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A logistic regression classifier for long-term probabilistic prediction of rock burst hazard

Author

Listed:
  • Ning Li

    (Universidad Politécnica de Madrid)

  • R. Jimenez

    (Universidad Politécnica de Madrid)

Abstract

Rock burst is a complex dynamic process can lead to casualties, to failure and deformation of the supporting structures, and to damage of the equipment on site; hence, its prediction is of great importance in underground construction. We present a novel empirical method to predict rock burst based on the theory of logistic regression classifiers. An extensive database collected from the literature, which includes observations about rock burst occurrence (or not) in underground excavations in projects from all over the world, is used to train and validate the model. The proposed approach allows us to compute new class separation lines (or planes) to estimate the probability of rock burst, using different combinations of five possible input parameters—tunnel depth, H; maximum tangential stress, MTS; elastic energy index, W et; uniaxial compressive strength of rock, UCS; uniaxial tensile strength of rock, UTS—among which it was found that the preferable model could be developed in H–W et–UCS space. The proposed model is validated with goodness-of-fit tests and nine-fold cross-validation; results show that its predictive capability compares well with previously proposed empirical methods and confirm that, as expected, the probability of rock burst increases with excavation depth, and that both W et and UCS have a similarly significant influence on rock burst occurrence. Finally, expressions are proposed for identification of conditions associated with several reference values of rock burst probability, which can be employed in preliminary risk analyses.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:nathaz:v:90:y:2018:i:1:d:10.1007_s11069-017-3044-7
    DOI: 10.1007/s11069-017-3044-7
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    References listed on IDEAS

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    1. Zaobao Liu & Jianfu Shao & Weiya Xu & Yongdong Meng, 2013. "Prediction of rock burst classification using the technique of cloud models with attribution weight," 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. 68(2), pages 549-568, September.
    2. Wu Cai & Linming Dou & Siyuan Gong & Zhenlei Li & Shasha Yuan, 2015. "Quantitative analysis of seismic velocity tomography in rock burst hazard assessment," 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. 75(3), pages 2453-2465, February.
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    Cited by:

    1. Diyuan Li & Zida Liu & Danial Jahed Armaghani & Peng Xiao & Jian Zhou, 2022. "Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
    2. Keyou Shi & Yong Liu & Weizhang Liang, 2022. "An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    3. Zhenlei Li & Shengquan He & Dazhao Song & Xueqiu He & Linming Dou & Jianqiang Chen & Xudong Liu & Panfei Feng, 2021. "Microseismic Temporal-Spatial Precursory Characteristics and Early Warning Method of Rockburst in Steeply Inclined and Extremely Thick Coal Seam," Energies, MDPI, vol. 14(4), pages 1-27, February.
    4. Weiyao Guo & Qingheng Gu & Yunliang Tan & Shanchao Hu, 2019. "Case Studies of Rock Bursts in Tectonic Areas with Facies Change," Energies, MDPI, vol. 12(7), pages 1-11, April.
    5. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," 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. 104(2), pages 1923-1946, November.
    6. 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.

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