Experimental investigation of predicting rockburst using Bayesian model

Abstract

Rockbursts, catastrophic events involving the violent release of elastic energy stored in rock features, remain a worldwide challenge for geoengineering. Especially at deep-mining sites, rockbursts can occur in hard, high-stress, brittle rock zones, and the associated risk depends on such factors as mining activity and the stress on surrounding rocks. Rockbursts are often sudden and destructive, but there is still no unified standard for predicting them. Based on previous studies, a new Bayesian multi-index model was introduced to predict and evaluate rockbursts. In this method, the rock strength index, energy release index, and surrounding rock stress are the basic factors. Values from 18 rock samples were obtained, and the potential rockburst risks were evaluated. The rockburst tendencies of the samples were modelled using three existing methods. The results were compared with those obtained by the new Bayesian model, which was observed to predict rockbursts more effectively than the current methods.