基于改进综合赋权的岩爆倾向性评价方法研究

doi:10.13722/j.cnki.jrme.2019.1142

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (915 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了解决岩爆倾向性评价中指标权重计算随机性大导致预测精度偏低以及岩爆倾向性等级评判过于单一化等问题，提出一种基于改进综合赋权的岩爆倾向性评价方法。综合选取岩性条件、应力条件和围岩条件对应的15个因素作为岩爆倾向性判别指标，应用综合赋权方法，获得综合权重，在此基础上，分别采用云正态模型和理想点法对具体工程案例的岩爆倾向性进行评价，判断岩爆倾向性等级，验证方法准确度与可靠性。研究结果表明：能量储耗指数k，T准则，动态DT参数、弹性能量指数Wet和应力指数S五个指标对岩爆倾向性影响较大，改进综合赋权法计算得到的指标权重结果更加合理，而与理想点法相比，云正态模型获得的岩爆倾向性等级评价结果要更加准确，研究结果将为矿山、隧道、水电站等岩土工程岩爆倾向性预测问题提供新思路。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李克钢1
	2
	李明亮1
	2
	秦庆词1
	2

关键词 ：岩石力学, 岩爆倾向性预测, 改进, 云模型, 理想点, 综合权重

Abstract：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.

Key words： rock mechanics prediction of rock burst tendency improvement cloud model ideal point comprehensive weight

引用本文:

李克钢1，2，李明亮1，2，秦庆词1，2. 基于改进综合赋权的岩爆倾向性评价方法研究[J]. 岩石力学与工程学报, 2020, 39(S1): 2751-2762.
LI Kegang1，2，LI Mingliang1，2，QIN Qingci1，2. Research on evaluation method of rock burst tendency based on improved comprehensive weighting. , 2020, 39(S1): 2751-2762.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2019.1142 或 http://rockmech.whrsm.ac.cn/CN/Y2020/V39/IS1/2751

