However, LDI does not account for site

geometry.

To best generate a relationship that would account site geometry, Zhang relied on

empirical data to tie displacements to sloping or free-face conditions. Comparing computed LDI

values to measured lateral displacement (LD) values, the following relationships were generated

based on known ground slope (S), or free face height (H) and distance to free face (L) in

Equation (3-10) and Equation (3-11):

0.2( )*LD S LDI= (3-10)

for gently sloping ground and

0.8

6* *LLD LDIH

− =

(3-11)

for level ground with a free face. Zhang et al. also advise against using this method outside any

of the parameter bounds that were used to analyze this data, including 0.2%<S<3.5% and

4<L/H<40 and those a part of the FS calculation, detailed in Chapter 2.

41

3.4.2 Zhang et al., 2004 Model Deficiencies

Despite the increased recognition and widespread use of the Zhang et al., (2004)

procedure, it comes with its deficiencies. These deficiencies include those relevant to this

specific model, and ones tied to empirical methods, in general. The Zhang et al., (2004) method

places a primary focus on detailing lateral spread displacements as a function of liquefaction-

induced cyclic shear strain. With this approach, other potentially contributing factors are

excluded, such as shear deformation and deformations due to water films or void distribution.

Another critical assumption includes the analyzation of CPT soundings individually. This treats

the narrow column of soil tested by a CPT sounding as representative of the whole site, which

then allows non-continuous layers to be considered continuous. Layer continuity factors in to

how much potential lateral spread over a certain area. As with any empirical model, the

accounting for topography is only as good and accurate as the analyzed historical data. Future

and since-recorded events might alter the empirical relationships. The Zhang method also does

not perfectly capture any or all scenarios accurately. As evidence of this, the Zhang et al. (2004)

paper includes a breakdown of calculated by measured lateral spread, capturing a certain amount

of the predicted deformations within a 0.5x to 2x ratio. Not only is this a relatively wide berth to

establish correlation, but there are still predicted values that lie outside this range. Another

deficiency is the inclusion of very thin layers. Although these layers, by calculation, would

suggest deformation at the surface, observations and guidelines (Youd et al., 2001; Robertson,

2009) have begun to rule out these attenuated layers. One final flaw is that the Zhang et al.

(2004) method does not directly account for fines content in the soil, since it’s based off CPT

data. It does establish a cut-off for certain soils (Ic > 2.6) that are likely too clay-rich or plastic to

42

liquefy. However, Youd et al. (2009) went further to demonstrate that liquefied soils could have

the potential to not spread laterally due to the plasticity in the fines.

The purpose of this paper is to evaluate modifications to the Zhang method, as presented

in the following sections.Equation Chapter (Next) Section 1

43

4 PROPOSED MODIFICATIONS TO CPT-BASED LATERAL SPREAD

PREDICTION PROCEDURE

The purpose of this study is to test six common modifications (from personal

communication with Drs. Youd and Robertson) to the Zhang et al. (2004) procedure against a

collection of actual lateral spread case histories. This chapter describes the analysis performed to

assess the validity of modifications applied to the Zhang et al. (2004) model for layered soil

deposits. The analysis modifies the Zhang et al. (2004) lateral spread model for given case

histories and compares observed displacements to the predicted displacements of the unmodified

model and the modified model. This chapter introduces and explains the methodology for each

of the modifications. An example problem shows how the modifications can be applied to a case

history sounding. The background and reasoning for the utilized case histories are detailed.

Selected Model

The modifications are best understood in the context of the base case, or unmodified

model. The base case is computed without any of the proposed modifications, following the

Zhang et al. (2004) lateral spread model in conjunction with the Robertson and Wride (1998)

triggering model and Robertson (2009) update, as detailed in Section 3.4. This method converts

CPT values of depth, sleeve friction, cone tip resistance, and pore water pressure measurements

through calculations of Ic with input parameters like acceleration and magnitude to create a

44

lateral displacement value summed by depth to represent LDI. Taking ground site slope

conditions into account, users calculate total predicted horizontal surface displacement. Some

existing computer programs already automate this calculation process, e.g. CLiq

(GeoLogismiki).

Because many of the model modifications evaluated in this study alter intermediate

calculation values, a new computer program was required integrate the modifications. Dr.

Franke’s research team developed such a program, CPTLiquefY (Franke, et al., 2019), and the

modifications have been implemented and made available on that platform. The following

sections will describe how each modification is computed.

Proposed modifications are applied only to the Zhang et al. (2004) model for this study.

Although the modifications may apply to other strain-based lateral displacement models, the

methods may require some adaptation and such translations are beyond the scope of this study.

Modifications

Six lateral spread modifications are evaluated in this study. These modifications were

developed by Professors T. L. Youd and P. K. Robertson and were provided for this study by

personal communication. Both Drs. Youd and Robertson indicated that they apply these

modifications regularly in their consulting, but the modifications have never been thoroughly

vetted or tested on actual case history data. Some of the modifications have been published, and

some have not. A discussion of the basis and method for each modification is provided in this

section.

45

4.2.1 Soil Transition Zone Modification

Two main strengths associated with the CPT are that it provides a continuous soil profile

and that it adequately registers cone tip resistance of very strong and very weak materials. A

deficiency, however, is with thinly interbedded layers of materials that have a sharp contrast of

cone tip resistance; the CPT does not adequately report these drastic shifts (Ahmandi &

Robertson, 2005). CPT readings instead tend to show the strength of the soil layer below, before

the tip reaches that layer (Treadwell, 1976). CPT tip resistance is also impacted by the zone of

shear influence in front of the cone, from 2-3 cone diameters in soft materials to 10-30 cone

diameters in stiff material (Ahmandi & Robertson, 2005). The zone of influence is illustrated in

Figure 4-1.

Figure 4-1: Cone Tip Resistance influenced by the zone of influence ahead of the cone.

Zone of influence

46

Take the example of a cone approaching the end of a thin, stiff layer deposited on top of a

weak layer. As the cone is pushing through the thin layer, the weak layer below would not as

effectively resist the force of the cone and would register a falsely low cone tip resistance value,

under-valuing the strength of the stiff soil layer before the tip has exited the weaker layer. This is

shown by the gradual decrease in tip resistance in Figure 4-2. This presents a problem in design

because the total thickness of layers susceptible to liquefaction may be increased, generated by

the falsely high (or low) cone tip resistance.

Figure 4-2: Cone tip resistance transitioning between contrasting stiff/soft layers (after Ahmandi and Robertson, 2005).

To remediate this issue, the soil transition zone modification attempts to correct for these

transitionary zones by multiplying the cone tip resistance by a KH factor, per Equation (4-1):

*c H cq K q= (4-1)

47

where KH is a multiplier proposed by Youd et al. (2001) in Equation (4-2):

2

0.25 1.77 1.017

cH

Hd

K

= − +

(4-2)

and is based on cone diameter, dc, and layer thickness, H.

The modification for soil zone transition is identified by looking for a positive change in

Ic (+ΔIc) values over a specified number of layers or thickness. Soil increments that have a ΔIc >

0.01, as defined by Robertson (2011), constitutes a steep change and indicates a transition zone.

Youd et al. (2001) , the source of the KH modifier, only defines this calculation in terms of a thin

sand layer and does not specify a thickness for a transition layer. Ahmandi and Robertson (2005)

does not specify a recommended thickness for a transition layer. Robertson (2009) states several

data points are ‘in transition’ when data are collected at close intervals. CPTLiquefY allows for

the user to change these inputs, but defaults to an increase ΔIc of 0.01 over 4 layers, after

Robertson (2011). Once identified, CPTLiquefY then multiplies the qc value by a KH value to

better represent the soil layer as a strong layer or a weak layer. After qc has been modified, the

Zhang et al. (2004) method continues the computation with the updated qc*.

4.2.2 Thin Sand Layer Modification

Sand layers can potentially be too thin to liquefy or to cause significant lateral spread on

the surface due to limiting shear strains. Such layers could lead to potential lateral spread over-

estimation. Thin sand layers are also difficult to correctly characterize with CPT, due to the

transition zone effect discussed in the previous section. Inaccurate values present a challenge of

under-predicting or over-predicting lateral spread (Youd et al., 2001; Robertson, 2009). Thin

48

dense sand layers embedded in soft clay deposits are often incorrectly identified as potentially

loose sands (Ahmandi & Robertson, 2005). Thin sand layers in clay deposits may register a

lower value because the cone comes into the sand layer influenced by the clay layer above and it

begins to feel the influence of the layer below before it starts to exit the layer, as seen in Figure

4-3. Ahmandi and Robertson (2005) suggested using the Youd et al. (2001) cone tip

modifications listed in Equations (4-1) and (4-2). This modification uses similar bounds to

identify sand layers (Ic < 2.6) and an upper bound of thickness < 0.3 m, as recommended by Dr.

Youd (personal communication, Youd T. L., 2018). This modification would theoretically

provide a more accurate stratigraphy for analysis, as the modified cone tip resistance would more

accurately reflect true soil properties and layering.

Figure 4-3: Modifying thin sand layer cone tip resistance (after Ahmandi & Robertson, 2005).

49

4.2.3 Dilative/Contractive Behavior Modification

Current SPT-based lateral spread displacement prediction methods filter out dilative soils

with the criterion of (𝑁𝑁1)60 < 15. This cutoff approximately coincides with the SSL boundary,

suggesting soils with (𝑁𝑁1)60 ≥ 15 are dilative at large strains and not prone to generating large

displacements under undrained cyclic loading. Relying on the state parameter, ψ, established by

Jeffries and Been (2006) and ψ correlations for the CPT by Robertson (2010), soils can be

roughly characterized as dilative or contractive using only CPT data. Youd believes that soils

with ψ < -0.05 would tend to behave as a dilative soil when sheared and would therefore not

dilate (personal communication, Youd, T. L., 2016). Youd argues that ψ < -0.05 is approximately

equal to a clean-sand equivalent normalized CPT tip resistance 𝑄𝑄𝑡𝑡𝑡𝑡,𝑐𝑐𝑐𝑐 < 70 for most practical

geotechnical projects. Based on these principles, CPTLiquefY filters out layers with 𝑄𝑄𝑡𝑡𝑡𝑡,𝑐𝑐𝑐𝑐 < 70

from contributing to LDI because they are potentially dilative when strained.

