RISK MANAGEMENT IN GEOTECHNICAL ENGINEERING

AND OPEN PIT MINE PLANNING

O K H STEFFEN Pr. Eng.

INTRODUCTION

Engineering is commonly considered to be an exact science as structures have

been created for the purpose of inhabiting space in a safe environment, utilizing

materials that are created for defined purposes. In the geotechnical field there is

less certainty as materials are developed from nature and different approaches

are required to quantify geotechnical data. Numerous models for predicting the

geotechnical properties have been proposed as follows:

1946, Terzaghi’s Rock Load classification for steel arch-supported

tunnels;

1964, Don Deere, initiated the original RQD concept of providing a

numeric for different quality of rock masses, based on drilled core;

Laubscher, RMR : Rock Mass Rating was developed to determine the

cave-ability of the rock mass subject to a “Hydraulic radius”;

Bieniawski, RQD: Rock Quality Designation developed from drilled cores

and rock exposures at the CSIR in South Africa;

Barton, Norwegian Geotechnical Institute, NGI for ‘Tunnelling Quality

Index’ or ‘Q’ value;

Hoek-Brown, GSI: Geological Strength Index, allowing for different

geological and structural environments.

STRUCTURAL COMPLEXITY, GRADINGS

Bayes’ theorem provides an insight to the approach required when data

uncertainty is questioned. A typical example of this approach is estimating the

quantity of structural data required to provide an acceptable level of confidence,

i.e. at what spacings should boreholes be drilled to provide an acceptable level of

confidence in the estimation of structural complexity. Figure 1 is an attempt to

classify drilling requirements from experience of numerous case histories for low,

medium, and highly complex structural geology. This approach will be modified

with time and additional subdivisions will be developed by continuously adding

real data.

Fig 1 - Degree of structural complexity related to data requirements

The same question arises: how much data is required to obtain the required

confidence in the structural models. In hard rock slopes, structures are the

dominant factor in engineering safe slopes. For this reason an attempt has been

made to classify the level of structural complexity, followed by an appropriate

level of data confidence. This table is a first pass at classification and should be

considered as work in progress. What is required is real data from many different

sites to compile a comprehensive data base of information versus success and

failures. While experienced geologists may consider this as ‘bread and butter’

fodder, their input is required to avoid repeating the learning curve from scratch,

and instead start from an advanced knowledge base.

Data quality definition:

Achieving an acceptable standard of data quantity and quality is the

ultimate aim for proceeding with engineering design. The current basis

for the required standards is presented in the figure below.

Bayes Theorem: In essence, Bayes theorem suggests that when more

data is added without changing the value of the result, then sufficient

information has been provided for a reliable outcome. This only applies

after all attributes have been considered. Bayes’ theorem

demonstrates how posterior outcome “B” alters the prior assessment of

“A”:

P[A I B] = P[A]{Additional Knowledge}

Parameters are Expected Value E[R] and Variance V[R]. Identify the difference

between variances of known parameters and uncertainties in feature

occurrences.

There is a great need for the development of the model in Fig 1 to provide

guidance in the approach to developing a reliable model for the quantifying of

data reliability. This topic has generated much interest and is the prime topic of

studies being undertaken by a number of research programs. The objective is to

define the data requirements related to different levels of structural complexity.

The approach developed in the case of Resource and Reserve Estimation is a

guide that could easily be adapted to the data requirements for determining the

reliability of the structural complexity.

CLASSIFICATION OF CONFIDENCE IN DATA

This concept has been developed in the theory of grade estimations using

sophisticated kriging techniques and others.

Fig 2 - Standard methodology for classifying confidence levels in grade estimations

A distinction is made in terms of viable economic value as ‘Reserves’ and total

mineralised content as ‘Resources’. Only ‘Proven’ and ‘Probable’ categories are

considered as Reserves, and the possible category as potential value to be

confirmed by additional evaluation. All parameters listed in Fig 2 need to comply

with the classification by a ‘competent person’ in his/her opinion, based on data

available.

CONFIDENCE CATEGORIES FOR SLOPE DESIGN

Possible slope angles:

Based on experience in similar rocks;

Rock mass classification;

Reasonable inference of geological conditions;

Apply kinematic slope design procedures.

