Stead Paper

�Slope Stability Santiago Chile, November 2009

Rock slope characterization for large open pits and high mountain slopes

Characterization of rock slopes has undergone considerable development during the last decade reflecting not only the availability of new methods of data collection but also the demands faced in designing and assessing the stability of large rock slopes up to 1000m in height. Many of the existing suggested methods for characterizing rock masses were developed based on the stability assessment of rock cuts or individual/multiple benches and hence were biased toward the kinematic and limit equilibrium analyses of slopes less than 100m in height. As numerical modelling of rock slopes became increasingly common the need for the derivation of rock mass strength and deformation parameters has increased. Recent advances in the area of synthetic rock mass modelling bring with them requirements for rigorous methods of characterizing both rock slope discontinuities and rock mass properties. This paper describes recent innovations in rock slope characterization emphasizing terrestrial LiDAR and photogrammetry. Remote sensing experiences for large open pit slopes and high mountain slopes illustrate the importance of observation and measurement scale bias. Derivation of data for discrete fracture networks (DFN’s) for both block stability assessment and rock mass characterization is discussed and the need to consider inherent bias in data collection methods highlighted.

Abstract

D. Stead, M. Sturzenegger and

F. Gao.

Resource Geotechnics Research

Group

D. Elmo

Golder Associates

E. Eberhardt

Geological Engineering

University of British Columbia

INTRODUCTION

The use of digital mapping techniques in the characterization of open pit mine slopes, highway rock cuts and landslides is now routinely undertaken using both airborne and ground based remote sensing. One of first uses of digital mapping for investigating mine slope failures was by Coggan et al. (1) who described the successful use of an MDL Quarryman laser scanner as early as 1999 in the monitoring of the retrogression of a landslide in a hydrothermally altered granite - china clay quarry slope. Today, ground-based LiDAR and photogrammetry are but a few of a larger suite of digital remote sensing tools that are being increasingly used in rock slope characterization. It is the authors’ intention in this paper to describe the state-of-the-art in digital remote rock mass characterization focusing on LiDAR and photogrammetry. Although conventional rock mass characterization field mapping techniques will not be discussed in detail the critical role that these techniques play, and will continue to play, cannot be overemphasized.

Read and Ogden (2) identified three types of fundamental knowledge to be addressed in predicting slope failure: that of the strength, geological structure and deformability of the potentially unstable rock mass; that leading to understanding of the failure mechanism(s); and that on how best to analyze the failure mechanism(s) for stability. This paper treats the first two as an inherent component of rock slope characterization with the objective of improving design in large rock slopes. A key question before introducing rock slope characterization techniques is what is meant by characterization. This is not straight forward as field characterization of rock slopes predominantly emphasizes the recording of parameters according to ISRM suggested methods published in the late 1970’s. These were developed largely around discontinuity line surveys at bench or highway cut mapping scales. Since the development of these methods, the scale of open pit slopes, engineered rock slopes and studied rock slides has increased significantly, from slope heights in the order of 100’s of metres to those approaching 1000m. Clearly the demands placed on rock mass characterization methods has increased as has the challenge to accurately represent rock mass strength in advanced numerical models. It is the tenet of this paper that the use of realistic, sophisticated, 3-D rock mass modelling techniques, including those that account for brittle fracture, demands substantial improvement in rock mass characterization particularly with respect to such elusive factors as persistence, joint intensity and rock bridges.

� Santiago Chile, November 2009 Slope Stability

TECHNIQUES OF ROCK SLOPE CHARACTERIZATION

True and realistic rock slope characterization requires an “integrated rock mass characterization approach” combining borehole logging, conventional face mapping and airborne or ground-based remote sensing techniques. This should be aimed at high risk large open pit or natural slopes, producing a virtual 3-D rock slope geological model suitable for VRML data presentation. Obviously for smaller or lower risk slopes a fully integrated characterization approach is not cost effective, although due diligence for design still requires that the 3-D fracture network be characterized as realistically as possible. It is important to clearly understand not only the advantages of individual rock slope characterization techniques but also their limitations and to combine methods in order to reduce uncertainty in the rock slope model.