4.2.4 Soil Depth Modification

The effect of depth has always been a consideration for liquefaction triggering

procedures, specifically as it relates to the shear stress that soil layers may feel. A stress

reduction coefficient was used to limit strains for deeper soil layers in liquefaction initiation

assessment (Seed & Idriss, 1971). Linearly weighting volumetric strains with depth was

proposed to limit vertical strains in deeper soil layers (Iwasaki et al., 1982). Both Professors

Youd and Robertson have applied a similar approach for shear strains (personal communication,

Youd and Robertson, 2016). This approach reduces the calculated shear strains at depth (zi) with

a depth weighting factor (DFi), as in Equation (4-3). Reasons for this approach include upward

seepage that causes void ratio alteration and overvalued void ratios at shallower depths; initial

50

liquefaction of surface layers reducing transmission of induced shear stresses; and nonliquefied

layers creating potential arching effects (Cetin et al., 2009). Using Cetin et al. (2009), DFi is

calculated as:

1 018

ii

zDF = − ≥ (4-3)

where DFi is a function of the new strain, calculated as:

*i i iDFγ γ= ∗ (4-4)

This inverse weighting allows for layers situated deep to contribute less predicted lateral

spread than layers closer to the surface. Liquefied upper layers reduce the value and quantity of

shear stresses transmitted down to deeper layers. This Cetin et al. (2009) modification was

generated for settlement calculation and has been adapted for lateral spread in this study. It

similarly scales strain values. However, a depth of 10 m was proposed as a threshold depth,

under the recommendation of personal communication with Dr. Youd (2016). This altered

version of the depth modification is calculated as:

1 010

ii

zDF = − ≥ (4-5)

and is used in conjunction with Equation (4-4). This procedure applies to flat or gently sloping

ground.

Features defined by free face conditions value the depth of layers differently. As

suggested by Chu et al. (2006), layers below 2H (H defined as the height of the free face) exhibit

close to zero deformations. To follow this practice, CPTLiquefY caps strains at 2H below the top

of the free face, not allowing layers below that depth to contribute towards lateral spreads.

51

4.2.5 Fines Content Modification

Another modification that will be assessed in this study is the fines content (FC)

modification, which explores the lateral spread potential based on the amount of fines in a soil

layer. Loose, clean sands present higher risk to lateral spread, but the addition of fines changes

the soil structure and how a soil layer might respond to loading (Kramer, 1996). Additionally,

too many fines may cause the layer to not be susceptible to lateral spread. A closer examination

of the recorded case histories showed incorrect fines content input for four such cases, a

contributing factor in a skew of the regressed data (Youd T. L., 2018). With these erroneous case

histories removed, the remaining database did not contain any cases with fines greater than 60%

that exhibited lateral spread. Furthermore, only Alaskan case histories, unique as glacial deposits

with non-plastic rock flour, had FC greater than 50%. This observation suggests a 50% fines

content might generally be used for a cutoff between layers susceptible and not susceptible to

lateral spread. Sediment layers with FC greater than 50%, silty sands and finer, will not likely

contribute to lateral spread. To make this modification applicable for CPT data, an Ic cutoff of

2.4 is implemented (Boulanger & Idriss, 2014) which corresponds to FC = 50%, approximately.

4.2.6 Eliminate Thin Sand Modification

The thin layer modification provides a way to remediate for over-accounting for thinly

interbedded sand layers. Any layer that is deemed susceptible to liquefaction will contribute to

lateral spread, by current practice (Zhang et al., 2004). However, there are conditions where this

assumption may not be valid, specifically, thinly interbedded sand layers. It has also been

observed that when sand layers are too thin, they may not be sufficiently continuous and

potentially over-representing the potential amount of lateral spread displacement that might

occur (Bastin et al., 2017). An evaluation of the Zhang et al. (2004) database by Youd (2018)

52

determined that no single soil layer less than 0.6 meters in thickness likely contributed to lateral

spread across the site. Youd further proposed that applying the Zhang et al. (2004) method to

these thin sand layer would be considered extrapolation thus adding uncertainty to the analysis.

To account for the possibility that thinner layers contribute to lateral spreads (i.e., his own

uncertainty) Professor Youd recommends a limiting soil layer thickness of 0.3 m. This is due to

insufficient continuity in granular layers based on depositional behavior of alluvial, fluvial and

aeolian methods. This recommended modification/limitation does not include lacustrine,

lagoonal or sea bottom deposits, which will require case histories from lateral spreads with thin

sand layers to establish a lower limit of layer thickness (Youd, 2018). This thin sand layer

modification targets sand (Ic < 2.6) layers with a thickness < 0.3 m. With the appropriate layers

identified, CPTLiquefY alters their calculated strain value to zero when this modification option

is enabled to effectively treat those layers as not contributing to lateral spread.

Case Histories

Several case histories were selected to analyze the effectiveness of the proposed

modifications to establish credibility and applicability for further studies. Criteria used to select

case histories relied on available data, relevance to lateral spread, and advisement of Drs. Franke,

Youd, and Robertson. Case histories used in this study pose relevance to the Zhang et al. (2004)

model and case history database because some cases didn’t perform well under the conventional

model, exhibiting displacements of both over prediction and under prediction. Some of the case

histories used in this study were originally included in the Zhang database. From the

recommended case histories, soundings that included all relevant site information were included

in this analysis. Some soundings could not be included because of one or more pieces of site

information was not detailed with the rest of the data. For this reason, some case history events

53

only contain a few soundings. Identifiers of the specific logs used in this study are available in

the appendix.

4.3.1 Christchurch

This case history set is chosen for a few reasons. The damage was unprecedented in the

area and there were numerous instances of repeated liquefaction. The data specifically used in

this study has already been quantified and used to analyze different lateral spread displacement

models (Deterling, 2015). The Christchurch series of earthquakes from 2010 and 2011 generated

extensive liquefaction and lateral spread damage. The examination of the damage has produced a

large database of CPT-based soil investigations and boring logs, hosted on the New Zealand

Geotechnical Database (NZGD) (Earthquake Commission, n.d.). The NZGD is a shared platform

designed for efficiently accessing relevant geotechnical information (Earthquake Commission,

n.d.). Researchers continue to analyze and draw conclusions from this database to update and

verify predictive models, such as Deterling (2015). This thesis analyzed severely-impacted

sections of the Avon River, creating cross-sections that displayed different displacement

patterns. Key site characteristics and reported deformations made 42 CPT data logs accessible

for use in this study. Acceleration values were obtained by overlaying cross sections onto

acceleration contours by Bradley & Hughes (2012).

The February 22nd, 2011 Christchurch, New Zealand earthquake registered a moment

magnitude (Mw) of 6.2 and peak ground acceleration (PGA) ranging from 0.31 g to 0.52 g.

Earthquake effects included liquefaction induced lateral spread, large settlement, and sand ejecta.

The large lateral spreads resulted from an elevated ground water table, loose sand deposits and

proximity to the Avon River free-face (Quigley, Bastin, & Bradley, 2013). Profiles from this site

54

include a silty sand and silt mixture topsoil about 1 m thick. Beneath the topsoil is layers of silty

fine to medium sand/sandy silt. The ground slopes for these soundings are all free-face slopes

with values of W ranging from 0.001 to 4 with an average of 0.14 (Deterling, 2015). The specific

soundings and site data used in this study are available in the appendix.

4.3.2 Turkey

The 1999 Kocaeli, Turkey earthquake was included in this study because of locations

observed with zero-lateral spread displacement in zones where models might have otherwise

predicted significant deformations. These cases are significant because the modifications

presented in this paper might better predict the observed lateral spread displacement. Three

locations were used, Ҫark Canal, Cumhuriyet Avenue, and Degemender Nose Site, totaling 8

CPT soundings. DeDen (2004) provided the necessary site characteristics for analysis.

The Mw = 7.4 earthquake registered a range 0.2 g to 0.4 g PGA. The geology for the area

consists of an alluvial plain seated between two rivers that migrated and deposited mixed layers

of clay, silt, and sand. Older, deeper deposits consist of lake-bed sediments of sands, gravels,

silts, and clays (DeDen, 2004). Cark Canal site and Cumhuriyet Avenue are underlain by fine-

grained sediments and Degirmendere Nose is underlain by medium dense silty sand (Youd, et

al., 2009). The topography consists of W of .23, and S of 20% and 0.30%. Displacements

registered as zero for all these sites, except one, which was measured to be 87 cm of lateral

spread. The specific soundings and site data used in this study are available in the appendix.

55

4.3.3 Taiwan

The 1999 Chi-Chi Earthquake of Taiwan was a significant case study for lateral spread.

The extensive spreading was driven by the Mw = 7.6 event from the Chelungpu Fault, centered

on the island. As Chu et al. (2006) noted, the available models do not reasonably predict

displacements. They associate this error to the lack of Taiwan data present in the empirical

models, plastic fines in the soils, partial drainage, and other effects (Chu et al., 2006). While this

engineering judgement is both practical and reasonable, it is difficult to quantify. These cases

will provide a valuable range of displacements to help establish veracity of the modifications.

Chu et al. (2006) provides the necessary site characteristics and 16 soundings performed at

lateral spread sites.

Most of the area contains Holocene-age sands, silts, clays and gravels in a relatively flat

alluvial plane. There is also presence of alluvial sediments grade to silty sands with occasional

clayey sand interbeds at some of the sites (Chu et al., 2006). The predicted PGA was reported as

0.67 g at the location of all soundings, except for two soundings that had a reported PGA of 0.39

g. The two soundings with the lower PGA also rested on a ground slope S of 3.8%. The

remaining soundings had free face slopes W ranging from 0.04 to 0.72 with an average of 0.16

for that group. The specific soundings and site data used in this study are available in the

appendix.

