Slope Stability Risk Management in Open Pit Mines · Slope Stability Risk Management in Open Pit...

International Geoinformatics Research and Development Journal

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Vol. 7, Issue 4, December 2016 Special Issue: GiT4NDM-EOGC2015

Slope Stability Risk Management in Open Pit Mines

Karam, K.S.1, He, M.C.

2, and Sousa, L.R.

2,3

1Masdar Institute of Science and Technology, Abu Dhabi

2China University of Mining & Technology, Beijing, China;

3University of Porto, Portugal

[email protected]

Abstract

The stability of slopes in open pit mines is an issue of great concern because of the significant detrimental con-

sequences instabilities can have. To ensure the safe and continuous economic operation of these mines, it is nec-

essary to systematically assess and manage slope stability risk. This, however, has not been traditionally easy

due to the fact that measuring the parameters needed to assess the stability of slopes can be laborious, expen-

sive, and cause disruption to mining operations. This paper presents a framework based on decision theory by

which risk can be systematically assessed and managed, and proposes a combination of traditional, and remote

sensing techniques, both earth and satellite based, to measure certain parameters. This combination allows one

to assess and update risk in a more efficient and cost-effective way than is traditionally done particularly where

satellite observation data is already available. The application of such a system at the Nanfen iron open pit mine

in China is presented, where a novel technique for monitoring sliding forces on pre-stressed rock bolts was

developed and successfully applied. This is the first step in building an automated risk management system,

which in the future will include smart sensors for warning systems and stabilization methods based on materials

that can self-adjust their properties such as strength, and stiffness in response to potential instabilities.

Keywords: Risk Management, Decision Analysis, Slope Stability, Open Pit Mines, Remote Sensing.

Introduction

Open pit mines are among the largest geotechnical structures in the world and are located necessarily where ore

can be extracted economically. These are frequently in inconvenient locations for the geotechnical engineer be-

cause processes such as seepage concentrate the ore along a fault, which then passes through the pit. It is not un-

common to find strata on opposite sides of the fault dipping in different directions and having different geotech-

nical properties.

Excavating these mines usually takes place in benches, and as such the stability of individual benches as well as

that of the overall slope must be ensured. Excavating and disposing of material are typically major costs in mine

operation and therefore it is economically desirable to reduce the volume that needs to be excavated. This im-

plies that the slopes of the pit be as steep as possible, which, in turn, implies greater danger of slope failures.

Slope instabilities can have detrimental consequences in terms of socio-economic damage, and sometimes even

loss of life. To ensure the safe and economic operation of these mines, it is necessary to systematically access

and manage slope stability risk. This paper presents a framework based on decision theory by which risk can be

systematically assessed and managed.

mailto:[email protected]


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The risk assessment involves estimating the properties of the materials in the slopes, calculating the stability of

various slope geometries, and monitoring the performance of the slopes as the pit is developed. The risk man-

agement involves making decisions on the actions to mitigate these risks.

Warning systems are described with emphasis placed on monitoring systems based on traditional and remote

sensed data. The application of a warning system in the Nanfen iron open pit mine in China is presented where a

novel technique for monitoring sliding forces on pre-stressed rock bolts was developed and successfully applied.

In the future, risk management systems will become automated and include smart sensors and materials that have

self-adjusting properties.

Decision Analytical Approach to Slope Stability Risk Assessment and Management

Definitions

Risk can be expressed in many different ways. The definitions used in this paper are based on the ISSMGE rec-

ommended Glossary of risk assessment terms (TC32).

Danger (Threat): Phenomenon, natural or manmade, that could lead to damage

Hazard: Probability that a danger (threat) occurs within a given period of time

Consequence (Damage): Result of a hazard being realized

Vulnerability: Degree of loss to a given element or set of elements within the area affected by a hazard

Risk: Measure of the probability and severity of an adverse effect on life, health, property, or the environment

Following these definitions, risk can be expressed as:

i

ii CuTCPTPR ]|[][ (1)

Where:

R is Risk; ][TP is the hazard, defined as the probability that a particular threat occurs within a given period;

i

i TCP ]|[ is the vulnerability, defined as the conditional probability that consequences iC occur given the

threat; iC are consequences which can include for example economic damage, as well as those that affect

human lives, such as injuries and fatalities; and iCu is the utility of consequences iC , defined as a function

that describes a decision maker’s relative preferences between attributes.

Framework

Decision analysis provides a framework by which complex decision problems can be simplified, allowing one to

more clearly make decisions based on alternatives [50, 41]. Fig. 1 Figis a graphical representation of decision

making under uncertainty as proposed by [162, 61]. This process is actually also the standard decision-making

process used in engineering in which one determines parameters, includes them in models and makes decisions

based on the model results. Fig. 2 represents the decision analytical approach applied to natural threat risk as-

sessment and management. The updating cycle in Figcan, amongst other things, represent the observational

method in geotechnical engineering [66, 60, 14].


