Integrating the effect of abutments in estimating the average vertical stress of elastic hard rock pillars by combining numerical modelling and artificial neural networks

Abstract

Estimating average vertical pillar stresses is a critical step in designing room-and-pillar mines. Several analytical methods can be used to estimate the vertical stresses acting on the pillars. However, the present analytical methods fail to adequately account for the influence of abutments on the distribution of vertical stresses, especially when applied to narrow panel widths and pillar layouts comprising evenly spaced barriers. In this study, a multi-layer perceptron neural network (MLPNN) was applied to predict the vertical loads of regular pillars more accurately. Hundreds of room-and-pillar mine layouts were modeled using a displacement discontinuity method (DDM), and a database of 2355 sampled pillar cases was compiled. The MLPNN was trained based on this database, and its prediction capabilities were further validated using simulations by a finite difference code (i.e., FLAC3D). The model predictions and the FLAC3D simulations reasonably agreed with a regression coefficient of 0.99. The model was also adapted for mine cases with evenly spaced barrier pillars, and its application to a real case study mine has shown to provide accurate pillar stress estimations; hence, this model is suitable for practical use at mines. Even though the MLPNN model cannot be applied universally to all mine situations, it seems as a significant improvement over existing analytical techniques in terms of accounting for the influence of abutments on pillar stresses.