Borehole TechniquesAlthough the engineering logging of core has not changed substantially since the early development of guidelines, numerous new

parameters are now recorded to comply with developments in rock mass classification and rock mass strength characterization, including those for providing input for numerical models. There have been considerable efforts to ensure uniformity in core logging, and core orientation methods in particular have seen dramatic improvement. Developments in acoustic televiewers and optical borehole cameras over recent years have led to improved characterization of poor ground, production of virtual cores, and recently, the characterization of breakouts for in-situ stress characterization for proposed open pits; e.g. Olympic Dam, Australia (3). Improved borehole logging techniques have been used to good effect in open pit mining in the production of data for discrete fracture networks; e.g. Diavik mine, Canada (4). For large rock slides, such as the Frank Slide, Alberta, acoustic televiewer logs have shown the considerable dilation and the damaged nature of the rock behind the slide surface (5). The optimal geostatistical integration of this data source with newly developed remote sensing and field mapping techniques is an important area for ongoing work.

GeophysicsGeophysical techniques have been used successfully in several recent rock slide projects. At the site of the 1991 Randa rockslide in

Switzerland, surface and borehole georadar techniques were used by Spillmann et al. (6) to resolve the geometries and position of active fractures at depth within the moving mass. Similarly, 3-D seismic tomography was used to delineate a zone of remarkably low P-wave velocities (~1100m/s) within and adjacent to the geodetically defined slide boundaries, characteristic of low-rock mass quality with intensive fracturing (6). Electrical resistivity tomography and seismic were successfully used in the investigation of La Clappiere by Jomard et al. (7). Their results were useful in evaluating the slip surface geometry, fracturing, weathering and groundwater movement. Leucci (8) investigated rock mass characteristics behind a sub vertical rock cliff in Italy using seismic refraction tomography, electrical resistivity tomography (ERT) and ground penetrating radar (GPR). Low velocity zones were again correlated with poor quality rock, and fracture systems were correlated using ERT and GPR results. Deparis et al. (9) combined ERT, GPR and LiDAR to provide a 3-D representation of the rock mass quality behind limestone cliffs in France. Pernito (10) successfully combined 3-D GPR, ground-based LiDAR and conventional scanline surveys to provide input for probabilistic planar and wedge stability analyses for volcanic rock slopes. These recent studies suggest that geophysical surveys are playing an increasing role in rock slope investigations.

LiDARAirborne LiDAR and DEM’sThe use of airborne LiDAR for rock slope characterization is common in natural rock slopes but relatively rare in open pit mining.

Oppikofer and Jaboyedoff (11) and Sturzenegger et al. (12) show the use of airborne LiDAR in the characterization of discontinuity orientations at the Aknes and Frank slides, respectively. Using the COLTOP3D code together with LiDAR and/or a digital elevation model (DEM), it is possible to determine the orientation (dip, dip direction) of slope facets corresponding to highly persistent discontinuities. These are colour-coded by COLTOP3D using a hue-saturation-intensity scheme (Figure 1). This technique has been applied to numerous inaccessible high rock cuts to characterize discontinuities, with Grenon et al. (13) demonstrating a similar application to show slope distribution in an open pit mine.

Ground-Based LiDARGround based LiDAR has been used extensively in discontinuity and rock mass characterization of highway cuts (14, 15), high mountain

rock slopes and surface mines. Numerous LiDAR scanners exist with varying range and resolution; however most applications are close range with few cases reported of scans over 1km. Figure 2 shows the combined use of ground-based and airborne LiDAR in characterization efforts at Turtle Mountain, Alberta. These techniques provided valuable information on joint set orientations and failure mechanisms. Ground-based LiDAR can provide excellent short range scans of open pit slopes where accessibility exists (e.g. Figure 3). Where benches are inaccessible, such as in larger open pits, long-range photogrammetry may be more appropriate. Although LiDAR scanners with ranges of several kilometres exist, published reports of their use are more limited. Geotechnical methods of processing LiDAR data include the use of codes such as Split-FX (www.spliteng.com/) and Polyworks (Innovmetric). From these, a wide range of discontinuity data can be derived as described in Sturzenegger and Stead (16, 17) and Tonen and Kottenstette (18).