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Figure 1: The Decision Analysis Cycle.

Figure 2: Decision Analytical Approach to Natural Threat Risk Assessment and Management [16].

The steps or levels in the decision analytical approach (Fig) are described in the following. Since this paper is re-

lated to risk management, the emphasis is placed on this level whereas the other levels are briefly described.

Level 1: Assess the State of Nature

In decision analytical terms, this level forms part of the information collection. This information pertains to the

factors that affect the stability of slopes in open pit mines. Slope instability initiation is complex and depends on

a great number of factors and the interaction between them. Some of these factors are shown in Fig


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Figure 3: Some Factors Affecting Slope Instability in Open Pit Mines.

The geology of a mine site is usually well known because the investigations that led to the establishment of the

mine in the first place should provide a reasonable geological description of the site. In contrast, engineering

properties of soils and rocks that affect the stability of slopes are often less well known. Gravitational loads are

the most important loads affecting the stability of a slope in a mine, and the major resistance to failure is the

shear strength of the ground. This is a major concern of geotechnical engineers since the strength parameters can

vary significantly even in small areas. The strength of a rock mass is affected by not only the intrinsic rock

strength but also the existence, and persistence of fractures and joint sets, patterns, and networks. Furthermore,

the percolation of pore fluids have major effects on the stability, and subsurface flow regimes can be complex. In

addition, natural events, such as rainfall and earthquakes, and man-made factors, such as blasting, can serve as

triggers to slope instabilities [47 ,43].

Collecting information on the parameters that affect slope stability, or site characterization, is usually done

through exploration schemes. Exploration is defined as information collection that involves processes of induc-

tion based on interpretations of regional geology and geologic history, as well as a deduction based on site inves-

tigations and measurements. Typically, information may comprise traditional techniques such as maps, photo-

graphs, boring logs, topographical data, weather data and visual observations. Site characterization, however,

requires the allocation of resources, effort, and money, both of which are usually scarce, and therefore a tradeoff

is usually made between the value of exploration and economics based on a cost-benefit analysis. Since this

forms part of the cost of a mine operation, frequently not enough resources are allocated to site characterization

as one would desire.


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Level 2: Identify and Describe Danger

This involves both information collection and deterministic modeling. There are various techniques to assess

slope stability that range in complexity from simple heuristic models to statistical models based on historical

data, to more complex mechanical models which attempt to represent reality. A typical mode of slope failure in

open pit mines involves a block of material sliding on two or three planes of discontinuity, known as rock

wedges. The stability analysis of wedges in rock slopes involves resolution of forces in two or three-dimensional

space. The problem has been extensively treated in, for example, [24, 46, 55, 36, 45, 37], Goodman [24,25,26],

Hoek and Bray [37,39,70,69]. The methods used include stereographic projection technique, engineering

graphics, and vector analysis.

Level 3: Determine Probabilities and Combine with Danger to Determine Hazards

Uncertainties are inherent to geology and geotechnical engineering since they deal with the underground and

thus cannot be seen. Several categorizations of uncertainties have been proposed over the years that include [3, 4,

13, 8, 51]. [17] Discuss uncertainties in the context of slope stability risk assessment. It should also be noted that

when formally assessing uncertainties, this can be done both by the relative frequentist or the subjective

approach. These approaches and their applicability have been discussed by Baecher [3,13,15].The formal

incorporation of uncertainties into the slope stability problem results in probabilities of slope instabilities or

hazards. These can range in from first and second order reliability analyses [31, 68, 11,10] to full distribution

probabilistic analyses based on numerical techniques [67, 56, 28]. Examples of such studies for the stability of

slopes in open pit mines include for example [63, 30, 12, 22, 58] as well as others.

Level 4: Risk Assessment

In Section 2.1, risk was defined as a measure of the probability and severity of an adverse effect on life, health,

property, or the environment or the product of the hazard and the potential worth of loss. Risk is therefore

obtained as a combination of the quantified hazard with the quantification of consequences conditional on the

hazard (eq. (1)). It is worth noting that a particular hazard can have different consequences. The quantification of

consequences requires the identification of consequences and associating an expression of loss with them. This

expression is usually in the form of a utility function [1, 2] , which is a dimensionless transformation that

describes the relative preferences of the decision maker towards different outcomes, as well as the decision

maker’s attitude towards risk, i.e. risk neutral versus risk averse. For a detailed discussion on utility, reference is

made to for example [50, 42, 41].

