Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

中文翻译：

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两种智能分类技术预测地下工程的岩爆危险：对比研究

岩爆是岩石地下开挖中动态失稳的复杂现象。由于岩爆机理复杂且不清楚，因此难以准确预测和合理评估岩爆潜力。随着岩石工程案例历史的不断增加以及数据科学的发展，数据挖掘算法提供了一种预测复杂现象（如岩爆潜力）的好方法。本文研究了J48和随机树算法在165个案例中预测围岩分类等级的潜力，该案例具有四个参数，即围岩的最大切向应力，单轴抗压强度，单轴抗拉强度和应变储能指数。对已开发模型的性能进行比较后发现，与J48和其他经验模型（Russenes准则，岩石脆性系数准则和人工神经网络）相比，随机树提供了更可靠的预测。与卷积神经网络的类似比较在岩爆危险数据建模中具有同等的性能。