slope stability risk management in open-pit mines
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Slope stability risk management in open pit mines
Conference Paper · December 2015
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Slope stability risk management in open pit mines
Karam, K.S.
1,2; He, M.C.
3 and Sousa, L.R.
3,4
1Masdar Institute of Science and Technology,
2Sarooj Construction Company, Abu Dhabi, United Arab Emirates.
kkaram@masdar.ac.ae. 3State Key Laboratory for Geomechanics and Deep Underground Engineering, University of
Mining & Technology, Beijing, China; 4University of Porto, Portugal
Abstract
The stability of slopes in open pit mines is an issue of great concern because of the significant detrimental
consequences instabilities can have. To ensure the safe and continuous economic operation of these mines, it is
necessary 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, expensive, 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
1. 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 because
processes such as seepage concentrate the ore along a fault, which then passes through the pit. It is not uncommon to
find strata on opposite sides of the fault dipping in different directions and having different geotechnical properties.
Excavation usually takes place in a series of benches, each of which may be many meters high, so the geotechnical
engineer must evaluate the stability of individual benches as well as the overall slope. Since excavating and
disposing of material is a major cost in operating the mine, the simplest way to hold down the cost of excavation and
disposal is to reduce the volume that needs to be excavated. This implies, on the one hand that the slopes of the pit
be as steep as possible, but on the other hand, the steeper the slopes, the greater the danger of slope failures. Slope
instabilities can have detrimental consequences, both in terms of economic damage, and sometimes even loss of life.
To ensure the safe and economic operation of these mines, it is necessary to systematically assess and manage slope
stability risk. This paper presents a framework based on decision theory by which risk can be systematically
assessed and managed. 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 management 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.
2. Decision Analytical Approach to Slope Stability Risk Assessment and Management
2.1. Definitions
Risk can be expressed in many different ways. The definitions used in this paper are based on the ISSMGE
recommended 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 to 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 of time
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
iCu is the utility of consequences iC , defined as a function that describes a decision maker’s relative preferences
between attributes
2.2. Framework
Decision analysis provides a framework by which complex decision problems can be simplified, allowing one to
more clearly make decisions based on alternatives (Hammond et al. (2015).; Howard and Abbas (2015)). Figure 1 is
a graphical representation of decision making under uncertainty as proposed by Raiffa and Schlaifer (1964), Pratt et
al. (1965) and Stael von Holstein (1974). 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. The updating cycles can, amongst other things, represent the observational method in geotechnical
engineering (Terzaghi (1961), Peck (1969), Einstein (1988)).
Figure 1. The Decision Analysis Cycle (after Einstein (1988))
Figure 2. Decision Analytical Approach to Natural Threat Risk Assessment and Management (after Einstein (1997))
The steps or levels in the decision analytical approach (Figure 2) are described in the following. Since this paper is
related to risk management, 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 Figure 3.
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 manmade factors, such as blasting, can serve as triggers to slope instabilities
(Karam (2005), Huang et al. (2012)).
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 induction based on
interpretations of regional geology and geologic history, as well as deduction based on site investigations and
measurements. Typically, information may comprise traditional techniques such as maps, photographs, 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.
Level 2: Identify and Describe Threat
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, Goodman and Taylor (1967), John (1968), Londe et al. (1969), Hendron et
al. (1971), Jaeger (1971), Hoek et al. (1973), Goodman (1976, 1989, 1995), Hoek and Bray (1977, 1981), Wittke
(1990), and Wyllie and Mah (2004). The methods used include stereographic projection technique, engineering
graphics, and vector analysis.