Figure 1 - Use of COLTOP3D for Joint Set Characterization in a Steep, Inaccessible Cliff at Hegguraksla (Western Norway). After Oppikofer and Jaboyedoff (11).

Figure 2 - Combined use of Airborne LiDAR for Natural Rock Slope Discontinuity Mapping, Turtle Mountain Alberta. After Sturzenegger et al. (12).

Figure 3 - Bench Scale LiDAR at Palabora Open Pit.


Ground-Based PhotogrammetryThe use of ground based photogrammetry in both surface and underground mining is now well accepted. Poropat and Elmouttie (19, 20)

and Elmouttie et al. (21) demonstrate the application of photogrammetry using Sirovision in both the automated structural mapping of rock faces and in the derivation and use of data for structural modelling of open pit mines using Siromodel. Mathis (22) provides a summary of practical application of terrestrial photogrammetry in pit slope design and structural analysis at the Jericho Diamond mine in northern Canada. In addition to demonstrating the use of the AdamTech software in rock slope characterization at the bench and interramp scale, results are presented in the next section that provide a clear description of the advantages and disadvantages of digital photogrammetry. A particularly important paper to those contemplating the use of terrestrial photogrammetry and LiDAR, is that by Lyman et al. (23) discussing uncertainty in rock mass joint characterization.

Close and Long Range Ground-Based LiDAR and Photogrammetry ImageryConventional close-range photogrammetry is usually taken at distances from 10’s to 300 metres with focal lengths from f = 20 to

150mm. At distances typical for large open pits where access to benches is no longer possible, it becomes increasingly necessary to use medium to long-range photogrammetry. Ranges of up to 2km are possible when using focal lengths of f = 400mm. An alternative to this with great future potential is to use helicopters, unmanned aerial vehicles, etc., to gain closer access. Figure 4 shows a photogrammetric model of the surface of a rock slide which closed the Sea-to-Sky corridor near Vancouver, Canada, a key route for the 2010 Winter Olympics. This could not be imaged conventionally due to a lack of space between the road and its steep drop-off to the sea. The solution in this case was to use a 100mm lens and to shoot from a boat offshore. The detail obtained in the images is clear.

Figure 5 shows an f = 20mm photogrammetric model of the Palabora open pit, South Africa. This pit was imaged completely using f = 50, 100 and 200mm focal lengths. Complete sections of the pit wall were also imaged using f = 400mm (Figure 5). A good comparison of discontinuity orientations was obtained between the photogrammetric data and previous conventional mapping (25). Similarly, Figure 6 shows a digital photogrammetry model of the Frank Slide, Alberta constructed using f = 400m imagery from about 2km, (17). This imagery was not possible using conventional LiDAR and close-range photogrammetry due to the line of sight distance involved.

Figure 4 - Ground-Based Photogrammetry Taken from an Offshore Camera Station to Image an Inaccessible Rock Slide that Closed the Sea-to-Sky Highway Corridor.

Figure 5 - Ground-Based Photogrammetry of Palabora Open Pit: f =20mm Model for Planning (Top Left), and Long Range f = 400mm from 1.6 km Model (Bottom Right) with Typical Derived Discontinuity Data Including Orientation and Trace Length.


Figure 6 - Turtle Mountain Ground-Based Photogrammetry: f =50mm Model from over 2km (Top Right), and Close- Up of Peak Using f = 400mm Lens (Bottom).

Rock Slope Characterization for Discrete Fracture Networks (DFN’s)The use of DFN’s in rock slope design has recently received a considerable amount of research focus. In essence there are two very

different uses for DFN’s, one as an input data for kinematic block theory approaches and the other as a basis on which to derive synthetic rock mass, SRM, models (e.g. 24). Numerous DFN codes have been used to date in rock slope analysis including Fracman (5), Stereoblock, (13), Resoblock, Siromodel (23) and 3FLO (24). Although DFN’s can be readily generated from LiDAR or photogrammetry (e.g. Figure 7), and can provide key input for numerical analyses, there is often little discussion in the literature on the inherent errors involved in the production of the DFN. The required parameters for the generation of a DFN model are discontinuity orientation distribution, trace length distribution and fracture intensity. These parameters can be directly obtained from both ground-based laser scanning point clouds and photogrammetric models (Figure 7a-c) and incorporated into software like FracMan (Figure 7d). Figure 7 and Table I show the parameters used for the generation of a DFN model of a bench scale rock cut in quartz diorite.

