A Study of Rockburst Prediction Method Based on D-S Evidence Theory

被引：0
|
作者：
Gao Y.-T. [1 ]
Zhu Q. [1 ]
Wu S.-C. [1 ,2 ]
Wang Y.-B. [3 ]
机构：
[1] School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing
[2] Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming
[3] Yunnan Chihong Zinc and Germanium Co.，Ltd, Qujing
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2024年 / 45卷 / 02期
关键词：
D-S evidence theory; fuzzy matter-element; K-means; rock mechanics; rockburst prediction;
D O I：
10.12068/j.issn.1005-3026.2024.02.012
中图分类号：
学科分类号：
摘要：
To effectively predict rockburst， a rockburst prediction method based on D-S evidence theory is proposed. Firstly，six indicator factors related to rockburst occurrence are selected as evidence，and the basic probability assignment of evidence is constructed through fuzzy matter-element framework and normal type degree of membership function. Then，the evidence is classified using K-means and a combined fusion rule of fusing evidence within clusters in classical Dempster’s rule while fusing evidence between clusters in the weighted way is proposed to reduce the adverse effects of high conflict evidence fusion. Finally，the model is applied to the No. 2 shaft project of the Qinling Zhongnanshan highway tunnel and compared with the empirical method. To address the uncertainty in the prediction process and to estimate the probability of rockburst occurrence，Monte Carlo simulation is used to perform sampling simulations and the global sensitivity of the input indicators is measured by Spearman’s rank correlation coefficient. The results show that the influence of the input indicators varies widely and in different directions for different rockburst cases；the probability of occurrence of the five rockburst cases ranges from 40. 8% to 70. 1%. The model shows outstanding predictive classification performance and can provide reference for rockburst prediction in deeply buried underground projects. © 2024 Northeast University. All rights reserved.
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页码：244 / 251
页数：7
相关论文
共 25 条
  • [1] Qian Qi - hu, Definition，mechanism，classification and quantitative forecast model for rockburst and pressure bump ［J］, Rock and Soil Mechanics, 35, 1, pp. 7-12, (2014)
  • [2] Wang J A, Park H D., Comprehensive prediction of rockburst based on analysis of strain energy in rocks［J］, Tunnelling and Underground Space Technology, 16, 1, pp. 49-57, (2001)
  • [3] Cai M F，, Brown E T．, Challenges in the mining and utilization of deep mineral resources［J］, Engineering, 3, 4, pp. 432-433, (2017)
  • [4] Qiao C, Guo Y H, Li C H., Study on rock burst prediction of deep buried tunnel based on cusp catastrophe theory［J］, Geotechnical and Geological Engineering, 39, 1, pp. 1101-1115, (2021)
  • [5] Askaripour M，, Saeidi A，, Rouleau A, Et al., Rockburst in underground excavations： a review of mechanism，classification，and prediction methods［J］, Underground Space, 7, 4, pp. 577-607, (2022)
  • [6] Zhou J, Li X B, Mitri H S., Evaluation method of rockburst ：state‐of‐the‐art literature review［J］, Tunnelling and Underground Space Technology, 81, pp. 632-659, (2018)
  • [7] Gong F Q, Wang Y L, Luo S., Rockburst proneness criteria for rock materials：review and new insights［J］, Journal of Central South University, 27, 10, pp. 2793-2821, (2020)
  • [8] Xu L, Gong F Q, Liu Z X., Experiments on rockburst proneness of pre ‐ heated granite at different temperatures：insights from energy storage，dissipation and surplus［J］, Chinese Journal of Rock Mechanics and Engineering, 14, 5, pp. 1343-1355, (2022)
  • [9] Hoek E, Brown E T., Underground excavations in rock［M］, (1980)
  • [10] Liang Wei - zhang, Guo Zhao, A review of long ‐ term and short ‐ term rockburst risk evaluations in deep hard rock ［J］, Chinese Journal of Rock Mechanics and Engineering, 41, 1, pp. 19-39, (2022)
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