[21]	李德毅，孟海军，史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展，1995，32(6)：15–20.(LI Deyi，MENG Haijun，SHI Xuemei，Membership cloud and membership generatiors[J]. Journal of Computer Research and Development，1995，32(6)：15–20.(in Chinese))
[1]	何满潮，苗金丽，李德建，等. 深部花岗岩试样岩爆过程实验研究[J]. 岩石力学与工程学报，2007，26(5)：865–876.(HE Manchao，MIAO Jinli，LI Dejian，et al. Experimental study on rock burst process of deep granite samples[J]. Chinese Journal of Rock Mechanics and Engineering，2007，26(5)：865–876. (in Chinese))
[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[J]. Environmental Earth Sciences，2019，78(15)：467–479.
[11]	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 comprehensive evaluation method of multidimensional cloud model for rock burst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(5)：1 199– 1 206.(in Chinese))
[13]	QIU D H ，CHEN J P，WANG Q，et al. Research on rock burst prediction with fuzzy comprehensive evaluations based on rough set[C]// International Young Scholars Symposium on Rock Mechanics. [S. l.]：[s. n.]，2008：831–835.
[9]	裴启涛，李海波，刘亚群，等. 基于改进的灰评估模型在岩爆中的预测研究[J]. 岩石力学与工程学报，2013，32(10)：2 088– 2 093.(PEI Qitao，LI Haibo，LIU Yaqun，et al. Prediction of rock burst based on improved grey evaluation model[J]. Chinese Journal of Rock Mechanics and Engineering，2013，32(10)：2 088–2 093.(in Chinese))
[19]	梁志勇. 锦屏二级水电站引水隧洞岩爆预测及防治对策研究[硕士学位论文][D]. 成都：成都理工大学，2004. (LIANG Zhiyong. Study on the prediction and prevention of rock burst in the diversion tunnel of Jinping II hydropower station[M. S. Thesis][D]. Chengdu：Chengdu University of Technology，2005.(in Chinese))
[5]	宫凤强，闫景一，李夕兵. 基于线性储能规律和剩余弹性能指数的岩爆倾向性判据[J]. 岩石力学与工程学报，2018，37(9)：1 993–2 014.(GONG Fengqiang，YAN Jingyi，LI Xibing. Criteria for rock burst tendency based on linear energy storage law and residual elastic energy index[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(9)：1 993–2 014.(in Chinese))
[8]	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，2016，37(增1)：596–602.(ZHOU Keping，LIN Yun，HU Jianhua，et al. Study of grading prediction of rock burst intensity based on entropy weight-normal cloud model[J]. Rock and Soil Mechanics，2016，37(Supp.1)：596–602.(in Chinese))
[15]	张晓燕. 基于模糊综合评价的岩爆危险性预测[硕士学位论文][D]. 邯郸：河北工程大学，2017.( ZHANG Xiaoyan．Study on risk forecast of rock burst based on fuzzy comprehensive evaluation[M. S. Thesis][D]．Handan：Hebei University of Engineering，2017.(in Chinese))
[18]	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 rock burst nucleation process and early warning method of deep-buried tunnels [Ph. D. Thesis][D]. Dalian：Dalian University of Technology，2016.(in Chinese))
[24]	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，FEI 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))
[2]	刘宁，张春生，单治钢，等. 岩爆风险下深埋长大隧洞支护设计与工程实践[J]. 岩石力学与工程学报，2019，38(增1)：2 934–2 943. (LIU Ning，ZHANG Chunsheng，SHAN Zhigang，et al. Engineering practice of support for long and deep buried tunnel under rock burst risk[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(Supp.1)：2 934–2 943.(in Chinese))
[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[J]. Bulletin of Engineering Geology and the Environment，2019，(1)：1–21.
[6]	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. Five-factor prediction classification of strain-type rock burst based on combined weight-ideal point method[J]. Chinese Journal of Geotechnical Engineering，2017，39(12)：2 245–2 252.(in Chinese))
[7]	吴顺川，张晨曦，成子桥. 基于PCA-PNN原理的岩爆烈度分级预测方法[J]. 煤炭学报，2019，44(9)：2 767–2 776.(WU Shunchuan，ZHANG Chenxi，CHENG Ziqiao. Classified prediction method of rock burst intensity based on PCA-PNN principle[J]. Journal of China Coal Society，2019，44(9)：2 767–2 776.(in Chinese))
[10]	裴启涛，李海波，刘亚群，等. 基于组合赋权的岩爆倾向性预测灰评估模型及应用[J]. 岩土力学，2014，35(增1)：49–56.(PEI Qitao，LI Haibo，LIU Yaqun，et al. A grey evaluation model for predicting rock burst proneness based on combination weight and its application[J]. Rock and Soil Mechanics，2014，35(Supp.1)：49–56.(in Chinese))
[12]	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. A rock burst prediction model based on back analysis of weights and standardized fuzzy comprehensive evaluation[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(10)：2 154–2 160.(in Chinese))
[14]	FAN C，ANYE C，LINMING D，et al. Method of coal burst hazard assessment based on region division and identification of main impact factors[J]. Journal of China Coal Society，2018，43(3)：607–615.
[16]	李明亮，李克钢，秦庆词，等.基于改进组合赋权–TOPSIS法的岩爆倾向性评判模型[J]. 中国安全生产科学技术，2020，16(3)：74–80.(LI Mingliang，LI Kegang，QIN Qingci，et al. Judgment model of rock burst tendency based on improved combination weighting-TOPSIS method[J]. China work safety science and technology，2020，16(3)：74–80.(in Chinese))
[17]	周科平，林允，邓红卫，等. 熵权–云模型对岩爆等级的预测[J]. 中国有色金属学报：2016，26(7)：1 995–2 002.(ZHOU Keping，LIN Yun，DENG Hongwei，et al. Entropy weight-cloud model for prediction of rock burst grade[J]. Transactions of Nonferrous Metals Society of China，2016，26(7)：1 995–2 002.(in Chinese))
[20]	江权，冯夏庭，苏国韶，等. 高地应力下拉西瓦水电站地下洞室群稳定性分析[J]. 水力发电学报，2010，29(5)：132–140.(JIANG Quan，FENG Xiating，SU Guoshao，et al. Stability analysis of large underground caverns in Laxiwa hydropower plant under high crustal stress[J]. Journal of Hydroelectric Engineering，2010，29(5)：132–140.(in Chinese))
[22]	龚剑，胡乃联，崔翔，等. 基于AHP-TOPSIS 评判模型的岩爆倾向性预测[J]. 岩石力学与工程学报，2014，33(7)：1 442– 1 448.(GONG Jian，HU Nailian，CUI Xiang，et al. Rock burst tendency prediction based on AHP-TOPSIS evaluation model[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(7)：1 442–1 448.(in Chinese))
[23]	张乐文，邱道宏，李术才，等. 基于粗糙集和理想点法的隧道围岩分类研究[J]. 岩土力学，2011，32(增1)：171–175.(ZHANG Lewen，QIU Daohong，LI Shucai，et al. Study of tunnel surrounding rock classification based on rough set and ideal point method[J]. Rock and Soil Mechanics，2011，32(Supp.1)：171–175.(in Chinese))