In recent years there have been many publications on the advances in slope stability modeling software and associated techniques, capable of performing high level analysis, however some of the fundamentals of slope design have not been re-iterated i.e. from field data collection, through to the formulation of a geotechnical model. It is the authors aim to provide an overview of geotechnical data collection and model formulation required for conducting a probabilistic slope design primarily aimed at new or junior practitioner. There are many benefits that arise from conducting a probabilistic slope design, that extend from the operation phase to the optimization phase of a mining operation, some of these potential benefits will be presented.
INTRODUCTION
Probabilistic slope design has been in practice for some time (at least 20 years or so), having been pioneered by Lilly, Martin, Piteau and others. However in recent years published material tends to deal with singular components of overall probabilistic slope design for example specifically focusing on the analysis of kinematic failure mechanisms, without necessarily providing the reader with an overall appreciation of how it fits into the ‘larger picture’ i.e. the overall stability of a mine slope. The author intends this paper to provide a ‘new-comer’ to slope design with an overall comprehension of how probabilistic slope design can be carried out and its potential benefits within the overall mine design and optimization process.
From geotechnical field data collection;
Laboratory testing; and
Interpretation and analyses of data for slope designs; and
How these results may be utilized within mine optimization software packages like Whittle when conducting economic analyses.
This paper intends to provide an overview of the probabilistic slope design methodology i.e.One of the most challenging aspects of conducting a slope stability analysis is the uncertainty associated with the geotechnical model typically this level of uncertainty is catered for by incorporating a higher factor of safety within the slope design, which experienced slope design engineers have a ‘feel for. However this may prove some what challenging for a new practitioner, who may either allow for too much or not enough thereby producing an overly conservative or overly optimistic (which may not be achievable) slope design.
It is the intention of the author that this paper would provide an adequate method for the quantification of this uncertainty hence enabling the slope design engineer to provide a slope design where the reliability is known.
One of the key items to that should be appreciated, is that probabilistic slope design potentially enables the slope engineer to design slopes with Factors of Safety (FOS) less than the usual 1.2 or even 1.0, providing the ‘consequences’ or risk can be catered for. To put into context, i.e. how an engineer might recommend a slope with a FOS of less than 1.0 is acceptable, the process by which FOS is calculated needs to be understood.