4.3.4 Northridge

The 1994 Northridge, California earthquake caused $20 billion in economic loss. The

ground failure varied from extensive slope failures to minor ground cracking (Holzer et al.,

1999). Specific causes of ground failure were unclear to researchers (Cruikshank et al., 1996;

56

Johnson et al., 1996), who proposed opposing theories (Holzer et al., 1996; Stewart et al., 1996),

until detailed investigations were performed. Holzer et al. (1999) found that liquefaction in a

silty sand layer was the actual cause of failure in a previously-supposed case of cyclic softening

deformed clay. Holzer et al. (1999) show a wide range of CSR versus corrected cone tip

resistance, as seen in Figure 4-4, suggesting a scatter that does not adequately predict lateral

spread. 45 of these case histories were included to consider how well the modifications assist in

better predicting sandy silt profiles.

Figure 4-4: Liquefaction potential of four different Northridge study sites (a)-(d).

The Mw = 6.7 earthquake registered a range of PGA values of 0.43 g, 0.51 g, and 0.85 g.

All the included soundings were located on a ground slopes S ranging from 0.30% to 1.8% with

an average of 1.17%. Lateral spread displacement values varied from 10 cm to 50 cm, averaging

27 cm. The geology consisted of Holocene sediments of clay to silts, with some soundings

57

having lean clay to sand silt with lenses of silt and silty sand on the upper layers. Deeper deposits

contain Pleistocene silty sands (Holzer et al., 1999). The specific soundings and site data used in

this study are available in the appendix.

4.3.5 Imperial Valley

Although the same location as the famous 1940 Imperial Valley Earthquake, the 1979

Imperial Valley Earthquake gained unique notoriety as it did not provide adequate estimates of

lateral spread using conventional methods and is the reason why these sites were included in this

study. Explanations for displacement were tied to age of deposit and shaking duration (Bennett et

al., 1981). Although informative, these effects are not quantitative with current predictive models

and require extensive engineering judgement to understand and apply to design. Some of the

liquefaction that occurred on Heber Road was from a 5-m deep loose sand deposit. At River

Park, 3-m deep loose sands and silts, and 6-m deep dense sands, lead to sand boils from the

liquefied layers. Bennett et al. (1981) details the site characteristics and 15 soundings used in this

analysis.

The Mw = 6.6 earthquake registered PGA values of 0.2 g, 0.6 g, and 0.7 g. Five of the

soundings were located on a sloping ground S of 0.4% and the other soundings were located near

a free-face slope W of 0.44 and 0.06. Displacements registered as 0 cm for the sloping ground,

120 cm and 100 cm for the free-face slope sites. Geologic setting includes river deposits of fine-

grained sand over silty sand and sand. Deposited beneath are lake and river deposits of

alternative beds of silty clay and sand (Bennett et al., 1981). The specific soundings and site data

used in this study are available in the appendix.

58

4.3.6 San Fernando

The 1971 San Fernando Earthquake caused wide-spread damage throughout the San

Fernando Valley, California. One noteworthy lateral spread inflicted serious damage to the San

Fernando Valley Juvenile Hall. Extensive investigations and engineering judgement identified

liquefiable layers that drove a block of 1.5 m downward movement along the gently sloping

ground (Bennett, 1989). However, use of the Zhang et al. (2004) method alone does not

adequately predict the observed lateral spread movements, requiring careful engineering

judgement to filter out non-liquefiable layers. This case was included in this study because many

of the displacements were not adequately predicted with current models. A loose layer of sandy

silt and silty sand liquefied during this earthquake. Deeper sediments were deemed to be older,

and thus more resistant to liquefaction (Bennett, 1989). Site characteristic and four soundings are

presented in Bennet (1989).

The Mw = 6.4 recorded earthquake registered an assumed PGA of 0.5 g at the tested sites

(Joyner & Boore, 1981). The gently sloping ground of 0.80% generated 15 cm of lateral spread.

The upper layers consist of silty sand and medium-dense sandy silt above poorly sorted, silty

sand. Middle deposits consist of poorly sorted, loose sandy silt and silty sand. Lower deposits

consist of medium-dense poorly sorted silty sand and sandy silt. One of the soundings consist of

mainly poorly sorted stiff clayey silt with interbeds of sandy silt (Bennett, 1989). The sediments

were deposited in an alluvial-fan environment. The specific soundings and site data used in this

study are available in the appendix.

59

4.3.7 Loma Prieta

The 1989 Loma Prieta Earthquake brought to light the effects earthquakes can have on

transportation structures with the collapse of the double decker Cypress Freeway in Oakland,

California. Local soil conditions amplified ground motions and concentrated heavy damage to

specific sites. Some of the soil deposits amplified shaking 2 to 3 times (Seed, Dickenson, &

Idriss, 1991). A large lateral spread occurred at Moss Landing and is documented by Boulanger

et al. (1997). A wide variety of liquefaction-induced damage was reported at the site of this

earthquake. Some of the soundings used in this study experienced no displacement, while others

did. This displacement disparity was a reason for including this case in this study. The six

soundings used for this analysis are supplied by the Boulanger et al. (1997).

The earthquake area contains Holocene deposits with thick sands offshore and estuarine

and fluvial deposits onshore. Subsurface soil conditions include 8- to 12-m-thick of poorly

graded sand with interlayers of gravelly sand and sandy gravel. Occasional clayey silt layers are

present throughout (Boulanger et al., 1997). The Mw=7.0 event produced an estimated PGA of

0.25 g. The free-face ground slopes had values of W from 0.14 to 0.37, with an average of 0.21.

The soil at the site experienced a range of lateral spread displacements from 0 to 28 cm. The

specific soundings and site data used in this study are available in the appendix.

Example Problem

To demonstrate these modification methods, an example problem is presented. An

exploration of the soil profile with no modifications, individual modifications, and the

application of all modifications at once highlight the intended effects of these modifications. The

reference profile is one from the 1999 Chi-Chi Taiwan earthquake. This earthquake displays

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many cases that had been previously unrepresented in the lateral spread history database,

including those of high CSR and relatively many marginally-plastic, fine soils (Chu et al., 2004).

The case examined here is one that severely over-predicts displacements using the base Zhang et

al. (2004) method: a calculated 169 cm compared to an observed lateral spread displacement of 0

cm. This site, labeled WCC-11, experienced a Mw = 7.6 and a PGA of 0.67, relatively high

values for a site that did not develop lateral spread. Site conditions position the boring 53.5 m (L)

away from a 2.8 m (H) tall free-face slope. The water table was measured at 1.33 m below the

ground surface. A generalized stratification is colored below in Figure 4-5, giving cone tip

resistance values, qc, soil behavior type, Ic, and layered soil types.

Figure 4-5: Example problem soil profile, WCC-11, after Chu et al., (2004).

CL

ML

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For reference, the same qc values are indicated in brown and the height of the water table

is indicated with the dashed line across all figures for this example problem. Figure 4-6 (a)-(g)

shows the lateral spread displacement (LD, cm) cumulative, by depth, from the bottom of the

profile to the surface.

4.4.1 No Modifications

First, it is necessary to examine the lateral spread displacement by depth with no

modifications applied to the conventional model. The Robertson and Wride (1998) with the

Robertson (2009) update is used as the triggering model in conjunction with the Zhang (2004)

lateral spread displacement predictive model. The dotted line in Figure 4-6 (a) shows a

cumulative lateral spread predicted displacement traverses up the soil profile. This unmodified

model shows sharp increases at the interbedded clayey sand layers, 10-15m, and close to the

water table. It also displays small incremental displacements throughout, which add up to the

surface total of 169 cm of predicted lateral spread displacement. The unmodified model is

displayed as the dotted line on each of the other models, for reference.

4.4.2 Soil Transition Zone Modification

The soil transition zone modification, or just transition modification, as presented in

Figure 4-6 (b), begins to diverge at the thinly interbedded layers, but has little effect on the

cumulative lateral spread, predicting165 cm of surface lateral spread displacement. The small

difference of only 4 cm from the unmodified model is because there are a limited number of soil

transitions from soft to hard layers.

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(a) No Modifications

(b) Transition Zone

(c) Thin Sand

(d) Dil/Cont

Figure 4-6: Predicted lateral spread displacements by modifications.

0 20 40

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(e) Depth

(f) Fines Content

(g) Elim. Thin Sand

(h) All Modifications

Figure 4-6: Continued

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4.4.3 Thin Sand Layer Modification

The thin sand layer modification is meant to capture sand layers 0.3 m or smaller. In this

example, it is capturing the value of several smaller layers in the clay below 15 m, as well as the

silty sand at 15m. Among these and other layers, the total displacement is reduced to 23 cm,

indicating that over half of the displacement derived from thin layers, as shown in Figure 4-6 (c).

4.4.4 Dilative/Contractive Modification

The dilative/contractive modification identifies layers that might not conventionally be

classified as dilative, or not prone to accruing large strains from undrained loading and filters

them out based off a corrected cone tip resistance value, 𝑄𝑄𝑡𝑡𝑡𝑡,𝑐𝑐𝑐𝑐. This modification captures a few

intervals, most prominent at depths from 1.6 to 0.95 m, one of the locations where the

unmodified method calculates maximum strain. As seen in Figure 4-6 (d), there are also a few

locations along the profile that meet this criteria, leading to a decrease in overall calculated

displacement, now 137 cm.

4.4.5 Soil Depth Modification

The depth modification, with a free-face scenario, as described above, eliminates lateral

spread for layers below 2H. At this site, with H = 3.5 m, 2H = 7 m. Figure 4-6 (e) shows this

phenomenon, as it doesn’t register any displacements at depths greater than 7m. Above that

depth, it is a simple translation of the no modification model, matching incremental

displacements. The cumulative surface lateral spread displacement with the soil depth

modification is 31 cm.

