Establishing a Bayesian Network model for predicting rockburst damage potential

In underground mines, rockburst can cause significant damages to underground excavations and equipment, which impacts negatively mine safety and productivity. Hence, the establishment of model capable of predicting the rockburst damage in a reliable manner is essential. Despite the existence of the extensive number of predictive models for rockburst, a reliable prediction of rockburst damage potential is challenging. One of the main difficulties to predict rockburst with existing models is their inability to account for missing or incomplete data, which is commonly encountered in practice. To overcome this issue, in this paper, a Bayesian network (BN) approach is employed to develop a short-term prediction model for rockburst on the basis of mine seismicity monitoring data. A rockburst database consisting of 254 case histories was compiled. The input parameters include the stress conditions, ground support capacity, excavation span, effect of geological structure, peak particle velocity, and the output defined as the rockburst damage scale (RDS) index. The Tree Augmented Naïve Bayes classifier structure was implemented. The proposed BN was tested using 5-fold cross-validation. The practical use of the BN was illustrated as well. Overall, the BN results indicated acceptable prediction accuracies ranging from 70 to 78%. It was concluded that BN could represent a reliable predictor to control excavation damages due to mine seismicity, especially when incomplete data prevail.