Risk Assessment and Interpretability Methods for Rockbursts in Tunnels

  • Yu Hongtao ,
  • Xiao Haohan ,
  • Li Mingyao ,
  • Cao Ruilang
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  • 1. State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100048, P.R. China;
    2. School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, P.R. China

Received date: 2025-10-12

  Online published: 2026-09-07

Abstract

To enhance the accuracy and interpretability of tunnel rockburst risk assessment, 386 sets of rockburst case datasets is collected, an XGBoost rockburst risk prediction model integrated with the SHAP algorithm is developed, and the impact of eight different combinations of rockburst risk assessment indicators on model performance is analyzed. The results indicate that: The feature set including all input indicators achieves the optimal performance in predicting rockburst risk levels. Compared with other machine learning techniques, the XGBoost algorithm demonstrates superior applicability in addressing the multi-input single-output challenges of rockburst risk prediction, owing to the advantages of its ensemble learning framework. Additionally, the SHAP algorithm unveiled that the elastic energy index and tunnel depth exert significant influences on the prediction outcomes. These findings offer can provide critical guidance for engineering practitioners and on-site decision-makers to mitigate potential rockburst hazards and formulate science-based mitigation strategies.

Cite this article

Yu Hongtao , Xiao Haohan , Li Mingyao , Cao Ruilang . Risk Assessment and Interpretability Methods for Rockbursts in Tunnels[J]. Chinese Journal of Underground Space and Engineering, 2026 , 22(4) : 1440 -1450 . DOI: 10.20174/j.JUSE.2026.04.32

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