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4.4.6 Fines Content Modification

The fines content modification searches for layers that might have too many fines to

spread laterally and removes these layers. As seen in Figure 4-6 (f), this modification seems to

affect the interbedded sandy clay layers from 5 to 15 m the most, suggesting that a few of these

intervals may be too clay-like to spread laterally. The cumulative surface lateral spread

displacement with the fines content modification is 93 cm.

4.4.7 Eliminate Thin Sand Modification

The eliminate thin sand modification targets sand layers that are potentially too thin (<0.3

m) to contribute to lateral spread. This modification removes much of the predicted displacement

from sands interbedded in 20-25 m as well as from 5-15 m. The eliminate thin sand modification

reduces overall predicted displacement to 95 cm, as seen in Figure 4-6 (g).

4.4.8 All Modifications

When combining these modifications together simultaneously, all predicted displacement

but a few centimeters has been removed. Modifications used in parallel target different weak

points in the predictive analysis and a drastic over-prediction of 169 cm is reduced to 1.3 cm,

which is much closer to the observed displacement of 0 cm. For reference, a combined graphic of

each of the above displacement charts is presented below in Figure 4-7.

In this example, applying all the modifications generated a predicted lateral spread

displacement that more closely aligned to the measured displacement than the unmodified

method. However, not all case histories perform like the above example, neither in accuracy nor

in equal mete of modification. Before these modifications can be implemented with confidence

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in forward design, they must be evaluated against a range of case histories to ensure the universal

prediction accuracy of the Zhang et al. (2004) model.

Figure 4-7: Predicted displacement with all individual and total modifications.

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5 EVALUATION OF MODIFICATIONS TO CPT-BASED LATERAL SPREAD

PREDICTION PROCEDURE

Results

Now that a proper foundation of liquefaction, lateral spread, the proposed modifications,

and the prediction methodology has been presented, the results of the calculations are discussed

in this section. The purpose of the results is to allow for a visual representation of the overall

data, to determine the effectiveness of the modifications, and to provide recommendations for

application of the modifications. More specifically, the following questions are investigated.

Does the consistent application of these modifications to non-clean (thin/transition/clayey) soil

profiles consistently reduce predicted displacements and consistently improve accuracy? Does

the implementation of modifications to cases of soils where existing methodology perform

adequate predictions significantly decrease predicted displacements resulting in potentially

dangerous under prediction?

The results will be discussed in the light of several different analysis methods. The

regression analysis and distribution charts help establish the effectiveness of the modifications.

The discriminant analysis seeks to provide indicators for when to use the modifications. The

statistical program JMP was used to analyze and visualize the data and analyses. The raw data

values are tabulated in the appendix.

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It is important to note that due to the large amount of non-clean soil profiles, as described

in Chapter 4, Zhang et al. (2004) does not perform as well with this set of case histories. Zhang

et al. (2004) states, “generally, about 90% of the calculated lateral displacements using the

proposed approach showed variations between 50 and 200% of measured values for the case

histories studied.” Comparatively, only 24% (33 out of 136) of the cases used in this study

predict within similar bounds, without modifications. Without specific industry standards to

establish defined amounts of non-clean (thin/transition/clayey) properties, case histories that over

predict without modification are treated as non-clean. The discriminant analysis later in this

chapter presents indicators of properties that tend over predict, under predict or predict within

reason.

5.1.1 Regression Analysis

The data presented in this section is a bivariate analysis to determine how effective the

predicted values match the measured values of lateral spread displacement. This method will

visualize and quantify comparisons from the unmodified model to the applied modifications.

Each bivariate modification compares the measured, or actual, displacement, as represented on

the y-axis. The calculated, or predicted, displacement is shown on the x-axis. To analyze the

data, soundings are separated between sites with a gently sloping ground, denoted with a, “S,”

and sites with a free-face slope, denoted with a, “W.” This separation of data is useful because

different equations transform LDI into lateral spread and the mechanisms that will contribute

towards the amount of lateral spread differ between sloping ground (S) and free face (W)

conditions.

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Figure 5-1: No Modifications, Free Face

Figure 5-2: Transition Zone Modification, Free Face

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Figure 5-3: Depth Modification, Free Face

Figure 5-4: Thin Sand Modification, Free Face

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Figure 5-5: Dilative/Contractive Modification, Free Face

Figure 5-6: Fines Content Modification, Free Face

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Figure 5-7: Eliminate Thin Sand Modification, Free Face

Figure 5-8: All Modifications, Free Face

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Figure 5-9: No Modifications, Sloping Ground

Figure 5-10: Transition Zone Modification, Sloping Ground

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Figure 5-11: Depth Modification, Sloping Ground

Figure 5-12: Thin Sand Modification, Sloping Ground

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Figure 5-13: Dilative/Contractive Modification, Sloping Ground

Figure 5-14: Fines Content Modification, Sloping Ground

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Figure 5-15: Eliminate Thin Sand Modification, Sloping Ground

Figure 5-16: All Modifications, Sloping Ground

To visualize the spread and accuracy of the data, simple boundaries are established, similar

to industry convention. Zhang et al. (2004) describes the amount of calculated lateral spread

within, “50 to 200% of measured values.” Using these ranges as a guideline, the following plots

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display data relative to the 1:1, 2:1, and 0.5:1 lines. These boundary lines help characterize

accuracy of the unmodified and modified predictions. The plots present linear scales up to 250

cm to make the majority of the data more easily viewable. An arrow is used to indicate where

plots have some data off scale. Table 5-1 -- Table 5-3 quantifies the change to the number of data

points that fall in the different ranges with each modification and the all modifications model.

The changes in prediction value assess how the modifications affect the accuracy of the

predictions.

Figure 5-1 -- Figure 5-16 visualize the measured displacement versus predicted

displacement, by ground condition: ground slope (S) or free-face (W). Many individual

soundings improve predictions with the modifications. However, there were also many

soundings where modifications do not improve prediction enough and remains an over

prediction. There were also many soundings where modifications lead to under prediction. To

numerically describe the how the modifications affect the data, Table 5-1, Table 5-2, and Table

5-3 present the amount of data within accuracy categories by modification.

First, accuracy categories were created, using boundaries similar to industry convention.

Certain soundings reduce small predictions minimally, still registering as an under-prediction,

but not extremely variant from the measured displacement. Other soundings are drastically

reduced from extreme predictions to, again, not extremely variant from the measured

displacement. A simple difference of modified results to the measured displacement would

adequately capture small (i.e. <15 cm) prediction improvements as reasonable but curtail

improvements of a larger scale. A simple scalar comparison would identify notable

improvements for all but small numbers, where the prediction may be as different as 10 cm, but

still beyond a factor of two (or half) from the observed displacements. To adequately identify

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modified soundings as reasonable predictions, both of the above principles are applied. If the

prediction is: 1) within +/-15 cm or 2) within a factor of two of the measured displacement, it is

classified as “Within Reason” (personal communication, Youd and Robertson, 2018). Values

that fall outside that range are classified as “Overpredict” or “Underpredict,” respectively.

Table 5-1 shows how applying different modifications affects prediction accuracy.

Generally, when applying singular or all modifications, the amount of over predictions

decreases, the amount of within reason increases, and the amount of under predictions increases.

Table 5-2 shows the amount of decrease or increase by accuracy category, reinforcing

observations just mentioned.

To go one step further and make observations about how the modifications affect the

accuracy of the predictions for individual accuracy categories, Table 5-3 is provided. This table

separates the data by the starting (no modifications) and ending (after applying modifications)

accuracy categories. This will help highlight how the modifications affect specific accuracy

categories, as the combined results only provides generalized results. It should be noted, due to

the nature of the modifications to only reduce predictions, there is an inherent proclivity to

descend in categories from over predicting to within reason or under predicting, and not the

inverse. Consequently, there are no samples that go from within reason to over predict, from

under predict to over predict, or under predict to within reason. There is also no change in the

amount of samples that start and end in under predict because these modifications only reduce

predictions and under predict is the lowest category. A few observations from Table 5-3 can be

made.

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Table 5-1: Count of Soundings by Modification and Prediction Accuracy

Table 5-2: Change in Count of Soundings by Modification and Prediction Accuracy

f g y f p y

Accuracy Category

No Modifications

Transition Modification

Depth Modification

Thin Sand Layer

Modification

Dilative/ Contractive Modification

Fines Content

Modification

Elim. Thin Sand Layer Modification

All Modifications

Overpredict 73 72 46 68 64 62 68 17Within Reason 33 30 42 32 34 35 34 56Underpredict 30 34 48 36 38 39 34 63Total 136

g f g y f p y

Accuracy Category


Depth Modification

Thin Sand Layer

Modification


Fines Content

Modification


All Modifications

Overpredict -1 -27 -5 -9 -11 -5 -56Within Reason -3 9 -1 1 2 1 23Underpredict 4 18 6 8 9 4 33Note . Negative denotes decrease, positive denotes increase

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Table 5-3: Count of Data in Each Final Accuracy Category Based on Starting (Unmodified) Accuracy Category

f f y g y g ( f ) y g y

Starting Category

Ending Category


Depth Modification

Thin Sand Layer

Modification


Fines Content

Modification


All Modifications

Overpredict Overpredict 72 46 68 64 62 68 17Overpredict Within Reason 1 26 5 9 11 5 47Overpredict Underpredict 0 1 0 0 0 0 9

Within Reason Within Reason 29 16 27 25 24 29 9Within Reason Underpredict 4 17 6 8 9 4 24Underpredict Underpredict 30 30 30 30 30 30 30

Note. 73 start in Overpredict, 33 start in Within Reason, 30 start in Underpredict.