Level 3: Determine Probabilities and Combine with Threat 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 Baecher
(1972, 1978), Einstein and Baecher (1982), Christian et al. (1994), and Lacasse and Nadim (1996). Einstein and
Karam (2001) 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 (1972), Einstein and Baecher (1982), and
Einstein (1995). 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 (Hasofer and Lind
(1974), Veneziano (1974), Ditlevesen (1981), Ditlevesen and Madsen (1995)), to full distribution probabilistic
analyses based on numerical techniques (Vanmarcke (1977), Low (1979), Low and Tang (1997), Griffiths and
Fenton (2004)). Examples of such studies for the stability of slopes in open pit mines include for example Riela et
al. (1999), Harr (1996), Duncan (2000), El-Ramly et al. (2002, 2006), Nadim (2007) 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 to life, health,
property, or the environment or the product of the hazard and the potential worth of loss. Risk is therefore obtained
as 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 (Ang and Tang (1975, 1984)), 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 Keeney and Raiffa (1976) and Howard and Matheson (1984), and Howard and Abbas (2015).
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:
(a) Passive countermeasures
Passive countermeasures reduce the vulnerability, resulting in a reduced risk 'R , where:
pasi
ii CuCuTCPTPR ]|['][' (2)
where:
]|[' TCP i is the reduced vulnerability
pasCu is the utility associated with the cost of the passive countermeasure
Other terms as defined for eq. (1).
Examples of passive countermeasures include galleries and nets.
(b) Active countermeasures
Active countermeasures reduce the hazard, resulting in a reduced risk 'R , where:
acti
ii CuCuTCPTPR ]|[]['' (3)
where:
][' TP is the reduced probability of threat
actCu is the utility associated with the cost of the active countermeasure
Other terms as defined for eq. (1).
Examples of active countermeasures include rock bolts, anchors, tie-backs and retaining structures.
(c) Warning systems
Warning systems mitigate risk by reducing consequences. They 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 affected areas and
lead to passive or active countermeasures.
Warning systems include the following components:
(i) Monitoring systems (data acquisition) that track a threat such as slope instability.
(ii) Communication systems (data transmission) that continually transit measurements to experts.
(iii) Models that predict (data analysis and interpretation) the threat in the short and long terms.
(iv) Alarm systems that transmit the warning signal(s) to the potentially affected parties efficiently and effectively
when a predefined alarm threshold is exceeded.
(v) Specific actions (procedures) that are implemented during the warning (evacuations routes, assembly points,
others), as well as those after the warning.
Figure 4 shows a flow chart for a warning system with the typical components involved.
Given the uncertainties involved in each of the components above, warning systems have a reliability associated to
them. That is to say that warning systems can on the one hand, fail to issue an alarm before a threat materializes, 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 systems to be flexible
and updatable both during an event/threat and as experience and technical capabilities increase.
Figure 4. Block Diagram for Warning System (after DiBiagio and Kjekstad (2007))
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. Designing
a monitoring program includes the following steps:
(a) Define the objectives of the monitoring program and select the type of measurements to be included, assigning
priorities to measurements.
(b) Decide on the measurement methods to be used, usually on a cost benefit basis, and select the appropriate
instruments.
(c) Determine the optimum instrumentation scheme by using, where possible, theoretical or empirical methods to
optimize the number and placement of instruments.
(d) Decide on the preferred method of data acquisition, e.g. manual or automatic recordings.
(e) Arrange for the proper installation, protection and marking of instruments and reference points in the field.
(f) Plan for the data flow, data management and analysis.
(g) Plan for the 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 related
to the fact that they can be localized. The use of remote sensed measurements is becoming more widespread 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 unstable slope in the areas
characterized by a high radar reflectivity. Near surface geophysical methods such as seismic, gravimetric, magnetic,
electric and electromagnetic, can be used to monitor hydrogeological phenomena, 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.
