Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k‐nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five‐fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects.

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集成学习在岩爆强度多类分类中的优势

岩石爆破是岩石的猛烈驱逐，这是由于存储在周围岩体中的应变能极度释放，导致对地下结构和设备的巨大破坏，并威胁到工人的安全。随着工程项目操作深度的增加，大量因素会影响岩爆的机理。因此，基于常规标准不能实现对岩爆强度的准确分类。迫切需要开发出高精度且易于实践的新模型。这项研究通过集合七个单独的分类器（包括反向传播神经网络，支持向量机，决策树，k最近邻，逻辑回归，多元线性回归和朴素贝叶斯。此外，我们提出了九种数据插补方法来替换已编译数据库中的缺失值，其中包括188个岩爆实例。五重交叉验证和甲虫触角搜索算法用于调整各个分类器的超参数和投票权重。结果表明，与测试集中的最佳单个分类器相比，分类器集合获得的岩爆分类精度提高了15.4％。分类器集合获得的预测变量重要性表明，弹性能指数是岩爆强度分类中最敏感的输入变量。这种强大的集成方法可以扩展为解决地下工程项目中的其他分类问题。