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The first three rows divide the 73 samples that start in over predict into ending categories

of over predict, within reason, and under predict, based on the modifications applied. The first

row lists the count of soundings that start in over predict and end in over predict. The application

of singular modifications does not drastically reduce this count from 73, aside from the depth

modification. However, applying all modifications does drastically reduce the amount of over

predicting samples from 73 to a mere 17. The second row lists the count of soundings that start

in over predict and end in within reason. The application of singular modifications does not

drastically increase the amount of within reason. However, applying all modifications does place

47 out of the 73 to be within reason. The third row lists the count of soundings that start in over

predict and end in under predict. Applying a singular modification does not increase the amount

of under-predicting samples. However, applying all modifications does generate some under

predictions, 9 out of the 73.

The next two rows divide the 33 samples that start in within reason into ending categories

of within reason and under predict, based on the modifications applied. The first of these rows

lists the count of soundings that start in within reason and end in within reason. The application

of a singular modification doesn’t drastically reduce the count, aside from the depth

modification. However, applying all modifications does drastically reduce the amount of within

reason samples from 33 to 9. The following row lists the count of soundings that start in within

reason and end in under predict. Applying a singular modification only slightly increases the

amount of under-predicting samples, with the exception of the depth modification which

increases greatly. However, applying all modifications greatly increases the amount of under-

predicting samples, 24 of the original 33.

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There is also the consideration of applying different combinations of modifications.

Without generating all 64 possible combinations, it’s impossible to know the ideal blend of

modifications. Even so, it would only provide an optimum solution for the collection of case

histories used in this study. However, a simpler bypass would be to treat all the modifications as

independent and assess the change in accuracy category for a few combinations. The Depth

Modification is a viable candidate to be considered with another modification because of the

amount of case histories it reduces from over predicting to within reason, 73 to 46. Two other

possible secondary options would be the Dilative/Contractive Modification and the Fines

Content Modification, as they appear to reduce the least amount of predictions that end in

underpredicting, while still increasing the amount of within reason. This approach identifies case

histories in the relevant accuracy category and adds them together, removing possible duplicates.

Duplicates are identified by looking at which case histories are already in the target accuracy

category for both modifications. Two scenarios will be considered: 1) Depth Modification (26

over predict to within reason, 17 within reason to under predict) with Dilative Contractive

Modification (9 over predict to within reason, 8 within reason to under predict) and 2) Depth

Modification with Fines Content Modification (11 over predict to within reason, 9 within reason

to under predict). In the both scenarios, there were equal amounts of common case histories that

went from over prediction to within reason and common case histories that went from within

reason to under prediction. The added benefit of considering another case history would be a

higher amount of cases that are predicted to be within reason, however, this comes at the cost of

also including cases that under predict.

The above observations suggest a few principles. The consistent application of these

modifications to non-clean soil profiles consistently reduces predicted displacements and

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consistently improves accuracy. These observations also suggest that the implementation of all

modifications to cases of soils where existing methodology performs adequately, significantly

decreases predicted displacements, resulting in potential under prediction. Generally, the

reduction in over prediction comes at the risk of slightly under predicting displacements.

5.1.2 Distribution Charts

The purpose of distribution charts in this study is to assess the accuracy of the data in

reference to the modifications. To quantify accuracy, this analysis describes the difference

between the predicted displacement of the unmodified model and the all modifications applied

model, both relative to the measured displacements. These comparisons establish how different

the model predictions are from the measured field displacements. Using this difference

comparison simplifies the analysis of the modification effectiveness. These distribution charts, or

histograms, visualize the overall trend, and the accompanying box and whisker plots describe the

spread and distribution of the data. The mean is also presented and is a useful indicator for

effectiveness of applied modifications on the overall data, as it represents the center of

distribution. When placed side-by-side, the changes of prediction accuracy by applying all the

modifications become apparent.

The graphics and statistical summary of the distribution charts are presented in Figure 5-17

and Figure 5-18, below. Both parameters are subtracted by the measured displacement, and

labels are condensed on the graph to: “No-dis” (no modifications - displacement) and “All-dis”

(all modifications - displacement). Because the presented values are differences or errors, values

larger than zero indicate over prediction, numbers less than zero indicate under prediction. The

value of bringing the data close to zero establishes a prediction accuracy, where the closer to

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zero, the more accurate the prediction. This metric is valuable for assessing accuracy for

individual and collective results.

Figure 5-17 and Figure 5-18 allow us to see how different the prediction models compare

to each other in reference to the measured displacement. In both cases, with the free-face and the

ground slope sites, the trends of greatly over-predicted estimates are consistently reduced.

Several clues indicate this pattern. First, the skewed distribution, influenced by over-predicted

data points, becomes more normalized with the application of the modifications as the over

predictions are reduced. Second, the mean value decreases from 140 cm and 182 cm to -9 cm

and -7 cm, respectively. This decrease shows an application of all the modifications presents a

more accurate prediction model, as the all modifications model is significantly closer to zero.

Finally, the box and whisker plots, with a tighter range centered closer to zero, indicate more

accurate predictions for this specific set of case histories. These are strong indicators that

consistent application of the proposed modifications nearly eliminates the potential for gross

over prediction of lateral spread displacements and consistently improves accuracy. This is also

an indicator for the increased potential of slightly under-predicting the displacements.

There are a few possible reasons why this data shows the above-mentioned trends of

reducing over prediction and slightly increasing under prediction. First, the case histories used in

this study contain many non-clean soundings with properties of thin interbedded, transition, or

clayey sand layers. These type of soundings have shown difficulty to accurately predict

displacements (Seed, 1987; Ahmandi & Robertson, 2005; Youd, 2018). These non-clean case

histories represent a large sample of the data used and overall results will reflect a bias in change

in accuracy towards the non-clean soil profiles.

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Figure 5-17: Distribution Plots, Free-Face (W)

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Figure 5-18: Distribution Plots, Sloping Ground (S)

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Another possible reason for the vastly over-predicted values is model extrapolation. For

example, sample 1108 in Christchurch has a free-face W of 4, which is well outside the

recommended bounds of 0.25 to 1. Engineers should interpret extrapolated results with caution,

and application of these proposed modifications does not eliminate or even reduce the risks

associated with extrapolation.

5.1.3 Discriminant Analysis

To further analyze the data, a discriminant analysis was performed to identify

characteristics that might contribute to over-prediction, reasonable prediction, or under-

prediction. A discriminant analysis is used to assess the adequacy of a classification and/or how

effective a certain set of variables are in predicting a category membership. A discriminant

analysis creates canonical variables based on a linear combination of the covariates. Canonical

variables define the two dimensions that provide the maximum separation among the groups.

The plot shows how each covariate contributes to a conical variable. Covariates are standardized

with a mean of 0 and a standard deviation of 1. Ellipses are created to show a 95% confidence

for the categorical variable. Intersecting confidence ellipses indicate an insignificant difference.

The rays denote the covariates, where the length and direction represent the degree of association

to the covariates.

To perform the discriminant analysis, categorical variables and covariates are first defined.

An a priori discrimination is applied to each of the soundings to determine the categorical

variable. The same metric used to classify accuracy categories in section 5.1.1, Regression

Analysis, is used here. If samples are 1) within +/-15 cm or 2) within a factor of two of the

measured displacement, it is classified as “Within Reason.” Values that fall outside that range are

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classified as “Overpredicted” or “Underpredicted,” respectively. The continuous covariates

available for the deterministic analysis include the PGA, earthquake moment magnitude, and

depth to the water table. The results of the discriminant analysis are presented in Figure 5-19 and

Figure 5-20.

From the discriminant analysis, a few observations and conclusions can be made. First, 25

out of the 74 points corresponding to free-face W sites are misclassified (33%) and only four

within-range soundings are predicted. The discriminant analysis performed accurately and

classifies over half of the free-face sites, but still has a significant amount of uncertainty. The

overlapping ellipses is an indicator that the covariates used would not necessarily distinguish a

sounding as within reason from tending to over-predict or under-predict. However, looking at the

ground slope S data, only 13 out of 62 points, 21%, were misclassified, which is a somewhat

stronger model, but still contains significant uncertainty. For the free-face sites, PGA values vary

and are not within a clean range, although lower values do have some tendency to indicate

under-prediction. Higher PGA values (0.85) are more likely to under-predict with ground slope

sites. High magnitude values (>7.0) tends to be an indicator for over-predicting both free-face

and ground slope sites. Water table depth does not strongly correlate to one specific

classification prediction.

A weakness from interpreting the discriminant data with only the above-listed covariates is

that the modifications discussed in this paper only affect the soil layers and are independent of

the covariates. To perform a more thorough discriminant analysis, more covariates that describe

the soil layers and their properties would be necessary. However, due to the nature of a

discriminant analysis, the soil profile would need to be interpreted and condensed to single-value

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variables, much like T15 or D5015 used in SPT-based lateral spread analysis, which is beyond the

scope of this study.

Figure 5-19: Discriminant Analysis, Free Face

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Figure 5-20: Discriminant Analysis, Sloping Ground

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5.1.4 Results summary

This chapter presents an analysis of the data and sought to answer two main questions. The

first question explores whether the consistent application of these modifications to non-clean

(thin/transition/clayey) soil profiles consistently reduces predicted displacements and

consistently improve accuracy. The regression analysis reveals that a consistent application of

the modifications to non-clean soil profiles does consistently reduce predicted displacements and

consistently improve accuracy. This was evidenced by the decrease in the amount of over

predicting samples and a slight increase in the amount of samples within reason. The distribution

charts also reinforced this idea with the drastic decrease in over predictions. Applying all

modifications shows to be most effective on over-predicting, or non-clean samples. The second

question explores whether the implementation of modifications to cases of soils where existing

methodology perform adequate predictions significantly decrease predicted displacements

resulting in potentially dangerous under prediction. Table 5-3 shows the risk of potentially

dangerous under prediction when all modifications are applied, but there is significantly less of a

risk when only singular modifications are applied. The distribution charts also show the increase

in under prediction with all modifications. The modifications definitely eliminate the potential

for over prediction in interbedded soils, but at the potential risk of under prediction. The

discriminant analysis illustrates that high magnitude values tend to be an indicator for over

prediction and high PGA values tend to be an indicator for over prediction for free-face sites.

