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Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value

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Abstract

Rockburst prediction is the basis of rockburst prevention and construction guidance. However, the complexity of the rock burst occurrence mechanism and inducing factors and the suddenness and randomness of rock burst behavior make the accurate prediction of rock bursts very difficult. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm is used to learn and predict the rockburst intensity of a database including 341 rockburst cases worldwide. A procedure for parameter optimization of XGBoost combined with grid search and cross validation methods is proposed. It improves the prediction performance, effectively avoids overfitting and also improves the operation efficiency. The model predicted 7 typical rockburst cases that occurred at **** II Hydropower Station, and the results showed that the GC-XGBoost model performs well in predicting rockburst intensity. In addition, compared with typical supervised learning models (SVM and RF), the model showed improved prediction performance. SHapley Additive exPlanations (SHAP, a game theoretic approach) was used to study the importance of feature parameters. The SHAP values showed that Wet and \(\sigma_{{\uptheta }}\) are the two most important feature parameters for predicting rockburst intensity.

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Funding

This research was supported by the National Natural Science Foundation of China (No. 51934003) and the Program for Yunnan thousand talents plan high-level innovation and entrepreneurship team.

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine, University of Science and Technology Bei**g, Bei**g, 100083, China

    Long Chen, Shunchuan Wu, Aibing **, Chaojun Zhang & Xue Li

  2. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, Yunnan, China

    Shunchuan Wu

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  1. Long Chen
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Correspondence to Aibing **.

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Chen, L., Wu, S., **, A. et al. Rockburst Prediction and Evaluation Model for Hard Rock Engineering Based on Extreme Gradient Boosting Ensemble Learning and SHAP Value. Geotech Geol Eng 41, 3923–3940 (2023). https://doi.org/10.1007/s10706-023-02496-4

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