Level 5: Risk Management (Decisions)

A decision is an irrevocable allocation of resources. Decisions in the slope instability problem concern measures

that can be taken to mitigate risk. These decisions, or actions, can range from construction countermeasures to

administrative procedures. Typical actions include:


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Passive countermeasures

Passive countermeasures reduce the vulnerability, resulting in a reduced risk 'R :

pas

i

ii CuCuTCPTPR ]|[']['

(2)

Where:

]|[' TCP iis the reduced vulnerability; pasCu is the utility associated with the cost of the passive

countermeasure; and other terms are as defined for Eq. (1).

Examples of passive countermeasures include galleries and nets.

Active countermeasures

Active countermeasures reduce the hazard, resulting in a reduced risk 'R :

act

i

ii CuCuTCPTPR ]|[]['' (3)

where: ][' TP is the reduced probability of threat; actCu is the utility associated with the cost of the active

countermeasure; and other terms as defined for Eq. (1).

Examples of active countermeasures include rock bolts (anchors), tie-backs and retaining structures.

Warning systems

Warning systems mitigate risk by reducing consequences. Warning systems consist of devices capturing relevant

signals, models for relating the signal to potential threats, people and procedures interpreting the modeled results,

evaluating consequences and issuing warnings. Such warnings have then to be communicated to potentially af-

fected areas and lead to passive or active countermeasures.

Warning Systems include the following components:

1 Monitoring systems (data acquisition) that track a threat such as a slope instability

2 Communication systems (data transmission) that continually transit measurements to experts

3 Models that predict (data analysis and interpretation) the threat in the short and long terms

4 Alarm systems that transmits the warning signal(s) to the potentially affected parties efficiently and effective-

ly when a predefined alarm threshold is exceeded

5 Specific Actions (procedures) that are implemented during the warning (evacuations routes, assembly points,

others), as well as those after the warning

Error! Reference source not found. shows a flow chart for a warning system with the typical components in-

volved.

Given the uncertainties involved in each of the components above, warning systems have a reliability associated

with them. That is to say that warning systems can, on the one hand, fail to issue an alarm before a threat materi-

alizes, and on the other hand, issue false alarms. Well-designed warning systems attempt, to the extent possible,

to reduce these events so as not to lose credibility, and hence effectiveness. It is also important for warning sys-

tems to be flexible and updatable both during an event/threat and as experience and technical capabilities in-

crease.


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Figure 4: Block Diagram for Warning System [9].

Monitoring is the key to slope instability assessment, management, and mitigation. The objective of a landslide

monitoring program is to systematically collect, record and analyze qualitative and quantitative information. De-

signing a monitoring program includes the following steps:

1 Define the objective of the monitoring program and select the type of measurements to be included in the pro-

gram, assigning priorities to measurements.

2 On a cost-benefit basis, decide on the measurement methods to be used and select the appropriate instruments.

3 Determine the optimum number of instruments and locations; if possible, use theoretical or empirical models

to optimize the number and placement of instruments.

4 Decide on the preferred method of data acquisition, e.g. manual or automatic recordings.

5 Arrange for proper installation, protection and marking of instruments and reference points in the field.

6 Plan for data flow, data management and analysis.

7 Plan for adequate maintenance of the monitoring system.

Traditionally, monitoring systems have been based on information collection using visual observations to look

for evidence of instability, coupled with surface and subsurface measurements to detect movements. While these

techniques still play an important role in monitoring instabilities, they suffer from various drawbacks mostly re-

lated to the fact that they can be localized. The use of remotely sensed measurements is becoming more wide-

spread with technological advancements, and as these methods become more cost effective. The integration of

Synthetic Aperture Radar (SAR) and optical images, along with interferometric SAR (InSAR) techniques, are

currently being used to characterize instabilities. New techniques such as Differential Synthetic Aperture Radar

(DInSAR) and high-resolution image processing are also increasingly being used in risk assessment studies.

Ground-based radar devices such as Linear SAR (LISA) are capable of assessing the deformation field of an un-

stable slope in the areas characterized by a high radar reflectivity. Near-surface geophysical methods such as


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seismic, gravimetric, magnetic, electric and electromagnetic, can be used to monitor hydrogeological phenome-

na, and electric and electromagnetic survey techniques can be applied to areas with complex geology.

These remote sensing tools are practical and for many of them, affordable, particularly in open pit mines

where consequences can be very detrimental. Table 1 shows some of the techniques, as well as their strengths

and limitations [54].

It is important to note that monitoring slope instabilities have to be coupled with continuous, and if possible real

time measurements on triggering events, such as rainfall, and induced vibrations. One also needs to consider the

different models of failure in interpreting the data and modes of failure in data interpretation and the setting of

threshold values to issue alarms.