It is also useful to note a reduction in prediction from 66 cm to 38 cm, as in Christchurch

15600 with all modifications, may not represent a large shift in displacement because both are

generally considered large displacements. It might be a more concerning possibility if the

modifications predicted no displacements, but more than 15 cm actually occurred. There were

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approximately 40 sites where applying all modifications reduced the predicted displacement to

below 5 cm, but measured displacement was greater than 15 cm. A majority of those cases were

from one earthquake, Northridge, and a trend isolated to a singular event may can be an

indication of an outlier. The number of sites where the modification reduced displacement

prediction to near zero where the measured displacement was greater than 15 cm drops down to

only 17 sites when considering predicted displacements reduced to below 1 cm. Again, a

majority of those sites were from one case history, Northridge. This risk is further reduced when

considering the application of only singular modifications. All the modifications except the depth

modification lead to few (around or below) 10 scenarios where the predicted displacement was

reduced to below 5 cm and the measured displacement was recorded greater than 15 cm.

Engineers can refer on the discriminant analysis to provide indicators for scenarios like an under

prediction or over prediction. Indicators such as a high PGA as an indicator for gently sloping

ground as tending to under predict. Or a high magnitude as an indicator for over prediction in

free face slope sites.

In summary, application of these modifications reduce over-predictions from strain-based

prediction methods. However, blind or universal application could result in risk of under

prediction. Further research is necessary to determine how to apply these modifications to which

soil conditions. It is also recommended for further research to re-regress a prediction model after

applying these modifications to case histories.

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6 CONCLUSIONS

Conclusions

This study set out to examine the effectiveness and reliability of six modifications to the

Zhang et al. (2004) CPT-based lateral spread prediction model. These six modifications

included: 1) Soil Transition Zone Modification, 2) Thin Sand Layer Modification, 3)

Dilative/Contractive Modification, 4) Soil Depth Modification, 5) Fines Content Modification,

and 6) Eliminate Thin Sand Modification. Two main questions were considered to evaluate the

modifications. Does the consistent application of these modifications to non-clean

(thin/transition/clayey) soil profiles consistently reduce predicted displacements and consistently

improve accuracy? Does the implementation of modifications to cases of soils where existing

methodology perform adequate predictions significantly decrease predicted displacements

resulting in potentially dangerous under prediction? Several analyses were performed to answer

these questions, including a regression analysis, distribution charts, and a discriminant analysis.

The results showed modifications definitely eliminate the potential for over prediction in

interbedded soils, but at the potential risk of under prediction. Low PGA values are more likely

to under-predict with free-face sites. High PGA values are more likely to under-predict with

gently sloping sites. High moment magnitude values tend to be an indicator for over-predicting

both free-face and gently sloping sites.

94

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APPENDIX A SITE DATA AND LATERAL SPREAD DISPLACEMENTS

Table A-1: Site Input Data by Sounding

Event Sounding H (m) L (m) W S (%) WT

Depth (m)

Mw PGA (g)

Christchurch 64 3.5 123 0.03 2.1 6.2 0.36 Christchurch 71 3.5 223 0.02 2 6.2 0.36 Christchurch 168 3 174 0.02 1.15 6.2 0.49 Christchurch 325 3 23 0.13 1.76 6.2 0.34 Christchurch 1108 4 1 4.00 1.04 6.2 0.4 Christchurch 1420 3 443 0.01 0.9 6.2 0.36 Christchurch 1422 3 16 0.19 0.9 6.2 0.31 Christchurch 1425 3 98 0.03 1.26 6.2 0.45 Christchurch 2153 3 749 0.00 1.96 6.2 0.38 Christchurch 2161 3 1357 0.00 2.38 6.2 0.52 Christchurch 2242 3 774 0.00 2.14 6.2 0.39 Christchurch 2319 3 1058 0.00 1.72 6.2 0.52 Christchurch 2333 3 994 0.00 1.63 6.2 0.51 Christchurch 3924 3 1693 0.00 2.67 6.2 0.49 Christchurch 4643 3 445 0.01 1.91 6.2 0.46 Christchurch 4985 3 937 0.00 1.16 6.2 0.34 Christchurch 5252 4.5 71 0.06 2.57 6.2 0.35 Christchurch 6382 4 246 0.02 2.01 6.2 0.47 Christchurch 9712 3 855 0.00 1.33 6.2 0.49 Christchurch 11033 3 617 0.00 1.67 6.2 0.47 Christchurch 12268 3 228 0.01 1.59 6.2 0.5 Christchurch 13716 4 426 0.01 2.01 6.2 0.36 Christchurch 15287 4 210 0.02 2.17 6.2 0.46 Christchurch 15599 4 131 0.03 1.91 6.2 0.36 Christchurch 15600 3.5 50 0.07 1.59 6.2 0.31 Christchurch 15632 3 252 0.01 1.53 6.2 0.49 Christchurch 15641 3 36 0.08 1.21 6.2 0.49 Christchurch 15682 3 130 0.02 1.82 6.2 0.34 Christchurch 15703 4 203 0.02 1.47 6.2 0.4

103

Table A-1 Continued


Depth (m)

Mw PGA (g)

Christchurch 15772 3 35 0.09 1.43 6.2 0.5 Christchurch 15776 3 132 0.02 0.87 6.2 0.33 Christchurch 19088 3.5 273 0.01 1.85 6.2 0.33 Christchurch 21509 4 50 0.08 1.85 6.2 0.4 Christchurch 21510 4 27 0.15 1.77 6.2 0.4 Christchurch 26641 4 315 0.01 2.11 6.2 0.4 Christchurch 27046 3 1548 0.00 2.47 6.2 0.51 Christchurch 29053 4 46 0.09 1.82 6.2 0.36 Christchurch 29058 4 71 0.06 1.97 6.2 0.36 Christchurch 34460 4 95 0.04 2.08 6.2 0.4 Christchurch 34616 3 713 0.00 3.23 6.2 0.34 Christchurch 38115 3 289 0.01 1.83 6.2 0.45 Christchurch 38121 3 351 0.01 1.9 6.2 0.34

Imperial Valley 442 1.56 26 0.06 1.8 6.6 0.7

Imperial Valley 700-lsu006 1.56 27.67 0.06 1.8 6.6 0.7

Imperial Valley Heber 1 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 2 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 3 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 4 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 5 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 6 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 7 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley Heber 8 2 4.5 0.44 1.75 6.6 0.6 Imperial Valley pqs1 0.40% 0 6.6 0.2 Imperial Valley pqs2 0.40% 0 6.6 0.2 Imperial Valley pqs3 0.40% 0 6.6 0.2 Imperial Valley pqs4 0.40% 0 6.6 0.2 Imperial Valley pqs5 0.40% 0 6.6 0.2

Loma Prieta UC-18 3.7 26 0.14 3.4 7 0.25 Loma Prieta UC-2 5.4 28.3 0.19 1.7 7 0.25 Loma Prieta UC-3 5.5 14.7 0.37 1.7 7 0.25 Loma Prieta UC-4 5.3 30.5 0.18 1.8 7 0.25 Loma Prieta UC-5 5.2 18.7 0.28 1.8 7 0.25 Loma Prieta UC-6 5.1 35.6 0.14 1.7 7 0.25 Northridge BAL-1 0.70% 10.7 6.7 0.85 Northridge BAL-10 0.70% 7.3 6.7 0.85 Northridge BAL-11 0.70% 7.8 6.7 0.85 Northridge BAL-12 0.70% 7.7 6.7 0.85

104

Table A-1 Continued


Depth (m)

Mw PGA (g)

Northridge BAL-13 0.70% 8.2 6.7 0.85 Northridge BAL-13.5 0.70% 8.4 6.7 0.85 Northridge BAL-15 0.70% 10.7 6.7 0.85 Northridge BAL-16 0.70% 17.1 6.7 0.85 Northridge BAL-2 0.70% 9.45 6.7 0.85 Northridge BAL-3 0.70% 9 6.7 0.85 Northridge BAL-4 0.70% 8.7 6.7 0.85 Northridge BAL-5 0.70% 8.4 6.7 0.85 Northridge BAL-6 0.70% 8.3 6.7 0.85 Northridge BAL-7 0.70% 8.2 6.7 0.85 Northridge BAL-8 0.70% 8.1 6.7 0.85 Northridge BAL-9 0.70% 7.7 6.7 0.85 Northridge MAL-11 0.70% 3.9 6.7 0.51 Northridge MAL-12 0.70% 3.9 6.7 0.51 Northridge MAL-13 0.70% 3.9 6.7 0.51 Northridge MAL-3 0.70% 3.9 6.7 0.51 Northridge MAL-4 0.70% 3.9 6.7 0.51 Northridge MAL-5 0.70% 3.9 6.7 0.51 Northridge POT-1 2.70% 5.6 6.7 0.43 Northridge POT-10 2.70% 2.6 6.7 0.43 Northridge POT-11 2.70% 2.2 6.7 0.43 Northridge POT-12 2.70% 2 6.7 0.43 Northridge POT-3 0.70% 3.2 6.7 0.43 Northridge POT-4 0.70% 2.7 6.7 0.43 Northridge POT-5 0.70% 2.9 6.7 0.43 Northridge POT-6 0.70% 2.8 6.7 0.43 Northridge POT-7 0.70% 2.5 6.7 0.43 Northridge POT-8 2.70% 3.3 6.7 0.43 Northridge POT-9 2.70% 2.6 6.7 0.43 Northridge WYN-1 1.80% 4.8 6.7 0.51 Northridge WYN-10 1.80% 4.2 6.7 0.51 Northridge WYN-11 1.80% 4.3 6.7 0.51 Northridge WYN-12 0.30% 3.9 6.7 0.51 Northridge WYN-13 0.30% 4.3 6.7 0.51 Northridge WYN-14 1.50% 6.7 6.7 0.51 Northridge WYN-2 1.80% 4.4 6.7 0.51 Northridge WYN-3 1.80% 4.4 6.7 0.51 Northridge WYN-4 1.80% 4.3 6.7 0.51