Table 1. Remote sensing technologies: strength and limitations, after Hutchinson (2008), Lacasse (2008), and Lacasse and Nadim (2012)
System Applications Resolution Limitations Strengths
(DEM from all) Future
Photogrammetry
Terrestrial
Joint survey, scarp
and change
detection cm to m,
depending
on scale
Field of view, image
resolution, requirement for
surveyed positions, vegetation
obscuring
Rapid, cheap, long term
record, stereoscopic; build
DEMs and see terrain
conditions
Digital imagery,
high resolution
scanners and
video Airborne
Landslide
inventory and time
series analysis
LiDAR
Ground based
(Static) Landslide
monitoring,
landslide mapping,
topography
extraction, joint
surveys, moisture
detection
mm to cm
Humidity, distance limitations,
angular limitations,
reflectivity, vegetation
obscuring, Field of view
Multi-return LiDAR allows
earth model, very high
accuracy, high rate of
acquisition, perspective
views possible
Cost effective,
signal
processing
improvements,
possible
automated
feature
extraction
Ground based
(Mobile)
Airborne
(Fixed Wing)
Airborne
(Helicopter)
InSAR
Standard
Movement
detection and
monitoring, time
series
displacement, HR
movement
detection (ground
based)
mm
Visibility and shadowing,
need reflectance, doesn't
penetrate vegetation, limited
to slow movements
Large area survey, long term
monitoring, return frequency
affects use, comparison or
combination of ascending
and descending paths,
movement measurement,
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 installation and
view point is required
As above, and can pick up
larger movements
Table 1 (continued). Remote sensing technologies: strength and limitations, after Hutchinson (2008), Lacasse (2008), and Lacasse and Nadim (2012)
System Applications Resolution Limitations Strengths
(DEM from all) Future
Optical Satellite Images
Landslide
inventory 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
resolution
satellites to be
deployed, more
satellites will
increase
coverage and
frequency
Weather Radar
Precipitation
intensity, early
warning from
rainfall intensity
and accumulation
Depending
on
calibration
km
Calibration is required Helps map spatial
distribution of weather
Enhanced
mapping of
weather
systems,
cheaper systems
Others (Thermal IR, etc)
Low
Resolution
Vegetation Classification,
Water Content, Change
Detection
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 going 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 Karam et al. (2007a, 2007b), and for landslides in
Einstein et al. (2008).
3. 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 (He et al. (1999), He et al. (2014)). The basic principles of the
monitoring system are shown in Figure 5. A set of high-resolution sensors are installed at pre-selected measurement
locations, which are then connected to a data acquisition and transmission system, as shown in Figure 6. With small
radio 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 locations
is analyzed, and the stability of the entire slope system is updated based on these measurements (He et al. (2009)).
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 largest
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
(Lie et al. (2001), Shuangwei and Ketong (2001), Yang et al. (2012)). 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.
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 and Choubey (1977) 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.
Figure 8. Outline of Nanfen open pit iron mine: sliding force monitoring points on the slope
With continuous monitoring, the system was continually field calibrated and refined, which led to the development
of a strong predictive tool. In October 2010, at monitoring location No. 334-4 (see Figure 8), large variations
(increase) in sliding force were measured, and on October 5, 2011, a large slope failure occurred. The spatial
distribution of monitoring points where the instability took place is shown in Figure 9.
Figure 9. Spatial Distribution of Landslide at Nanfen Mine
Figure 10 shows the monitoring stations and the geometry of the landslide that ultimately occurred.
Figure 11shows 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.
(b)
i
cd e f
a
Scale
(c)
b c d e f g i j
250°
Figure 10. Monitoring Stations and the Geometry of the Landslide
Figure 11. 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 November 2011 landslide is shown in Table .
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
significant increase particularly
at locations No. 322-334
Micro-fissures and three 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
The three cracks developed to
become 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
reduction of the cumulative the
mining (Figure 11).
4 October
2011
Sliding forces, particularly at
location No. 334-4 continue to
rise although at a lesser rate
Cracks continue to extend and
propagate.
The footwall at stope at 322-
334 platform was squeezed and
destroyed.
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
1700kN to 1400kN 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 take
decisive measures and prevent casualties and property losses. While it is true that there are economic opportunity
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 SKLGDUE 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 excavation
and blasting as the mine is developed.
4. 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. Technological
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 response to
potential instabilities.
5. Conclusions
Decisions regarding mine operation, and even which mines to open and close, are based on costs of operation,
amount of ore to be extracted, and the value of the product on world markets. The economy of scale has made large
scale open pit mines or underground large caving operations attractive for the recovery of low grade mineral
deposits. These massive operations have placed new demands on rock engineers to design slopes and underground
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 assess and manage the risks associated with slope instabilities. Warning
systems are effective tools to mitigate slope instability risks in mines. As remote sensed data becomes 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.
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