Traditional landslide monitoring systems have been based on measuring movements. These techniques cannot

easily predict brittle slope failures or failure that change from ductile to brittle. Where a slope failure is brittle, an

observation of no movement could give a false sense of security. In Section 3, a case study is presented where a

warning system was successfully implemented in the Nanfen Open Pit Iron Mine in China. The warning system

is based on a monitoring system that uses a novel technique to measure forces to complement displacement, as

well as other, measurements.

Level 6: Information Model

Slope stability risk assessments, as well as standard geotechnical engineering practice involve the gathering of

additional information. This involves updating prior probabilities to result in posterior probabilities that reflect

the new information. This is usually done using Bayesian updating. It is possible and often desirable to check

whether it is worthwhile to collect additional information through the information modeling prior to actually go-

ing out and obtaining the information. This is done through the process of pre-posterior or virtual exploration.

This process has been applied in the context of tunnel exploration planning in [48, 49] and for landslides in [20].

Early Warning System: The Nanfen Open Pit Iron Mine

Traditional landslide monitoring systems have often been based on displacements measurements because these

are easy to do, and the necessary equipment is widely available. The issue with measuring displacements is that

for brittle failures, early detection of movements comes too late.

Monitoring forces, or stresses, is more desirable since these reflect the kinematic characteristic of a slope. Force

and stress measurements are however not very easy because they form part of a natural system and are not easy

to measure often requiring sophisticated methods.

A system to measure forces was developed at the China University of Mining and Technology, Beijing

(SKLGDUE) and first applied to the Three Gorges Project in China [32,35]. The basic principles of the monitor-

ing system are shown in Fig. 5. A set of high-resolution sensors are installed at pre-selected measurement loca-

tions, which are then connected to a data acquisition and transmission system, as shown in Fig. 6. With small ra-

dio antennas mounted at the measurement locations, they are connected through a base station to a wireless

receiver. The data received is transferred via satellite to a control station where data from all measurement loca-

tions is analyzed, and the stability of the entire slope system is updated based on these measurements [33].


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Table 1. Remote sensing technologies: strength and limitations, after [44, 54, and 51].

System Applications Resolution Limitations Strengths - DEM from

all Future

Photogrammetry

Terrestrial

Joint survey,

scarp and

change detec-

tion cm to m, de-

pending on

the scale

Field of view, im-

age resolution, re-

quirement for

surveyed posi-

tions, vegetation

obscuring

Rapid, cheap, long

term record, stereo-

scopic; build DEMs

and see terrain con-

ditions

Digital Image-

ry, High-

Resolution

Scanners, and

Video Airborne

Landslide in-

ventory and

time series

analysis

LiDAR

Ground-based

(Static) Landslide

monitoring,

landslide map-

ping, topogra-

phy extraction,

joint surveys,

moisture detec-

tion

mm to cm

Humidity, dis-

tance limitations,

angular limita-

tions, reflectivity,

vegetation obscur-

ing, Field of view

Multi-return LiDAR

allows earth model,

very high accuracy,

high rate of acquisi-

tion, perspective

views possible

Cost Effective,

Signal Pro-

cessing Im-

provements, au-

tomated feature

extraction?

Ground-based

(Mobile)

Airborne (Fixed

Wing)

Airborne (Heli-

copter)

InSAR

Standard

Movement de-

tection and

monitoring,

time series dis-

placement, HR

movement de-

tection (ground

based)

mm

Visibility and

shadowing, need

reflectance,

doesn't penetrate

vegetation, lim-

ited to slow

movements

Large area survey,

long- term monitor-

ing, return frequen-

cy affects use,

comparison or

combination of as-

cending and de-

scending paths,

movement meas-

urement, rapid

mapping of targeted

More Frequent

Satellite Passes,

Monitoring of

Faster Slope

Instabilities PS

As above but only

works with stable

reflectors, very

expensive

As above with mm

accuracy movement

detection

Ground-based

As above, but can

monitor faster

movements, semi-

permanent instal-

lation, and

viewpoint is

required

As above, and can

pick up larger

movements

Optical Satellite Images

Landslide in-

ventory and

time series

analysis

10's cm to

10's m

Expensive, image

quality can be

poor due to cloud

cover

Landslide surveys,

large area coverage,

historical record

since 1990's, change

detection, rapid

mapping of targeted

areas

Higher resolu-

tion satellites to

be deployed,

more satellites

will increase

coverage and

frequency

Weather Radar

Precipitation in-

tensity, early

warning from

rainfall intensi-

ty and accumu-

lation

Depending

on calibra-

tion km

Calibration is re-

quired

Helps map spatial

distribution of

weather

Enhanced map-

ping of weather

systems,

cheaper sys-

tems

Others Thermal, IR Low Resolution

Vegetation Classifi-

cation, Water Con-

tent, Change Detec-

tion


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Figure 5: The Remote Sliding Force Monitoring System.

Figure 6: High-Resolution Sensor and Data Acquisition and Transmission.

