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A stability analysis was carried out for an underground narrow vein room and pillar operation using linear elastic numerical modelling combined with a probability of failure (PoF) approach. The as-mined layout was input into the model to identify areas of increased pillar stress and obtain an average pillar stress (APS) for each pillar in an identified area. Pillar dimensions were measured from the given layout to calculate an empirical pillar strength (PS) using the Hedley and Grant (1972) approach. A Factor of Safety (FS) was evaluated for each pillar from the APS and PS, and the risk of failure for selected areas was estimated based on the proportional number of pillars with a sub-critical FS. As a result, three critical areas on the operation were identified for inspection and implementation of additional monitoring and/or remedial action. A statistical simulation was further carried out using the Monte Carlo method to evaluate the impact of measured and observed variability in pillar size on the design. The final design proposed for the operation was based on a revised pillar strength factor (K), taking into account the effect of mining practices on the empirical design. (Note: confidentiality limitations apply such that the operation is undisclosed and target areas are arbitrarily renamed). 2 Methodology 2.1 Site characterisation The orebody is hosted within a platinum-bearing igneous intrusion. The mineralised zone comprises pyroxenite approximately 2.0 m thick, overlain and underlain by similar pyroxenitic material, dipping between 9 and 0° (practically sub-horizontal). The mine is classified as 'dry' with no significant water ingress. The ratio of horizontal to vertical stress (k) was assumed to be unity in all directions (hydrostatic) based on visual assessment of underground conditions and in the absence of a measured stress field. https://papers.acg.uwa.edu.au/p/1511_02_Walls/ Room and pillar stability analysis using linear elastic modelling and probability EJ Walls et al. of failurea case study 96 Underground Design Methods 2015, Perth, Australia 2.1.1 Mining layout The orebody is extracted through a series of strike-orientated drives (bords) separated by rectangular pillars approximately 1.5 to 1.8 times wider than the mining height (dip dimension) with lengths approximately 3 times longer than the pillar width (strike dimension). Bord widths are reduced by more than half to 6 m in poorer ground. 2.1.2 Major structures The orebody is intersected by several sub-vertical faults and aplite dykes of variable throw and thickness, resulting in isolated geological loss zones (pillars); however, major disruptions to the mining layout have not occurred.

The collection and analysis of geotechnical data forms the basis for understanding the geotechnical characteristics and the overall quality of the rock mass in a mining environment. While the use of statistics can provide an impression of the average rock mass quality across a project area, it does not assist with a detailed understanding of the way in which data may be spatially related. With the introduction of geostatistics, the spatial continuity of a dataset may be investigated. This may be carried out with the use of semi-variograms. Once the spatial continuity of a dataset is understood, geostatistical methodologies may be applied to create a geotechnical block model. This paper focuses on the creation of a geotechnical block model which provides a three-dimensional visual representation of rock mass data (in varying levels of confidence) across a project area. This concept is illustrated using a case study where geostatistics is adopted to estimate the rock mass quality across a proposed mining area by applying the appropriate geostatistical methodologies between geotechnical boreholes. A holistic impression of the rock mass conditions is given by the model, whilst also providing insight on areas where poor rock quality and associated potential instabilities can occur. This study also brings to light the importance of collecting reliable data during the geotechnical logging process, as the success of any geotechnical block model is highly dependent on the input data that the geostatistics is applied to. If created with careful consideration it is believed that geotechnical block models are valuable tools which can be continually updated as more data is gathered as mining progresses.