One of the utmost severe mining catastrophes in underground hard rock mines is rock burst phenomena. It can lead to damage to mine openings and equipment as well as trigger accidents or even threat to life as well. Due to this, a number of researchers are forced to study some easy-to-use alternative methods to predict the rock burst occurrence. Nevertheless, due to the extremely multifaceted relation between mechanical, geological and geometric factors of the mines, the conventional prediction methods are not able to produce accurate results. With the expansion of machine learning methods, a revolution in the rock burst occurrence has become imaginable. In present study, three machine learning methods, namely XGBoost, decision tree and support vector machine, are utilized to predict the occurrence of rock burst in various underground projects. A total of 134 rock burst events were gathered together from various published literatures comprising maximum tangential stress (MTS), elastic energy index (EEI), uniaxial compressive strength and uniaxial tensile stress (UTS) that have been used to develop various machine learning models. The performance of machine learning methods is evaluated based on the accuracy, sensitivity and specificity of the rock burst prediction.

地下硬岩矿山中最严重的采矿灾难之一就是岩爆现象。它可能导致对矿井口和设备的损坏，甚至引发事故甚至威胁生命。因此，许多研究人员被迫研究一些易于使用的替代方法来预测岩爆的发生。然而，由于矿山的机械，地质和几何因素之间的多方面关系，传统的预测方法无法产生准确的结果。随着机器学习方法的扩展，岩爆发生中的一场革命已经可以想象到了。在本研究中，XGBoost，决策树和支持向量机这三种机器学习方法被用来预测各种地下工程中岩爆的发生。从各种已公开的文献中收集了总共134个岩石破裂事件，这些文献包括最大切向应力（MTS），弹性能指数（EEI），单轴抗压强度和单轴拉伸应力（UTS），这些文献已用于开发各种机器学习模型。机器学习方法的性能是根据岩爆预测的准确性，敏感性和特异性进行评估的。