The monitoring system was applied to the Nanfen Open Pit Iron mine in Liaoning Province, China. It is the larg-

est open pit iron mine in Asia, covering an area of approximately 3 km long by 2 km in wide. In 2008, the mine

underwent a series of expansions, and the current production capacity is about 10 million tons of iron ore per

year [64, 71]. The mine consists of benches, each about 24 m high with an average slope angle of 46° on the

western side (Figure 7: ). The total height of the slope is about 756 m.


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Figure 7: Aerial View of Nanfen Open Pit Iron Mine.

The Nanfen open pit iron mine is located in the south wing of Heibei mountain inversed anticline, and geology is

composed mainly of the Archean Anshan Group, Algonkian Liaohe Group, Sinian and Quaternary systems. The

slopes are typically intercepted by 5 to 6 joint sets, two of which have potential impact on stability on the

heading side: (1) Major Join Systems: these joints have an average attitude of 295/48, and the value of the

roughness of the joints is about 2-4 according to the Barton [5], classification system. These joints form the main

potential sliding surfaces on the heading side; (2) Secondary Joints Systems: these joints have an average attitude

of 291/13, spreading extensively, and are characterized by small dip angles.

Twenty-eight sets of remote monitoring systems were installed at various re-selected locations in the mine as

shown in Figure 8:

With continuous monitoring, the system was continually field calibrated and refined, which led to the develop-

ment of a strong predictive tool. In October 2010, at monitoring location No. 334-4 (see Figure 8: ), large varia-

tions (increase) in sliding force were measured, and on October 5, 2011, a large slope failure occurred. The spa-

tial distribution of monitoring points where the instability took place is shown in

Fig.

Fig. 10a shows the monitoring stations and the geometry of the landslide that ultimately occurred. Fig. 10b

shows the continuous measurements of sliding force, the cumulative amount of mining, as well as rainfall data in

the days prior and post the landslide event. When combined, these provide an early warning system based on

which actions, such as evacuations can be taken.


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Figure 8: Outline of Nanfen open pit iron mine: sliding force monitoring points on the slope.

Figure 9: Spatial Distribution of Landslide at Nanfen Mine.

a)

(b)

11-1005滑坡

未开采的矿产资源

i

j A

646

g h

cd e f

a b

A’ 274

Scale

0 20 40m

(c) a b c d e f g h i j

646

562 A

250° 526

430

370

358

346

334

274

322 10-0731滑坡

11-1005滑坡

回采区


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b)

Figure 10: Early Warning System for Nanfen Open Pit Mine. a) Monitoring Stations and the Geometry of the

Landslide. b) Measurements of Sliding Force, Cumulative Mining, and Rainfall Data Prior and Post Landslide.

A more detailed timeline of events prior to the occurrence of the 5 October 2011 landslide is shown in Table 2.

Table 2. Timeline and Observations Prior to 5 October 2011 Landslide at Nanfen Mine.

Date Laboratory Observations Field Observations Actions

30 September

2011

Monitored forces at location

No. 334-4 are very small,

and monitoring curve tends

to a straight horizontal line,

indicating that the slope was

relatively stable

None None

1 October

2011

Sliding forces show a clear

upward trend.

Field investigations did

not find any surface

cracks and other signs of

slippage

None

2 October

2011

Sliding forces show a signif-

icant increase particularly at

locations No. 322-334

Micro-fissures and

cracks appear on the

surface of the rock slope

surface with a width of

15-40 cm and a length

1.5-5 m

None

3 October

2011

Sliding forces continue to

increase

Cracks developed to be-

come longer and wider,

with a width of 25-60

cm and a length of 3-9

m.

Mining activities were

stopped, and personnel and

equipment were evacuated.

These measures are reflected

in the reduction of the cumu-

lative the mining (Fig. 10).

4 October

2011

Sliding forces, particularly

at location No. 334-4 con-

tinue to rise although at a

lesser rate

Cracks continue to ex-

tend and propagate

Mining activities continue to

be halted as the slope is still

deemed unstable

5 October

2011

Sliding forces at location

No. 334-4 reduce abruptly

from 1700 kN to 1400 kN

indicating slope rupture

Intense rainfall event,

(accumulated rainfall

31.8 mm in 3 hours)

A significant landslide

with height 36m and

length 50m

None

The measurement of sliding force in the rock mass served, successfully, as a timely warning system in the case

of the October landslide at the Nanfen Mine. Because of the continuous monitoring system, it was possible to


14


take decisive measures and present casualties and property losses. While it is true that there are economic oppor-

tunity costs to the cessation of mining activities, losses from the landslide occurring during mining operations

would have been far greater, possibly including loss of lives.

