Evaluation of rockburst potential in deep underground excavations using artificial intelligence methods in WEKA data mining software

Rockburst in deep underground excavations is a phenomenon that is observed in the form of sudden rock failure and release of strain energy stored in underground mines and rock tunnels, usually in stressful places and at a depth greater than the earth's surface. Due to this explosive failure, rock in pieces Small and large are scattered around and cause damage to humans or equipment. In the present study, the potential of rockburst using data mining techniques and comparing their results with three experimental methods, tangential stress criterion (SC), brittleness criterion (BC) and elastic energy index (EEI) are investigated. For this purpose, data mining models such as support vector machine (SVM), k-nearest neighbor (KNN), Bayesian networks (BNs), artificial neural network (ANN) and CHAID tree model have been used in WEKA software. The results show the superiority of data mining algorithms over experimental methods. Among data mining algorithms, the support vector machine model had the highest accuracy with 80.8% accuracy. However, measuring the accuracy of the models by RMSE method considers the artificial neural network model with the value of RMSE = 0.337 as the best model. In addition, among the experimental methods, the elastic energy index method with an accuracy of 60.7 is the best method.