Rockburst prediction is of vital significance to the design and construction of underground hard rock mines. A rockburst database consisting of 102 case histories, i.e., 1998–2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods. The dataset was examined with six widely accepted indices which are: the maximum tangential stress around the excavation boundary (MTS), uniaxial compressive strength (UCS) and uniaxial tensile strength (UTS) of the intact rock, stress concentration factor (SCF), rock brittleness index (BI), and strain energy storage index (EEI). Two boosting (AdaBoost.M1, SAMME) and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated. The available dataset was randomly divided into training set (2/3 of whole datasets) and testing set (the remaining datasets). Repeated 10-fold cross validation (CV) was applied as the validation method for tuning the hyper-parameters. The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles. According to 10-fold CV, the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1, SAMME algorithms and empirical criteria methods.

岩爆预测对地下硬岩矿山的设计和施工具有至关重要的意义。通过三种基于树的集成方法，对由102个案例历史（即来自14个硬岩矿的1998-2011年期间的数据）组成的岩爆数据库进行了研究，以评估易爆矿山的岩爆预测。使用六个广泛接受的指标对数据集进行了检查，这些指标是：完整岩石的开挖边界附近的最大切向应力（MTS），完整岩石的单轴抗压强度（UCS）和单轴抗拉强度（UTS），应力集中系数（SCF），岩石脆性指数（BI）和应变能存储指数（EEI）。评估了两种提升算法（AdaBoost.M1，SAMME）和袋装算法，其中使用分类树作为学习岩爆能力的基准分类器。将可用数据集随机分为训练集（整个数据集的2/3）和测试集（其余数据集）。重复的10倍交叉验证（CV）被用作调整超参数的验证方法。边缘分析和可变的相对重要性被用来分析乐团的一些特征。根据10倍CV，岩爆数据集的准确性分析表明，与AdaBoost.M1，SAMME算法和经验标准方法相比，岩爆潜力的最佳预测方法是装袋。边缘分析和可变的相对重要性被用来分析乐团的一些特征。根据10倍CV，岩爆数据集的准确性分析表明，与AdaBoost.M1，SAMME算法和经验标准方法相比，岩爆潜力的最佳预测方法是装袋。边缘分析和可变的相对重要性被用来分析乐团的一些特征。根据10倍CV，岩爆数据集的准确性分析表明，与AdaBoost.M1，SAMME算法和经验标准方法相比，岩爆潜力的最佳预测方法是装袋。