AI-aided short-term decision making of rockburst damage scale in underground engineering

Abstract

Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. The research considered factors such as stress conditions, support system capacity, excavation span, geological characteristics, seismic magnitude, peak particle velocity, and rock density as input variables. The rockburst damage scale, categorized into four severity levels based on displaced rock mass, served as the target variable. Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. The findings underscore the effectiveness of BO-RF in enhancing rockburst risk assessment and providing reliable predictive insights for underground engineering applications.

Keywords

Underground engineering / Rockburst damage scale / Short-term decision making / Ensemble learning / Bayesian optimization

CRediT authorship contribution statement

Chukwuemeka Daniel: Writing - review & editing, Writing - original draft, Validation, Supervision, Software, Methodology, Data curation, Conceptualization. Shouye Cheng: Writing - original draft. Xin Yin: Writing - review & editing, Writing - original draft, Validation, Supervision, Software, Methodology, Data curation, Conceptualization. Zakaria Mohamed Barrie: Writing - original draft. Yucong Pan: Methodology, Conceptualization. Quansheng Liu: Methodology, Conceptualization. Feng Gao: Software. Minsheng Li: Software. Xing Huang: Software.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: the research project is funded by Tiandi Science and Technology Company Limited.

Acknowledgement

This research is supported by the Young Elite Scientist Sponsorship Program by China Association for Science and Technology under Grant No. YESS20230742.

Supplementary material

Supplementarydatatothisarticlecanbefoundonlineat https://doi.org/10.1016/j.undsp.2025.02.005.

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