105

Table A-1 Continued


Depth (m)

Mw PGA (g)

Northridge WYN-5 1.80% 4.3 6.7 0.51 Northridge WYN-7 1.80% 3.8 6.7 0.51 Northridge WYN-8 1.10% 4 6.7 0.51

San Fernando sfvjh81-2 0.80% 3.3 6.4 0.5 San Fernando sfvjh81-4 0.80% 3.3 6.4 0.5 San Fernando sfvjh81-6 0.80% 3.3 6.4 0.5 San Fernando sfvjh9 0.80% 3.3 6.4 0.5

Taiwan MAA-C-9 3 24.5 0.12 1 7.6 0.67 Taiwan NCC-2 3.80% 5.3 7.6 0.39 Taiwan NCC-3 3.80% 1.5 7.6 0.39 Taiwan RESI-C7 2.9 4 0.73 1.245 7.6 0.67 Taiwan WBC-1 2.9 46 0.06 1.122 7.6 0.67 Taiwan WBC-4 2.9 15.3 0.19 1.288 7.6 0.67 Taiwan WCC-1 2.9 11.8 0.25 1.245 7.6 0.67 Taiwan WCC-11 2.8 53.5 0.05 1.33 7.6 0.67 Taiwan WCC-12 3 18.5 0.08 0.75 7.6 0.67 Taiwan WCC-13 2.9 58.4 0.04 0.9 7.6 0.67 Taiwan WCC-2 2.9 23.8 0.12 1.254 7.6 0.67 Taiwan WCC-4 2.8 15 0.19 1.24 7.6 0.67 Taiwan WCC-6 3.5 26.5 0.13 1.11 7.6 0.67 Taiwan WCC-7 3.5 37.5 0.09 1.1 7.6 0.67 Taiwan WCC-8 2.8 40 0.07 1.2 7.6 0.67 Taiwan WCC-9 3.5 18.5 0.19 1.12 7.6 0.67 Turkey Cark 24 3.5 15 0.23 2.601 7.4 0.41 Turkey Cark 25 3.5 15 0.23 2.6 7.4 0.41 Turkey dn1 20.00% 1.7 7.4 0.3 Turkey dn2 20.00% 2.5 7.4 0.3 Turkey dn3 20.00% 1.7 7.4 0.3

Turkey Cumhuriyet 22 0.30% 0.951 7.4 0.41



106

Table A-2: Measured and Predicted Lateral Spread Displacements by Modifications and Sounding

Event Sounding Measured Disp. (cm)

Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)

Predicted Disp., Depth (cm)

Predicted Disp., Thin Sand

(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)

Predicted Disp., Fines

Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)

Predicted Disp., All

Mod. (cm)

Christchurch 64 4 77.4 71.4 43.1 71.6 55.2 67.8 71.6 18.1 Christchurch 71 4 47.9 43.6 13.9 46.8 41.0 44.2 46.8 11.9 Christchurch 168 28 127.9 122.7 38.7 122.6 79.4 116.7 122.6 12.0 Christchurch 325 89 147.0 134.9 97.8 147.0 100.7 145.3 147.0 54.0 Christchurch 1108 79 3594.6 3338.2 2195.0 3363.9 3098.9 3234.5 3363.8 1640.8 Christchurch 1420 77 24.1 22.5 15.5 23.3 13.6 19.5 23.3 7.3 Christchurch 1422 87 302.2 285.9 218.8 279.0 157.0 285.1 278.9 63.1 Christchurch 1425 62 60.4 56.2 49.9 57.5 25.8 46.5 57.5 13.1 Christchurch 2153 18 7.1 6.7 1.8 7.1 7.1 7.0 7.1 1.5 Christchurch 2161 48 7.7 7.6 2.8 7.7 6.3 7.6 7.7 1.3 Christchurch 2242 29 9.0 8.1 5.0 9.0 6.8 8.3 9.0 2.1 Christchurch 2319 90 7.6 7.2 3.4 7.6 6.6 7.0 7.6 2.0 Christchurch 2333 83 6.5 6.3 2.9 6.5 6.2 6.5 6.5 2.6 Christchurch 3924 39 2.6 2.5 0.9 2.6 2.1 2.5 2.6 0.3 Christchurch 4643 32 15.2 14.5 9.7 13.4 11.5 13.9 13.4 6.0 Christchurch 4985 36 6.5 5.8 2.3 6.5 5.6 6.0 6.5 1.5 Christchurch 5252 38 75.8 71.5 42.2 73.8 35.9 64.0 73.8 6.3 Christchurch 6382 6 15.1 14.3 6.7 15.1 15.1 15.1 15.1 6.2 Christchurch 9712 84 4.5 4.2 1.4 3.9 4.5 4.2 3.9 1.2 Christchurch 11033 60 14.6 14.1 8.5 14.6 7.2 14.2 14.6 1.3 Christchurch 12268 66 21.5 20.5 0.6 21.5 21.5 21.4 21.5 0.3 Christchurch 13716 0.1 4.0 3.8 1.3 4.0 4.0 4.0 4.0 1.3

107

Table A-2 Continued


Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)



(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)


Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)


Mod. (cm)

Christchurch 15287 27 6.9 6.6 3.9 6.9 6.9 6.7 6.9 3.7 Christchurch 15599 76 13.9 12.7 10.8 13.9 12.9 13.5 13.9 8.9 Christchurch 15600 95 66.0 58.9 58.0 66.0 50.9 62.6 66.0 37.5 Christchurch 15632 10 10.0 9.3 8.1 10.0 6.7 10.0 10.0 4.6 Christchurch 15641 93 50.3 45.8 34.3 43.0 32.9 45.7 43.0 12.8 Christchurch 15682 52 26.8 26.0 18.9 26.1 15.4 24.0 26.1 7.8 Christchurch 15703 35 19.3 18.4 19.3 19.3 8.4 19.3 19.3 8.9 Christchurch 15772 105 60.7 56.7 51.3 60.7 36.7 56.9 60.7 30.1 Christchurch 15776 90 45.8 42.5 35.0 40.6 35.3 39.6 40.6 16.7 Christchurch 19088 9 2.5 2.5 1.7 2.5 2.5 2.5 2.5 1.6 Christchurch 21509 69 89.9 83.6 33.6 89.9 77.3 89.9 89.9 19.8 Christchurch 21510 106 141.2 129.6 49.7 141.2 134.6 132.2 141.2 38.8 Christchurch 26641 21 34.6 31.4 22.1 33.7 29.6 33.2 33.7 16.3 Christchurch 27046 43 3.9 3.7 2.5 3.8 3.5 3.6 3.8 1.8 Christchurch 29053 104 82.3 79.5 77.7 82.3 41.7 81.1 82.3 37.6 Christchurch 29058 102 83.1 78.8 68.7 83.1 61.4 75.1 83.1 49.0 Christchurch 34460 57 23.8 22.6 14.9 23.8 23.8 22.9 23.8 14.6 Christchurch 34616 38 9.5 8.7 5.5 8.9 5.2 8.5 8.9 1.9 Christchurch 38115 39 10.6 9.9 6.5 8.4 7.9 10.6 8.4 3.4 Christchurch 38121 69 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Imperial Valley 442 100 90.9 90.8 16.6 64.1 81.7 56.5 64.1 7.1

Imperial Valley 700-lsu006 100 125.2 122.3 27.5 110.5 119.3 98.7 110.5 19.3

Imperial Valley Heber 1 120 36.7 36.7 0.1 36.7 36.7 6.8 36.7 0.1

108

Table A-2 Continued


Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)



(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)


Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)


Mod. (cm)

Imperial Valley Heber 2 120 358.8 347.9 123.5 326.6 283.7 153.3 326.6 54.6 Imperial Valley Heber 3 120 290.1 290.1 155.5 225.9 225.9 142.4 225.9 68.0 Imperial Valley Heber 4 120 557.8 557.8 285.7 409.6 493.5 194.3 409.4 108.6 Imperial Valley Heber 5 120 322.2 322.2 193.4 258.0 290.1 73.9 258.0 37.1 Imperial Valley Heber 6 120 503.3 500.2 129.9 319.2 353.9 281.9 319.2 3.4 Imperial Valley Heber 7 120 504.2 504.2 116.7 408.0 415.8 230.6 407.9 60.3 Imperial Valley Heber 8 120 291.8 291.8 71.3 259.7 291.8 168.8 259.7 71.5 Imperial Valley pqs1 0.1 45.6 45.6 38.6 39.4 21.2 30.7 39.4 10.1 Imperial Valley pqs2 0.1 41.4 41.4 38.7 41.4 13.7 33.9 41.4 6.0 Imperial Valley pqs3 0.1 56.2 55.9 48.9 50.0 14.5 50.5 50.0 7.9 Imperial Valley pqs4 0.1 50.3 50.3 43.5 50.3 19.9 29.1 50.3 12.7 Imperial Valley pqs5 0.1 41.4 41.4 37.5 41.4 7.0 38.3 41.4 6.7

Loma Prieta UC-18 0.1 105.6 102.5 0.1 48.3 80.1 69.3 48.2 0.1 Loma Prieta UC-2 7.4 157.8 156.8 108.3 77.4 91.6 82.9 77.1 26.9 Loma Prieta UC-3 28 247.7 246.0 134.5 188.4 201.3 180.0 188.3 69.7 Loma Prieta UC-4 28 157.1 151.5 87.0 93.0 113.4 100.3 93.0 21.4 Loma Prieta UC-5 25 114.6 114.4 48.2 103.5 64.9 91.1 103.5 31.7 Loma Prieta UC-6 25 58.1 58.1 14.1 42.7 38.6 42.4 42.6 1.9 Northridge BAL-1 50 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Northridge BAL-10 50 33.9 32.0 3.5 29.4 29.3 22.1 29.3 1.8 Northridge BAL-11 50 28.6 24.6 2.2 22.7 26.7 22.4 28.6 1.7 Northridge BAL-12 50 21.6 21.7 0.9 4.0 21.7 9.2 4.0 0.4 Northridge BAL-13 50 34.3 31.5 0.9 14.7 29.7 25.8 14.7 0.7 Northridge BAL-13.5 50 30.0 26.0 1.3 30.0 30.0 17.0 30.0 0.7