The high-resolution sliding force monitoring system developed at SKLGDU provides a rapid and accurate early

warning system for open pit mines. Monitoring sliding forces can better reflect the complex systems mines exist

in which include natural systems, such as rainfall, erosion, and others, as well as manmade systems, such as ex-

cavation and blasting as the mine is developed.

Future Trends

With increasing global satellite coverage and access to open and in many occasions free data, remote sensing

techniques are bound to play a more significant role in slope stability risk assessments in the future. Technologi-

cal advancements will lead to the development of cheaper and more reliable sensors that can be deployed as part

of monitoring systems. Increasing computing power and cheap storage are also likely to play more significant

roles in the future enabling the acquisition, storage, and analysis of big data. In the future, the risk assessment

and management procedure described in this paper may become fully automated. Smart sensors can issue alarms

based on the results of the risk assessment. The properties of materials used in countermeasures can also become

self-adjusting based on the risk assessment. For example, the strength and stiffness of rock bolts can vary in re-

sponse to potential instabilities.

Conclusions

Decisions regarding mine operations, even which mines to open and close, are typically based on a cost to bene-

fit analysis. Increasing urgency for economical, technological and environmental mining operations have placed

unprecedented demand on engineers to design mines on a scale not attempted before. Modern management

methods, such as the one described in this paper, can and should be used to systematically access and manage the

risks associated with slope instabilities. Warning systems are effective tools to mitigate slope instability risks in

mines. As remotely sensed data become more widely available and cheaper to collect, these techniques are

bound to play a more significant role in monitoring systems of the future. The Nanfen Iron Ore case study

showed that warning systems can be successfully used to prevent substantial economic losses, and possibly save

lives.

References

1. Ang A, Tang WH (1975) Probability concepts in engineering planning and design. Vol 1, Basic

Principles, New York, John Wiley & Sons

2. Ang A, Tang WH (1984) Probability concepts in engineering planning and design. Vol. II-Decision,

risk, and reliability. John Wiley & Sons, Inc., New York, NY

3. Baecher GB (1972) Site Exploration: A Probabilistic Approach. Massachusetts Institute of Technology.

Ph.D. Thesis, 515 pp

4. Baecher GB (1978) Analyzing Exploration Strategies. In: CH Dowding (ed) Site Characterization and

Exploration, NSF Site Characterization Workshop, Northestern University

5. Baecher GB, Christian JT (2003) Reliability and statistics in geotechnical engineering. John Wiley and

Sons, West Sussex, England. 605 pp


15


6. Barton NR, Choubey V (1977) The shear strength of rock joints in theory and practice. Rock Mech, vol

10(1-2), pp 1-54

7. Chowdhury RN, Xu DW (1995) Geotechnical system reliability of slopes. Reliability Engineering and

Systems Safety, Northern Ireland, vol 47, pp 141-151

8. Christian JT, Ladd CC, Baecher GB (1994) Reliability applied to slope stability analysis. ASCE Jrnl. of

Geot. Eng. Vol 10, No 12

9. DiBiagio E, Kjekstad O (2007) Early warning, instrumentation and monitoring landslides. 2nd Regional

Training Course, RECLAIM II, 29th January–3rd February 2007

10. Ditlevesen O, Madsen HO (1996) Structural reliability methods. Wiley. 384 pp

11. Ditleveson O (1981) Uncertainty modelling. McGraw-Hill Inc., US. pp 410

12. Duncan JM (2000) Factors of safety and reliability in geotechnical engineering. Journal of Geotechnical

and Geoenvironmental Engineering, vol 126, pp 307-316

13. Einstein HH, Baecher GB (1982) Probabilistic and statistical methods in engineering geology, I. Prob-

lem statement and introduction to solutions. Rock Mechanics. Suppl 12, pp 47-61

14. Einstein HH (1988) Landslide risk assessment procedure. Proceedings of the Fifth International Sympo-

sium on Landslides, Lausanne, Switzerland, A.A. Balkema, Rotterdam, Netherlands, vol 2, pp 1075-

1090

15. Einstein HH (1995) Risk and risk analysis in rock engineering. Keynote Lecture, Swedish Rock Me-

chanics Day, Stockholm.

16. Einstein HH (1997) Landslide risk: systematic approaches to assessment and management. In: Cruden,

A. and Fell, R. (editors). Landslide Risk Assessment. Balkema, Rotterdam. pp 25-50

17. Einstein HH, Karam KS (2001) Risk assessment and uncertainties. Keynote Lecture. Proceedings of the

International Conference on Landslides - Causes, Impacts and Countermeasures. Davos, Switzerland,

2001.

