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Rockburst intensity evaluation by a novel systematic and evolved approach: machine learning booster and application

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Abstract

Prediction of rockburst in underground engineering is of significance as it is close to the safety of support structures, personnel, and working environments. To improve the accuracy of rockburst classification, this paper proposed an ensemble classifier RF-FA model in which the random forest classifier (RF) and firefly algorithm (FA) were combined to achieve the optimum performance on rockburst prediction. Five key parameters of surrounding rock, i.e., the depth H, the maximum tangential stress σθ, the uniaxial compressive strength σc, the tensile strength σt, and the elastic energy index Wet, are selected as input variables while the rockburst intensity including none, light, moderate, and strong classes was chosen as output. A total of 279 cases worldwide were collected and used for train and test the proposed RF-FA model. The results showed that the FA can optimize the hyperparameters of RF efficiently and the optimum model exhibited a high performance on rockburst data from the independent test set and new engineering projects. The feature importance obtained by the ensemble RF-FA model indicated that the elastic energy index plays the most important role in rockburst. Besides, the proposed model showed much better accuracy on rockburst classification compared with previously existing RF models and empirical criteria, which means it is a useful and robust tool for rockburst prediction.

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Funding

This research was supported by the projects of “the Fundamental Research Funds for the Central Universities (2020ZDPY0221, 2021QN1003)”, “National Natural Science Foundation of China (52104106, 52174089)”, "Natural Science Foundation of Jiangsu Province" (BK20210513), Basic Research Program of Xuzhou (KC21017).

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Authors and Affiliations

  1. School of Mines, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, Xuzhou, 221116, China

    Yuantian Sun, Guichen Li & Jiandong Huang

  2. School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China

    Junfei Zhang

Authors
  1. Yuantian Sun
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  2. Guichen Li
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  3. Junfei Zhang
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  4. Jiandong Huang
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Correspondence to Jiandong Huang.

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Sun, Y., Li, G., Zhang, J. et al. Rockburst intensity evaluation by a novel systematic and evolved approach: machine learning booster and application. Bull Eng Geol Environ 80, 8385–8395 (2021). https://doi.org/10.1007/s10064-021-02460-7

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  • DOI: https://doi.org/10.1007/s10064-021-02460-7

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