Figure 7 - Incorporating Ground-Based LiDAR Data into DFN Models: (a) Point Cloud of Bench Scale Rock Cut; (b) Digital Discontinuity Mapping; (c) Discontinuity Orientation and Persistence Distributions, (d) FracMan Model.


Discontinuity Set Mean DipMean Dip Direction

Fisher’s KMean Trace

LengthTrace Length Distribution

P32

[°] [°] [m] [m2/m3]

J1 79 299 71 2.26 Exponential 2.71

J2 75 177 43 1.7 Exponential 1.23

J3 33 17 98 9.32 Exponential 0.17

Bootstrap n/a n/a 150 3.2 Exponential 1.9

Table I - Parameters Used for the Generation of a Bench Scale Rock Cut DFN Model

The authors emphasize that the uncertainty involved in the production of a DFN must be understood. Although face mapping using both conventional scanline techniques and remote sensing are able to produce excellent data quality for rock slope kinematic analyses, that used to provide input for numerical modeling, in particular SRM models (24), requires high quality integrated rock mass characterization incorporating borehole, remote sensing (LiDAR/photogrammetry) and conventional engineering field surveys. It is the authors’ experience that characterization using one method alone rarely produces the required quality of data due to the dependence of persistence, termination and intensity information on the method of data collection and/or observation scale effects. As an example, long range photogrammetric surveys using f =100 and 400mm focal length lenses can produce excellent discontinuity orientation data comparable with other techniques. However as the distance from the slope to the measurement location increases, there is generally a marked reduction in the intensity recorded in jointing through low persistence joints being truncated, Figure 8.

Figure 8 - Equivalent Trace Length Distributions of Discontinuities Mapped for a Pit Wall from a Distance of >1.5 km Using both f =100 and 400mm Lenses. After (25).

The variation in rock slope stability or SRM strength over the range of stochastically generated DFN realizations has received little attention. In most published studies, only one DFN is used and there is very little discussion, if any, of the input data. Figure 9 shows a preliminary relationship between the P

21 estimate of areal fracture intensity and the ground point spacing attained through the use of

varied focal length lenses/distances; clearly the observation scale has an important influence. This can significantly influence the number of blocks formed by the DFN. In contrast bench scale observations result in a bias in the recording of high persistence discontinuities (i.e. > than bench height), which in turn has a marked effect on a slope kinematics analysis. The spatial variations of discontinuities within a rock slope or open pit mine, i.e. geostatistics, should be incorporated into the production of the DFN with full consideration given to the structural geology setting; choice of the correct DFN model is at the user’s discretion. Most published uses of DFN focus specifically on rock mechanics data to generate the DFN (discontinuity dip, dip direction dispersion, joint intensity measures P

10, P

21, trace length and

termination characteristics). The engineer should not, however, ignore the importance of geological/structural changes in the rock mass and its importance to the DFN, the resulting kinematic analysis and/or synthetic rock mass strength. Mathis (22) states the potential advantages of using photogrammetry of successive open pit cuts to allow the changes in discontinuity characteristics to be mapped; this is an important area for future research.


Figure 9 - Graph Illustrating a Decrease in P21 value as Ground Point Spacing Increases.

DFN Modelling and Influence of Observation Scale Amongst the statistically defined spatial and geometric properties required to build a DFN model, fracture length is certainly one of

the most critical, particularly if the DFN results are to be used for kinematic slope stability analysis or for a more complex stress-based geomechanical analysis (e.g. SRM modelling). Manually derived field observations of fracture size (length) typically include a fracture size threshold below which no measurements are taken. Similarly, photogrammetric techniques for discontinuity mapping have been shown to introduce an observation scale bias depending on the use of varying focal length lenses over long ranges.

