Tengjie Yang ¹ Xinqiang Gao ^1* Lichuan Wang ² Yongqing Xue ² Haobo Fan ¹ Zhengguo Zhu ¹ Jingbo Zhao ² Beiyi Dong ¹

• ¹ Shijiazhuang Tiedao University, Shijiazhuang, China
• ² China Railway 18th Bureau Group, Tianjin, China

The accurate rockburst prediction is crucial for ensuring the safety of underground engineering construction. Among the various methods, machine learning-based rockburst prediction can better solve the nonlinear relationship between rockbursts and influencing factors and thus has great potential for engineering applications. However, current research often faces certain challenges related to the feature selection of prediction indices and poor model optimization performance. This study compiled 342 rockburst cases from domestic and international sources to construct an initial database. In order to determine the relevant prediction indicators, a feature selection method based on the ReliefF-Kendall model was proposed. The initial database was equalized and visualized using the Adasyn and t-SNE algorithms. Five rockburst prediction models (SVM, LSSVM, KELM, RF, and XGBoost) were established by employing the Secretary Bird Optimization (SBO) algorithm and 5fold cross-validation to optimize performance. The optimal model was selected based on a comprehensive assessment of generalization ability (accuracy, kappa, precision, recall, and F1-score) and stability (average accuracy). The reliability of the proposed feature selection, model optimization, and data balancing methods was verified by comparing the optimal model with other methods. The results indicate that the PSO-SVM model demonstrated superior prediction accuracy and generalization performance; the accuracy can reach 81.4 % (optimal) and 80.1 % (average). The main factors affecting the occurrence of rockburst are Wet, MTS, D, and UCS. Finally, the model was applied to the domestic rockburst engineering cases, achieving a prediction accuracy of 90% and verifying its engineering applicability.