18. Einstein HH (2002) Risk assessment and management in geotechnical engineering. 8th Port. Geotech-

nical Congress, Lisbon, pp 2237-2262

19. Einstein HH, Sousa RL (2007) Warning systems for natural threats. Georisk, vol 1(1), pp 3-20

20. Einstein HH, Karam KS, Sousa RL (2008) Reducing the risks associated with natural hazards. Interna-

tional Geological Congress, Oslo, Norway

21. El-Ramly H, Morgenstern NR, Cruden DM (2002) Probabilistic slope stability analysis for practice. Ca-

nadian Geotechnical Journal. vol 39, pp 665-683

22. El-Ramly H, Morgenstern NR, Cruden DM. (2006). Lodalen slide: A probabilistic assessment. Canadi-

an Geotechnical Journal. vol 43, pp 956-968

23. Gao L, Ge Y, Liu J (2001) A preliminary discussion on the slope sliding mass in the bottom wall in

Nanfen Iron Surface Mine and its control measures. Mining Research Division. 2001; vol 21(6): pp 4–6

24. Goodman RE (1976) Methods of geological engineering in discontinuous rocks. West Publishing Co,

St. Paul, Minn

25. Goodman RE (1989) Introduction to rock mechanics, 2nd Ed, John Wiley & Sons, Inc, New York, NY

26. Goodman RE (1995) Thirty-fifth Rankine lecture: Block theory and its application. Geotechnique, Lon-

don, vol 45(3), pp 383-423

27. Goodman RE, Taylor RL (1967) Methods of analysis for rock slopes and abutments: a review of recent

developments. Failure and breakage of rocks, Fairhurst C (ed), Am. lnst. of Min., Metallurgical and Pet-

ro Engrs, New York, NY, pp 303-320

28. Griffiths DV, Fenton GA (2004) Probabilistic slope stability analysis by finite elements. J. Geotechnical

and Geoenvironmental Engineering, vol 130(5), pp 507-518

29. Hammond JS, Keeney RL, Raiffa H (2015) Smart choices: A practical guide to making better decisions

Hardcover. Harvard Business Review Press; Reprint edition 2015

30. Harr ME (1996) Reliability based design in civil engineering. Dover, New York, USA. 281 pp

31. Hasofer AM, Lind NC (1974) Exact and invariant second moment code format. Journal of Engineering

Mechanics, ASCE, vol 100(1), pp 111-121

32. He MC, Cui ZQ, Jiang YJ (1999) Study on 3S engineering analysis system of the slope stability in the

three gorges area. The Chinese Journal of Engineering Geology, vol 7(2), pp 112-117 (in Chinese)

33. He MC (2009) Real-time remote monitoring and forecasting system for geological disasters of land-

slides and its engineering application. Chinese Journal of Rock Mechanics and Engineering, vol 28(6),

pp 1081-1090 (in Chinese)

34. He MC, Tao ZG, Zhang B (2009) Application of remote monitoring technology in landslides in the

Luoshan mining area. Mining Science and Technology, vol 19(5), pp 609-614


16


35. He MC, Sousa, LR, Jili F, Zhigang T (2014) Monitoring of sliding forces for opens pits in China. Rock

Mechanics for Natural Resources and Infrastructure, SBMR 2014 Specialized Conference 09-13 Sep-

tember, Goiania, Brazil

36. Hendron, AJ, Cording EJ, Aiyer A K (1971) Analytical and graphical methods for the analysis of slopes

in rock masses. U.S. Army Engrg. Nuclear Cratering Group Tech. Rep. no 36, US Army Engineers Wa-

terways Experiment Station, Vicksburg, Miss

37. Hoek E, Bray JW (1977) Rock slope engineering. Inst. Min. Metall., London

38. Hoek E, Bray JW, Boyd JM (1973) The stability of a rock slope containing a wedge resting on two in-

tersecting discontinuities. Quarterly J. Engrg. Geol., vol 6(1), pp 1-55

39. Hoek E, Bray J (1981) Rock slope engineering, 3rd Ed., lnst. Min. Metall., London.

40. Hoek E, Kaiser PK, Bawden WF (1995) Support of underground excavations in hard rock. A. A.

Balkema, Rotterdam, The Netherlands

41. Howard RA, Abbas AE (2015) Foundations of decision analysis. Prentice Hall. First Edition. pp 832

42. Howard RA, Matheson JE (editors) (1984) Readings on the principles and applications of decision anal-

ysis. 2 vol, Menlo Park CA: Strategic Decisions Group.

