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A prediction model on rockburst intensity grade based on variable weight and matter-element extension

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  • Jianhong Chen
  • Yi Chen
  • Shan Yang
  • Xudong Zhong
  • Xu Han

Abstract

Rockburst is a common dynamic disaster in deep underground engineering. To accurately predict rockburst intensity grade, this study proposes a novel rockburst prediction model based on variable weight and matter-element extension theory. In the proposed model, variable weight theory is used to optimize the weights of prediction indexes. Matter-element extension theory and grade variable method are used to calculate the grade variable interval corresponding to the classification standard of rockburst intensity grade. The rockburst intensity grade of Engineering Rock Mass is predicted by rock burst intensity level variable and the interval. Finally, the model is tested by predicting the rockburst intensity grades of worldwide several projects. The prediction results are compared with the actual rockburst intensity grades and the prediction results of other models. The results indicate that, after using variable weight theory and grade variable method, the correct rate of prediction results of matter-element extension model is improved, and the safety of the prediction results is also enhanced. This study provides a new way to predict rock burst in underground engineering.

Suggested Citation

  • Jianhong Chen & Yi Chen & Shan Yang & Xudong Zhong & Xu Han, 2019. "A prediction model on rockburst intensity grade based on variable weight and matter-element extension," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
    • Handle: RePEc:plo:pone00:0218525
    DOI: 10.1371/journal.pone.0218525
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    References listed on IDEAS

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    1. Ning Li & R. Jimenez, 2018. "A logistic regression classifier for long-term probabilistic prediction of rock burst hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 197-215, January.
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    Cited by:

    1. Zhe Liu & Jianhong Chen & Yakun Zhao & Shan Yang, 2023. "A Novel Method for Predicting Rockburst Intensity Based on an Improved Unascertained Measurement and an Improved Game Theory," Mathematics, MDPI, vol. 11(8), pages 1-18, April.
    2. Kailei Li & Han Bai & Xiang Yan & Liang Zhao & Xiuguang Wang, 2023. "Cooperative Efficiency Evaluation System for Intelligent Transportation Facilities Based on the Variable Weight Matter Element Extension," Sustainability, MDPI, vol. 15(3), pages 1-16, January.

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