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| Prediction of rock burst intensity based on unascertained measure-intuitionistic fuzzy set |
| WU Shuliang1，2，3，YANG Shan3，HUO Liang1，2 |
| (1. Key Laboratory for Digital Land and Resources of Jiangxi Province，East China University of Technology，Nanchang，Jiangxi 330013，China；2. School of Earth Sciences，East China University of Technology，Nanchang，Jiangxi 330013，China；3. School of Resources and Safety Engineering，Central South University，Changsha，Hunan 410083，China) |
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Abstract Rock burst is one of the main hidden safety hazards in deep underground engineering. In order to effectively deal with the fuzzy information in the process of rock burst intensity prediction，a prediction model of rock burst intensity based on unascertained measure-intuitionistic fuzzy set was established. Five indices including uniaxial compressive strength of rock ，the ratio of rock's compressive-tensile strength ，the stress coefficient of rock ，the elastic energy index of rock Wet and integrality coefficient Kv were chosen as the prediction index of rock burst. Firstly，the single index measure function of unascertained measure theory was used to deal with the measured value of rock burst index to obtain the fuzzy membership degree of index based on intuitionistic fuzzy set and construct the evaluation matrix of single index intuitionistic fuzzy. Then，by synthesizing various subjective and objective weighting methods，the intuitionistic fuzzy set weight vector of multi-source weights was constructed，and the score value of samples and the rock burst intensity grade were gained. Finally，the influence of index weight on rock burst prediction results was obtained by analyzing the fuzziness of index weight based on intuitionistic fuzzy sets. The established model was applied to 20 typical rock burst cases at home and abroad，and the predicted results were compared with the actual rock burst cases and the predicted results of three other models. The results show that the prediction results of the prediction model of rock burst intensity based on unascertained measure-intuitionistic fuzzy set are consistent with the actual situation，and the sensitivity of index weight fluctuation is independent of the weight value. The established model improves the fuzzy information representation ability of rock burst prediction and reduces the influence of sample quality on rock burst prediction model.
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| [1] 李春林. 岩爆条件和岩爆支护[J]. 岩石力学与工程学报，2019，38(4)：674–682.(LI Chunlin. Rockburst conditions and rockburst support[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(4)：674–682.(in Chinese))
[2] 江飞飞，周 辉，刘 畅，等. 地下金属矿山岩爆研究进展及其预测与防治[J]. 岩石力学与工程学报，2018，37(5)：956–972. (JIANG Feifei，ZHOU Hui，LIU Chang，et al. Progress，prediction and prevention of rockbursts in underground metal mines[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(5)：956–972.(in Chinese))
[3] 于 群. 深埋隧洞岩爆孕育过程及预警方法研究[博士学位论文][D]. 大连：大连理工大学，2016.(YU Qun. Study on rockburst nucieation process and early warning method of deep-buried tunnels[Ph. D. Thesis][D]. Dalian：Dalian University of Technology，2016.(in Chinese))
[4] JIE X，JIANG J，NING X，et al. A new energy index for evaluating the tendency of rockburst and its engineering application[J]. Engineering Geology，2017，230：46–54.
[5] 张传庆，俞 缙，陈 珺，等. 地下工程围岩潜在岩爆问题评估方法[J]. 岩土力学，2016，37(增1)：341–349.(ZHANG Chuanqing，YU Jin，CHEN Jun，et al. Evaluation method for potential rockburst in underground engineering[J]. Rock and Soil Mechanics，2016，37(Supp.1)：341–349.(in Chinese))
[6] 贾义鹏. 岩爆预测方法与理论模型研究[博士学位论文][D]. 杭州：浙江大学，2014.(JIA Yipeng. Study on prediction method and theorial model of rockburst[Ph. D. Thesis][D]. Hangzhou：Zhejiang University，2014.(in Chinese))
[7] PAN J，LIU S，WANG S，et al. A new theoretical view of rockburst and its engineering application[J]. Advances in Civil Engineering，2018：1–12.
[8] LI T，MA C，ZHU M，et al. Geomechanical types and mechanical analyses of rockbursts[J]. Engineering Geology，2017，222：72–83.
[9] 郭建强，刘新荣. 强度准则与岩爆判据统一的研究[J]. 岩石力学与工程学报，2018，37(增1)：3 340–3 352.(GUO Jianqiang，LIU Xinrong. Study on the uniformity between strength criterion and rockburst criterion[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(Supp.1)：3 340–3 352.(in Chinese))
[10] AMICI R，PEACH G，NADEEM M. TBM in Azad Kashmir region of Pakistan：in situ stress measurements for rockburst prevision[J]. Geotechnical Research，2018：1–13.
[11] 马天辉，唐春安，唐烈先，等. 基于微震监测技术的岩爆预测机制研究[J]. 岩石力学与工程学报，2016，35(3)：470–483.(MA Tianhui，TANG Chunan，TANG Liexian，et al. Mechanism of rock burst forcasting based on micro-seismic monitoring technology[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(3)：470–483. (in Chinese))
[12] GAO W. Non-linear dynamic model of rock burst based on evolutionary neural network[J]. International Journal of Modern Physics B，2008，22(09n11)：1 518–1 523.