43. Huang R, Pei X, Fan X, Zhang W, Li S, Li B (2012) The characteristics and failure mechanism of the

largest landslide triggered by the Wenchuan earthquake, May 12, 2008, China. J. Landslides, vol 9, pp

131-142

44. Hutchinson J (2008) Rock slide hazards: detection, assessment and warning. Presentation at the 33rd In-

ternational Geological Congress, Oslo, Norway, 2008

45. Jaeger JC (1971) Friction of rocks and the stability of rock slopes. Geotechnique, London, vol 21(2), pp

97 -134

46. John KW (1968) Graphical stability analysis of slopes in jointed rock. J. Soil Mech. and Found. Div.,

ASCE, vol 94(2), pp 497-526

47. Karam, KS (2005) Landslide risk assessment and uncertainties. Massachusetts Institute of Technology.

Ph.D. Thesis, 768 pp

48. Karam KS, Karam JS, Einstein HH (2007a) Decision analysis applied to tunnel exploration planning. I:

Principles and case study. ASCE Journal of Construction Management, vol 133(5), pp 344–353

49. Karam KS, Karam JS, Einstein HH (2007b) Decision analysis applied to tunnel exploration planning. II:

Consideration of uncertainty. ASCE Journal of Construction Management, vol 133(5), pp 354–363

50. Keeney RL, Raiffa H (1976) Decision analysis with multiple conflicting objectives. John Wiley and

Sons, New York

51. Lacasse S, Nadim F (1996) Uncertainty in characterizing soil properties. ASCE Geotechnical Special

Publication, No 58

52. Lacasse S, Nadim, F (2009) Landslide risk assessment and mitigation strategy. In: Sassa K, Canuti P

(eds) Landslides-Disaster risk reduction. Springer-Verlag, Berlin Heidelberg. pp 31-61

53. Lacasse S, Nadim F, Høeg K, Gregersen, O (2004) Risk assessment in geotechnical engineering: The

importance of engineering judgment. The Skempton Conference, Proc. London UK. Vol 2, pp 856-867

54. Lacasse S, Eidsvik U, Nadim F, Høeg K, Blikra LH (2008) Event tree analysis of Åknes rock slide haz-

ard. IV Geohazards Québec, 4th Canadian Conf. on Geohazards. pp 551-557

55. Londe P, Vigier G, Vormeringer R (1969) The stability of rock slopes, a three-dimensional study. J. Soil

Mech. and Found. Div., ASCE. vol 95(1), pp 235-262

56. Low BK, Tang WH (1997) Efficient reliability evaluation using spreadsheet. J. Engrg. Mech., ASCE,

123(7)

57. Low BK, Gilbert RB, Wright SG (1998) Slope reliability analysis using generalized method of slices.

Journal of Geotechnical and Geoenvironmental Engineering. ASCE vol 123(6), pp 498-505

58. Nadim F (2007) Tools and strategies for dealing with uncertainty in geotechnics. Probabilistic Methods

in Geotechnical Engineering, vol 491, pp 71-95

59. Nadim F, Lacasse S (2012) Strategies for mitigation of risk associated with landslides. Landslide Disas-

ter Risk Reduction. Berlin: Springer Berlin Heidelberg

60. Peck RB (1969) Advantages and limitations of the observational method in applied soil mechanics. 9th

Rankine Lecture, Géotechnique, vol 19, pp 171-187

61. Pratt JW, Raiffa H, Schlaiffer R (1965) Introduction to statistical decision theory. McGraw Hill.

62. Raiffa H, Schlaifer RL (1964) Applied statistical decision theory. Harvard Business School, Cambridge,

MA.

63. Riela J, Urza A, Christian JT, Karzulovic A, Flores G (1999) Sliding rock wedge reliability analysis of

Chuquicamata mine slopes. XI Pan American Conference on Soil Mechanics and Geotechnical Engi-

neering, Foz do Iguacu, Brazil.


17


64. Shuangwei L, Ketong L (2001) Study on the causes of the local landslide in Nanfen Open Pit Iron Mine.

Benxi Steel Technology 2001, vol 1, pp 2–7

65. Stael von Holstein CAS (1974) A tutorial in decision analysis. Readings in Decision Analysis. Howard,

Matheson, Miller (eds.) SRI, Menlo Park

66. Terzaghi K (1961) Engineering geology on the job and in the classroom. Journal of the BSCE, April

1961

67. Vanmarcke EH (1977) Reliability of earth slopes. J. Geotech. Engrg., ASCE, vol 103(11), pp 1247-

1266

68. Veneziano D (1974) Contributions to second moment reliability. Res. Rep. no. R74-33, Dept. of Civ.

Engrg., Massachusetts Inst. of Technology. Cambridge, Mass

69. Willye DC, Mah CW (2004) Rock slope engineering: civil and mining. CRC Press, London & New

York. pp 456

70. Wittke W (1990) Rock mechanics: theory and applications with case histories. Springer-Verlag New

York. Inc. New York, NY

71. Yang J, Tao Z, Li B, Gui Y, Li H (2012) Stability assessment and feature analysis of slope in Nanfen

Open Pit Iron Mine. International Journal of Mining Science and Technology, vol 22, pp 329–333

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