Rockburst prediction using machine learning: Comparative study of algorithms and features

Abstract

Rockbursts represent a critical dynamic hazard in deep tunnel construction; however, the scarcity of labeled data poses significant challenges for accurate predictions. Hence, we reformulate the conventional four-class rockburst classification task into a binary-classification problem. A comprehensive rockburst dataset was compiled based on an extensive literature review. Six machine-learning algorithms-support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), multilayer perceptron (MLP), random forest (RF), and extremely randomized trees (ETs)-were implemented and evaluated across multiple feature set configurations. The results are as follows: (1) feature set selection substantially affects predictive accuracy, with higher-dimensional feature combinations yielding superior performance; (2) ensemble methods(RF and ETs) outperform SVM and MLP by reducing variance and enhancing generalization on complex rockburst data; and (3) the binary-classification framework consistently outperforms the conventional four-class scheme, achieving accuracies above 0.80 by simplifying decision boundaries and reducing interclass ambiguity. These findings contribute to the development of a real-time online framework for rockburst risk prediction and offer valuable insights into proactive hazard mitigation in underground engineering.

Keywords

Machine learning / Rockburst prediction / Binary classification / Feature selection / Ensemble methods

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