For a given rock mass, the probability density functions describing fracture length covers a wide spectrum and corresponds to the natural fracture intensity. Because the introduction of a fracture cut-off acts to remove fractures from the rock mass volume, the fracture intensity has to be varied accordingly. To demonstrate this, DFN models were developed with varying fracture cut-offs relative to different observational scales using Fracman. Input was based on a highly dispersed set of fractures with a log-normal distribution of lengths (mean = 5m, std. dev. = 5m). As shown in Table II, the assumed initial fracture frequency (P10) progressively decreases as the fracture cut-off is increased. This then influences the block forming potential. Figure 10 shows the geometry of the DFN model generated: a cylindrical volume 20m high and 60m in radius. A Mohr-Coulomb criterion was used to define the shear strength properties of the fracture surfaces (cohesion = 0kPa, friction angle = 30o). Figures 11 and 12 show that by introducing the fracture cut-off, the number and total volume of the fully formed blocks is significantly impacted. Clearly, for a large fracture cut-off the same DFN would respond in a much different manner to any imposed loading conditions, and in particular failure would be characterized by a much greater degree of intact rock fracturing.

Fracture cut-off (m)Average Fracture Frequency (P

10) used for

calibration of the DFN model

0.00 3.10

2.50 2.05

3.75 1.46

5.00 1.04

7.50 0.56

8.75 0.42

10.00 0.32

Table II - Relationship between Fracture Cut-Off and Fracture Frequency, Assuming a Log Normal Distribution for the Fracture Length.

Figure 10 - Model Set-Up. The DFN Model is Generated within a Cylindrical Volume of Dimensions 20m High and 60m Radius.


2.5m Fracture Cut-Off 3.75m Fracture Cut-Off

5m Fracture Cut-Off 7.5m Fracture Cut-Off

Figure 11 - Formed Blocks from Selected DFN Models with Varying Fracture Cut-Off Length. Green and Red Indicate Stable and Unstable (FoS<1) Blocks, Respectively.

Figure 12 - Blocks Formed and Total Block Volume with Varying Fracture Cut-Off.

Incorporation of Digital Mapping Data into Numerical Methods of Slope AnalysisGround based LiDAR and photogrammetry have significant potential as data sources for rock slope analysis. Generation of both 2-D

sections and 3-D slope DEM’s for import into numerical codes is possible. Figure 13 shows the generation of a 3-D distinct element model from LiDAR data at the rock cut (bench) scale. Figure 14 shows how photogrammetric/LiDAR models of a large open pit can be used to generate a FLAC3D model using Rhino/Kubrix pre-processing software. The release of a 64bit version of Rhino will allow similar 3-D model generation from point clouds with more limited truncation of data, although errors due to non-slope data require careful scrutiny.

Figure 13 - Generation of 3DEC Model from LiDAR at Bench (Rock Cut) Scale.


Figure 14 - 3-D Numerical Model of a Large Open Pit Generated from a Photogrammetric Model.

CONCLUSIONS

This paper has attempted to demonstrate the use of remote sensing data in rock slope characterization of large mountain slopes and large open pits. It has emphasized the increased demands for integrated rock slope characterization using conventional field methods and remote sensing techniques (including geophysics). Particular attention has been given to the increasing demands that will be placed on rock mass characterization for the production of DFN’s to be used in SRM analysis; these are not trivial and require a clear understanding of the limitations of the tools being used. The integrated use of prism monitoring, ground-based LiDAR, photogrammetry, slope stability radar and microseismicity in addition to satellite-based methods (e.g. InSAR) have found increased use over the last 5 years in both large open pits and in high mountain, high risk, rock slopes. It is emphasized that these techniques provide important slope monitoring data, but that they have been underutilized with respect to furthering our understanding of the complex interplay between geological structures and rock mass quality in the failure mechanisms that develop. Accordingly, new rock slope characterization techniques are ideally positioned to provide the necessary input and constraints for the sophisticated 3-D numerical models now being developed. Most importantly these new techniques must be used in an integrated and coherent manner with a clear understanding of measurement bias and limitations and the uncertainty that this may entail.

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�0 Santiago Chile, November 2009 Slope Stability

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