CLASSIFICATION OF

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GEOTECHNICAL, MINING, ECONOMIC, METALURGICAL, MARKETING, GEOTECHNICAL, MINING, ECONOMIC, METALURGICAL, MARKETING,

ENVIRONMENTAL, SOCIAL AND GOVERNMENTAL FACTORS MAY ENVIRONMENTAL, SOCIAL AND GOVERNMENTAL FACTORS MAY

CAUSE MATERIAL TO MOVE BETWEEN RESOURCES AND RESERVESCAUSE MATERIAL TO MOVE BETWEEN RESOURCES AND RESERVES

RESERVESRESERVES

INDICATEDINDICATED

INFERREDINFERRED

MEASUREDMEASURED

PROBABLEPROBABLE

POSSIBLEPOSSIBLE

PROVENPROVEN

RESOURCESRESOURCES

Probable slope angles:

Data allows reasonable assumptions for continuity of stratigraphic and

lithological units;

All major features and joint sets identified;

Some structural mapping will have been carried out with estimates of

joint frequencies, lengths and condition;

Some testing of rock and joint surfaces;

Groundwater based on water intersection in exploration holes with a

few piezometer holes;

Data will allow simplified design models to allow sensitivity analyses;

Slope design using kinematic as well as numerical modelling;

Define factor of safety criterion.

Proven slope angles:

High confidence in continuity of stratigraphic, lithological units and

major features is confirmed;

Structural fabric extrapolated with high confidence;

Strength of structures tested to provide for reliable statistics;

Laboratory testing of rock strengths to a high level of confidence for all

different rock types;

Sufficient piezometer installations to determine the ground water

compartments and water pressure monitoring;

Slope displacement monitoring using radar and/or robotic prism

systems;

Kinematic and numerical slope design models as appropriate, including

parameter variability;

Risk evaluation of potential slope failures.

The development process of moving from the initial stages of ‘Possible’, to

‘Probable’ and finally ‘Proven’ design, can be demonstrated in Fig 3 below.

Fig 3 - Development phases from “conceptual” to “bankable feasibility” study

With relatively little data available at ‘conceptual’ stage of the design process, the

likely range of slope angles will be widely spread as shown in the ‘red range’ of

Fig 3. As more data becomes available during the Pre-feasibility stage the spread

of data reduces, and finally in the bankable feasibility stage a further reduction in

the data spread provides sufficient reliability for the planning at feasibility

standards.

A similar interpretation can be presented as shown in Fig 4 below, where the

same data is shown in the spread of the variances to a single PoF (probability of

failure) value for the different data distributions. Fig 4 demonstrates the

variability of the FoS from 1.6 to 1.2, purely due to the volume of data available.

This simple exercise demonstrates the requirements of data volumes to improve

economics and reliability.

Fig 4 - FoS reduces from 1.6 to 1.2 for the same PoF of 5%, due to improved data

VOLUME VARIANCE CHARACTERISTICS

Fig 5 below demonstrates the volume variance characteristics for typical sample

data on different properties of an iron ore deposit. Of interest is the trend

analysis for the majority of iron elements at a very gradual conversion, while in

the case of the outlier element there is a very slow rate of conversion resulting in

a large residual variance.

Such variances result in the need for increased volumes of exposed ore to allow

an acceptable blend of ore to the mill. Blending beds are an integral part of the

management to control the quality of ore delivered to consumer clients.

Fig 5 - Data obtained from blast hole drilling, demonstrating the Volume/Variance

relationships

ASSESSING RELIABILITY OF OPEN PIT MINE PLANNING

Many factors in surface mining operations contribute to ‘non-delivery of the

promise’ as presented in the mine plan. The advance of mine planning computer

models has provided the engineers the opportunities to investigate numerous

options within a short time frame. The challenge is to match the computer

output to an executable mining operation. Experience is essential in forming the

link between the mine plan and the practical execution in the ground.

This presentation will discuss the pit falls that embrace assumptions made in the

plan, the NPV expectations from the owners, the practical execution, the risks

associated with slope in-stability and social and environmental impacts.

Risk definition and quantification

Risk = Probability (event) x (consequence)

The ultimate impact relates to the final consequence, which represents the

perceived risk. Different levels of risk are identified in the following discussion.

Mine planning

A typical structure for an open pit mine risk valuation is presented in Fig 6 below.

