Slope Stability in High Stress and Hard Rock Conditions

Authors: Stacey, TR

DOI https://doi.org/10.36487/ACG_repo/708_Stacey

Cite As:
Stacey, TR 2007, 'Slope Stability in High Stress and Hard Rock Conditions', in Y Potvin (ed.), Slope Stability 2007: Proceedings of the 2007 International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 187-200, https://doi.org/10.36487/ACG_repo/708_Stacey

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Abstract:
Open pit mines are being planned to greater depths that will certainly involve high stress and hard rock conditions. These conditions are not satisfactorily accounted for by conventional stability analysis approaches that are based on the common mechanisms of failure involving planar, wedge and circular shear surface, and toppling. Failure mechanisms in high, hard rock slopes are much more complex than this. Progressive failure in hard rock slopes involves initiation and progression of failure along existing weakness planes, and initiation and progression of failure in intact rock. Mechanisms of slope failure behaviour, and the implications for slope stability analysis and slope design, are considered in this paper. It is concluded that what is required for robust stability evaluation and design is much better understanding of the rock mass, and methods of analysis that can model the rock mass and take into account variability in all of the geotechnical parameters. Better site investigations and modern methods of slope monitoring should supply considerable information regarding the understanding of the three dimensional rock mass. Owing to geotechnical variability, data must be expressed in the form of statistical distributions, and methods of stability analysis must therefore be probabilistic rather than deterministic. The output from such analyses will not be a single evaluation, but a distribution of evaluations that will form the basis for a decision on the acceptability of risk of failure. Although the methods required for such analyses are already available in theory, they cannot yet be practically implemented because of lack of computing power. In particular, the requirement that analyses are probabilistic and three dimensional places enormous demands on computing capacity.

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