109

Table A-2 Continued


Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)



(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)


Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)


Mod. (cm)

Northridge BAL-15 50 5.5 5.5 0.1 0.1 5.5 5.5 0.1 0.1 Northridge BAL-16 50 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Northridge BAL-2 50 0.7 0.7 0.1 0.7 0.7 0.7 0.7 0.1 Northridge BAL-3 50 28.1 27.2 1.4 18.4 28.2 10.3 18.4 0.3 Northridge BAL-4 50 10.2 8.8 0.9 10.2 10.2 2.2 10.2 0.2 Northridge BAL-5 50 8.0 8.0 1.2 0.1 8.0 8.0 0.1 0.1 Northridge BAL-6 50 8.7 8.7 0.8 1.5 4.0 3.9 1.4 0.1 Northridge BAL-7 50 43.8 43.8 2.8 27.7 30.0 17.8 27.6 0.2 Northridge BAL-8 50 20.0 17.8 1.4 20.0 20.0 20.0 20.0 1.1 Northridge BAL-9 50 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Northridge MAL-11 25 22.4 20.9 3.4 17.7 22.4 13.4 17.7 2.6 Northridge MAL-12 25 29.6 29.6 3.7 11.0 29.6 7.3 11.0 2.0 Northridge MAL-13 25 4.9 4.9 2.1 4.9 4.9 4.9 4.9 2.1 Northridge MAL-3 25 104.8 103.7 24.2 77.3 67.9 42.5 77.1 3.1 Northridge MAL-4 25 72.5 72.5 13.8 58.8 40.2 35.0 58.7 3.1 Northridge MAL-5 25 59.3 59.3 7.8 45.1 17.9 28.0 44.9 1.7 Northridge POT-1 10 112.4 112.4 26.8 14.4 97.5 54.7 14.4 1.1 Northridge POT-10 10 233.8 231.1 71.8 89.5 204.1 88.3 89.5 11.3 Northridge POT-11 10 491.0 495.3 89.4 387.1 367.4 308.2 387.1 20.1 Northridge POT-12 10 732.4 719.9 179.8 598.8 406.2 359.6 598.8 46.7 Northridge POT-3 10 116.8 116.8 29.0 38.4 112.2 54.8 38.4 10.0 Northridge POT-4 10 98.9 96.5 21.3 59.0 80.5 98.9 59.0 4.7 Northridge POT-5 10 135.5 128.7 25.9 106.8 123.3 108.7 106.8 10.7 Northridge POT-6 10 181.3 180.4 35.6 119.6 159.0 96.9 119.6 16.9

110

Table A-2 Continued


Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)



(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)


Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)


Mod. (cm)

Northridge POT-7 10 237.6 233.8 55.9 233.0 196.1 166.3 233.0 22.9 Northridge POT-8 10 394.8 384.5 84.2 169.1 365.1 203.3 168.4 2.5 Northridge POT-9 10 333.0 338.6 115.6 188.2 294.1 146.1 188.2 46.7 Northridge WYN-1 15 121.4 118.1 27.4 101.3 94.8 102.4 101.2 15.9 Northridge WYN-10 15 141.5 141.5 26.2 58.4 121.0 66.1 58.1 9.1 Northridge WYN-11 15 107.1 106.6 21.3 59.1 96.9 77.2 59.0 12.6 Northridge WYN-12 15 38.1 23.6 6.2 7.5 18.5 10.8 7.3 0.8 Northridge WYN-13 15 10.7 10.7 1.6 3.2 8.2 8.2 3.2 1.0 Northridge WYN-14 15 93.3 92.6 16.9 36.4 58.3 51.3 36.3 0.2 Northridge WYN-2 15 93.5 93.5 15.6 40.3 83.2 53.5 40.2 11.2 Northridge WYN-3 15 141.2 141.2 14.0 95.5 90.0 42.3 95.5 4.8 Northridge WYN-4 15 202.2 202.2 22.5 92.6 140.8 103.3 92.0 13.2 Northridge WYN-5 15 425.4 419.8 27.5 242.4 169.4 90.2 240.9 8.7 Northridge WYN-7 15 232.0 232.0 34.4 104.8 160.3 103.8 104.2 13.8 Northridge WYN-8 15 199.4 199.4 40.4 162.3 100.4 102.7 162.1 8.2

San Fernando sfvjh81-2 15 148.7 137.4 45.1 139.4 148.7 129.5 139.4 38.7 San Fernando sfvjh81-4 15 151.8 145.3 54.2 140.9 151.8 90.5 140.9 31.7 San Fernando sfvjh81-6 15 20.0 20.0 2.0 13.0 20.0 13.6 13.0 1.9 San Fernando sfvjh9 0.1 12.2 12.2 1.1 1.4 12.2 4.7 1.4 0.3

Taiwan MAA-C-9 40 33.7 32.5 33.7 6.3 10.9 28.2 30.7 6.3 Taiwan NCC-2 25 943.4 889.6 79.7 772.2 773.9 691.2 772.2 37.8 Taiwan NCC-3 25 548.1 522.3 190.4 194.9 404.7 287.3 436.6 45.7 Taiwan RESI-C7 95 1258.5 1218.9 432.5 762.3 1199.2 652.2 762.1 33.4 Taiwan WBC-1 35 119.5 113.4 71.2 103.3 93.8 81.7 103.3 26.9

111

Table A-2 Continued


Predicted Disp., No

Mod. (cm)

Predicted Disp.,

Transition (cm)



(0.3m) (cm)

Predicted Disp.,

Dil/Cont (cm)


Content (cm)

Predicted Disp.,

Elim Thin Sand

(0.3m) (cm)


Mod. (cm)

Taiwan WBC-4 96 296.5 290.8 152.8 288.4 264.0 256.6 288.4 110.6 Taiwan WCC-1 70 816.7 785.2 251.0 639.7 798.0 571.1 639.7 160.1 Taiwan WCC-11 0.1 168.1 163.7 30.7 73.9 136.3 92.9 73.9 2.9 Taiwan WCC-12 40 315.7 300.7 59.8 171.8 304.9 163.2 171.7 22.3 Taiwan WCC-13 0.1 88.6 87.2 25.7 44.3 64.9 51.5 44.3 2.5 Taiwan WCC-2 45 247.5 241.2 84.8 159.2 254.4 165.9 159.2 43.2 Taiwan WCC-4 55 446.9 438.3 142.3 278.2 427.4 230.6 278.2 41.4 Taiwan WCC-6 23 234.2 215.7 190.1 216.7 200.8 173.4 216.7 94.6 Taiwan WCC-7 10 288.7 281.0 105.8 152.6 277.3 150.2 152.6 46.3 Taiwan WCC-8 0.1 181.5 175.8 62.2 109.6 166.0 96.7 109.6 17.3 Taiwan WCC-9 29 431.8 404.0 281.2 395.8 396.3 321.6 395.8 173.3 Turkey Cark 24 0.1 181.0 172.6 180.9 106.9 116.7 97.3 106.8 52.0 Turkey Cark 25 0.1 206.9 186.8 204.6 143.0 146.0 145.7 142.9 78.4 Turkey dn1 87 3886.9 3666.6 308.5 3659.4 3477.2 2910.0 3659.4 297.8 Turkey dn2 9 712.7 665.5 181.7 712.7 712.7 700.4 712.7 171.8 Turkey dn3 0.1 133.7 130.0 70.6 125.8 133.7 132.4 125.8 63.8

Turkey Cumhuriyet 22 0.1 157.1 148.0 36.8 100.8 83.9 101.2 100.8 8.0



112

APPENDIX B SOUNDING LOGS

The following graphs are logs of the all the sounding used in this study. These show

typical outputs of data: cone tip resistance (qc), sleeve friction (fs), pore water pressure (u), and

soil type index value (Ic). Some of the pore water pressure data was not provided and the logs

reflect these as a vertical line on zero.

113

Figure B-1: Christchurch Avon River 11033

114


115


116


117


118


119


120


121


122


123


124


125


126


127


128


129


130


131


132


133


134


135


136


137


138


139


140


141


142


143


144


145


146


147


148


149


150


151


152


153


154


155

Figure B-43: Imperial Valley Heber 1

156


157


158


159


160


161


162


163

Figure B-51: Imperial Valley Heber 700-lsu006

164


165

Figure B-53: Imperial Valley Riverpark pqs1

166


167


168


169


170

Figure B-58: Loma Prieta Moss Landing UC-18

171


172


173


174


175


176

Figure B-64: Northridge Balboa 1

177


178


179


180


181

Figure B-69: Northridge Balboa 13.5

182


183


184


185


186


187


188


189


190


191


192

Figure B-80: Northridge Malden 11

193


194


195


196


197


198

Figure B-86: Northridge Potrero 1

199


200


201


202


203


204


205


206


207


208


209

Figure B-97: Northridge Wynne 1

210


211


212


213


214


215


216


217


218


219


220


221

Figure B-109: San Fernando Juvenile Hall sfvjh81-2

222


223


224

Figure B-112: San Fernando Juvenile Hall sfvjh9

225

Figure B-113: Taiwan MAA C9

226

Figure B-114: Taiwan NCC 2

227

Figure B-115: Taiwan NCC 3

228

Figure B-116: Taiwan RESI C7

229

Figure B-117: Taiwan WBC 1

230

Figure B-118: Taiwan WBC 4

231

Figure B-119: Taiwan WCC 1

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240


241

Figure B-129: Turkey Cark 24

242

Figure B-130: Turkey Cark 25

243

Figure B-131: Turkey Cumhuriyet 22

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246

Figure B-134: Turkey Degirmendere dn1

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248


analysis of applied modifications to a cone penetration

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