Risks can be estimated for each of the boxes within the branches and are

accumulated into the “TOP FAULT”. Deterministic data can provide real

information while subjective judgement is also required to determine acceptable

risk profiles.

Fig 6 Typical construct of a high level risk analysis

Each of the above components can be assessed in detail as shown in Fig 6 below.

In this case, the mine planning risks are addressed in terms of geological,

geotechnical, mine layouts, as well as operating risks. In addition, QA/QC, the

macro economic impact, PR and HR components provide control mechanisms

within the planning programs for risk management.

Fig 7 - Risk components of the mining plan

RISK EVALUATION

The first level of risk evaluation is the well-developed qualitative risk matrix

shown in Fig 8. The impact on the vertical scale (Million Rand) presents a cost in

South African Rands and the likelihood of an event is presented on the horizontal

scale in percentage terms.

Fig 8 - Qualitative risk matrix

The above risk matrix is a subjective process in terms of the 5 x 5 matrix, of

impact versus likelihood. This process is universally accepted as a guide to assess

risks. In addition to the qualitative assessment, a quantitative approach has been

adopted in many critical decisions which impact directly on safety and economics.

It has become standard practice that quantitative risk evaluation follows the

qualitative model and thereby moderates both approaches to an acceptable

outcome.

Figures 9 to 12 show the development of a quantitative valuation of risk applied

to a case history. This model was developed by Luis Fernando Contreras of SRK

Consulting, and has become standard practice in improving the ‘value add’ to the

design process.

Different slope angles were chosen and evaluated in terms of risk and

consequences, expressed quantitatively. The base case was selected for differing

hanging wall and footwall, in increments of 50 each as illustrated in Fig 9.

Fig 9 - Slope geometry of alternative design options for risk analyses

Fig 10 - Different pit development profiles are developed over time

Typical mining profiles are developed for each alternate mine geometry for every

second year as indicated. The life of mine boundary curve represents the

cumulative impact of the likelihood of events and consequential costs, expressed

in NPV terms.

Fig 11 below, provides the alternative outcomes for the different options for

slope angles developed in the mine design. These determinants are then

evaluated in NPV terms, but could equally be determined on an annualized

operating cost basis, instead of the NPV parameter.

Fig 11 - The boundary curves developed for the different slope angles into NPV

Different values of risk can be determined as shown in Fig 12 below. Cost

associated with risk acceptance has been determined ranging from 50% to 90%

likelihood, as shown in the lower diagram. These costs are expressed relative to

the NPV. These costs have then been applied to the value created by the mine

design. The zero risk option is represented in the upper curve as NPV without

risk. Adjustments are then made to allow for the cost of risk as per the lower

graph, resulting in the more realistic valuation as indicated in the upper graph

including the risk component.

These models have been developed in a number of case studies and have resulted

in more realistic outcomes than previously used models of ‘gut feel or subjective

estimates’.

Fig 12 - Value and Risk relative to NPV

CONCLUSIONS

CONCLUSIONS

Competitive designs for the same orebody development have invariably

resulted in improved results of efficiency, costs and safety. With the

numerous design packages available today, competitive designs should

be based on the following paramenters:

• Mining layouts for ore exposure,

• Drilling and blasting techniques and advanced technologies, e.g pre-

split benches with high standard of QA/QC.

• Dual ramp layouts to provide alternative access in case of obstruction

and bench failures

• Ore routes separated from waste haulage routes

• In pit space risk for suitable and safe working environments

• Pushback strategy excludeing outer limit ramp systems

Geotechnical design.

Geotechnical designs are probably the highest risk parameter in open pit

operations, as steeper slopes results in lower costs and a quantified risk

analysis is essential. Blast damage of slopes can be hazardous and dual

access should always be available.

Geotechnical data gathering should be an ongoing process to improve

data reliability to provide optimal designs. Ground water conditions

should be identified for it is the only parameter which can be controlled

by drainage.

Risk integration of geotechnical and operating standards should always

be a high priority of mine management. Implementation of the required

standards frequently is ignored and awareness should be recorded

diligently House keeping is an integral part of ensuring sound standards

which develops into a total responsibility of all personnel.

Slope monitoring has become a standard procedure in predicting failure

potential and should be reported regularly. In spite of all the technology

that assists in monitoring potential failures, there are still cases of

surprise and unexpected slope failures.