Research on evaluation method of rock burst tendency based on improved comprehensive weighting

被引：0

作者：

Li K. ^{[1
,2
]}

Li M. ^{[1
,2
]}

Qin Q. ^{[1
,2
]}

机构：

[1] Kunming University of Science and Technology, School of Land and Resources Engineering, Kunming

[2] Yunnan Key Laboratory of Sino-German Blue Mining and Utilization of Special Underground Space, Kunming

来源：

Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering | 2020年 / 39卷

基金：

中国国家自然科学基金;

关键词：

Cloud model; Comprehensive weight; Ideal point; Improvement; Prediction of rock burst tendency; Rock mechanics;

D O I：

10.13722/j.cnki.jrme.2019.1142

中图分类号：

学科分类号：

摘要：

In order to solve the problems of the large randomness of index weight calculation leads to low prediction accuracy in rock burst tendency evaluation and the judgment of the rock burst propensity level is too singular, etc. An evaluation method of rock burst propensity based on improved comprehensive weighting is proposed. The 15 factors corresponding to lithological conditions, stress conditions and surrounding rock conditions are comprehensively selected as the judgment index of rock burst tendency, and then applying comprehensive weighting method to obtain comprehensive weight. On this basis, the cloud normal model and ideal point method were used to evaluate the rock burst tendency of specific engineering cases respectively, to judge the level of rock burst tendency, and to verify the accuracy and reliability of the method. The results show that five indicators have a greater impact on rock burst propensity which are the energy storage consumption index k, T criterion, dynamic DT parameters, elastic energy index Wet and stress index S, the index weight result calculated by the improved comprehensive weighting method is more reasonable. Compared with the ideal point method, the evaluation result of the rock burst propensity grade obtained by the cloud normal model is more accurate. The research results will provide new ideas for the prediction of rock burst propensity for geotechnical engineering such as mines, tunnels and hydropower stations. © 2020, Science Press. All right reserved.

引用

页码：2751 / 2762

页数：11

共 24 条

[1] HE Manchao, MIAO Jinli, LI Dejian, Et al., Experimental study on rock burst process of deep granite samples, Chinese Journal of Rock Mechanics and Engineering, 26, 5, pp. 865-876, (2007)
[2] LIU Ning, ZHANG Chunsheng, SHAN Zhigang, Et al., Engineering practice of support for long and deep buried tunnel under rock burst risk, Chinese Journal of Rock Mechanics and Engineering, 38, pp. 2934-2943, (2019)
[3] WANG P, JIANGL S, ZHENG P Q, Et al., Inducing mode analysis of rock burst in fault-affected zone with a hard-thick stratum occurrence, Environmental Earth Sciences, 78, 15, pp. 467-479, (2019)
[4] HU L, FENG X T, XIAO Y X, Et al., Effects of structural planes on rock burst position with respect to tunnel cross-sections: a case study involving a railway tunnel in China, Bulletin of Engineering Geology and the Environment, 1, pp. 1-21, (2019)
[5] GONG Fengqiang, YAN Jingyi, LI Xibing, Criteria for rock burst tendency based on linear energy storage law and residual elastic energy index, Chinese Journal of Rock Mechanics and Engineering, 37, 9, pp. 1993-2014, (2018)
[6] XU Chen, LIU Xiaoli, WANG Enzhi, Et al., Five-factor prediction classification of strain-type rock burst based on combined weight-ideal point method, Chinese Journal of Geotechnical Engineering, 39, 12, pp. 2245-2252, (2017)
[7] WU Shunchuan, ZHANG Chenxi, CHENG Ziqiao, Classified prediction method of rock burst intensity based on PCA-PNN principle, Journal of China Coal Society, 44, 9, pp. 2767-2776, (2019)
[8] ZHOU Keping, LIN Yun, HU Jianhua, Et al., Study of grading prediction of rock burst intensity based on entropy weight-normal cloud model, Rock and Soil Mechanics, 37, pp. 596-602, (2016)
[9] PEI Qitao, LI Haibo, LIU Yaqun, Et al., Prediction of rock burst based on improved grey evaluation model, Chinese Journal of Rock Mechanics and Engineering, 32, 10, pp. 2088-2093, (2013)
[10] PEI Qitao, LI Haibo, LIU Yaqun, Et al., A grey evaluation model for predicting rock burst proneness based on combination weight and its application, Rock and Soil Mechanics, 35, pp. 49-56, (2014)

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