[13] 李 宁，王李管，贾明涛. 基于粗糙集理论和支持向量机的岩爆预测[J]. 中南大学学报：自然科学版，2017，(5)：1 268–1 275.(LI Ning，WANG Liguan，JIA Mingtao. Rockburst prediction based on rough set theory and support vector machine[J]. Journal of Central South University：Science and Technology，2017，(5)：1 268–1 275.(in Chinese))
[14] 陈鹏宇，余宏明，师华鹏. 基于权重反分析和标准化模糊综合评价的岩爆预测模型[J]. 岩石力学与工程学报，2014，33(10)：2 154–2 160.(CHEN Pengyu，YU Hongming，SHI Huapeng. Prediction model for rockburst based on weighted back analysis and standardized fuzzy comprehensive evaluation[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(10)：2 154–2 160.(in Chinese))
[15] 詹金武，李 涛，谭忠盛，等. 基于人工智能专家系统的岩爆烈度分级预测研究[J]. 土木工程学报，2017，50(增1)：99–104.(ZHAN Jinwu，LI Tao，TAN Zhongcheng，et al. Study on prediction of rockburst intensity classification with artificial intelligence expert system[J]. China Civil Engineering Journal，2017，50(Supp.1)：99–104.(in Chinese))
[16] 赵国彦，李振阳，梁伟章，等. 岩爆预测的Vague集模型[J]. 矿冶工程，2018，38(1)：1–4.(ZHAO Guoyan，LI Zhenyang，LIANG Weizhang，et al. Vague set model for rockburst prediction[J]. Mining and Metallurgical Engineering，2018，38(1)：1–4.(in Chinese))
[17] 过 江，张为星，赵 岩. 岩爆预测的多维云模型综合评判方法[J]. 岩石力学与工程学报，2018，37(5)：1 199–1 206.(GUO Jiang，ZHANG Weixing，ZHAO Yan. A multidimensional cloud model for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(5)：1 199–1 206.(in Chinese))
[18] 董 源，裴向军，张 引，等. 基于组合赋权–云模型理论的岩爆预测研究[J]. 地下空间与工程学报，2018，14(增1)：409–415. (DONG Yuan，PEI Xiangjun，ZHANG Yin，et al. Prediction of rock burst based on combination weighting and cloud model theory[J]. Chinese Journal of Underground Space and Engineering，2018，14(Supp.1)：409–415.(in Chinese))
[19] 徐 琛，刘晓丽，王恩志，等. 基于组合权重–理想点法的应变型岩爆五因素预测分级[J]. 岩土工程学报，2017，39(12)：2 245–2 252.(XU Chen，LIU Xiaoli，WANG Enzhi，et al. Prediction and classification of strain mode rockburst based on five-factor criterion and combined weight-ideal point method[J]. Chinese Journal of Geotechnical Engineering，2017，39(12)：2 245–2 252.(in Chinese))
[20] 李登峰. 直觉模糊集决策与对策分析方法[M]. 北京：国防工业出版社. 2012：12–36.(LI Dengfeng. Intuitionistic fuzzy set decision and game analysis methodologies[M]. Beijing：National Defense Industry Press，2012：12–36.(in Chinese))
[21] 王 超. 基于未确知测度理论的冲击地压危险性综合评价模型及应用研究[博士学位论文][D]. 北京：中国矿业大学(北京)，2011.(WANG Chao. Research of rockburst risk comprehensive evaluation method based on unascertained measurement model and application[Ph. D. Thesis][D]. Beijing：China University of Mining and Technology(Beijing)，2011.(in Chinese))
[22] 王元汉，李卧东，李启光，等. 岩爆预测的模糊数学综合评判方法[J]. 岩石力学与工程学报，1998，17(5)：493–501.(WANG Yuanhan，LI Wodong，LI Qiguang，et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，1998，17(5)：493–501.(in Chinese))
[23] 周科平，林 允，胡建华，等. 基于熵权—正态云模型的岩爆烈度分级预测研究[J]. 岩土力学，2016，27(增1)：596–602.(ZHOU Keping，LIN Yun，HU Jianhua，et al. Grading prediction of rockburst intensity based on entropy and normal cloud model[J]. Rock and Soil Mechanics，2016，27(Supp.1)：596–602.(in Chinese))
[24] 周科平，雷 涛，胡建华. 深部金属矿山RS-TOPSIS岩爆预测模型及其应用[J]. 岩石力学与工程学报，2013，22(增2)：3 705–3 711. (ZHOU Keping，LEI Tao，HU Jianhua. RS-TOPSIS model of rockburst prediction in deep metal mines and its application [J]. Chinese Journal of Rock Mechanics and Engineering，2013，22(Supp.2)：3 705–3 711.(in Chinese))
[25] 史秀志，周 健，董 蕾，等. 未确知测度模型在岩爆烈度分级预测中的应用[J]. 岩石力学与工程学报，2010，29(增1)：2 720–2 726. SHI Xiuzhi，ZHOU Jian，DONG Lei，et al. Application of unascertained measurement model to prediction of classification of rockburst intensity[J]. Chinese Journal of Rock Mechanics and Engineering，2010，29(Supp.1)：2 720–2 726.(in Chinese)) |
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2020, Vol. 39 
