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| Rockburst prediction based on nine machine learning algorithms#br# |
| TANG Zhili1，2，XU Qianjun1#br# |
(1. State Key Laboratory of Hydroscience and Engineering，Tsinghua University，Beijing 100084，China；2. Beijing Jingtou Urban Utility Tunnel Investment Co.，Ltd.，Beijing 100027，China)
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Abstract Rockburst prediction is the basis for mitigating and eliminating rockburst hazards. In this paper，a rockburst prediction dataset containing 325 sets of rockburst cases was constructed. Based on nine classical machine learning algorithms，nine rockburst comprehensive prediction models that considering multiple factors were established. In the process of model establishment，multiple data preprocessing techniques were used to clean，normalize and dimensionly reduce the dataset，which addressed the data-imbalance problem. The optimal feature combination of rockburst prediction was obtained by extracting and selecting features，and the optimal parameters of the models were obtained by using grid search cross-validation technique. The prediction performances of the models were verified and evaluated by using accuracy，precision，recall rate，F1，macro-average，micro-average and other indicators，and compared with the classification performance of the commonly used theoretical criteria. The results of the model performance evaluation show that the accuracy of the model built in this paper is much higher than that of the widely used theoretical criteria. Based on the established models，the rockburst prediction of Tibet Duoxiongla tunnel is carried out，and the results are in good agreement with the field situation.
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2020, Vol. 39 
