Predicting rockburst intensity is an important task in mining since rockburst occurs as a violent expulsion of rock in high geo-stress condition which causes considerable damages to underground structures, equipment and most importantly presents serious menaces to workers’ safety. It has been responsible for numerous deaths and injuries in underground mines across the world. Due to this importance, the current study aims at predicting the intensity of rockburst on the basis of 174 rockburst events that were compiled. Several existing criteria were considered to model the rockburst intensity. The inputs parameters included the maximum tangential stress, the uniaxial compressive strength, the uniaxial tensile strength of the surrounding rock and the elastic strain energy index. A Bayesian inference approach was implemented to identify the most appropriate models for estimating the rockburst intensity category among three rockburst criteria. The WinBUGS software was used to compute the posterior predictive distributions of the model parameters and the deviance information criterion (DIC) corresponding to the models. The DIC and the percentage of correctly predicted rockburst category were employed to assess the model performance. Overall, the results indicate that the Bayesian inference allows achieving satisfactory predictive performance in modelling the rockburst intensity. Also, the associated predictive uncertainty can be improved when new data are available. The results suggest that the implemented Bayesian models can be helpful in managing rockburst events in mines using site specific data and therefore, reducing the casualties induced by rockburst.

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