基于联系云-证据理论的岩爆烈度预测模型

汪明武; 董昊; 叶晖; 周天龙; 金菊良

doi:10.21656/1000-0887.380286

基于联系云-证据理论的岩爆烈度预测模型

doi: 10.21656/1000-0887.380286

合肥工业大学土木与水利工程学院, 合肥 230009

基金项目: 国家重点研发计划（2016YFC0401303）；国家自然科学基金（41172274；51579059）

详细信息

作者简介:
汪明武(1972—），男，教授，博士，博士生导师（通讯作者. E-mail: wanglab307@foxmail.com).

中图分类号: TU457
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- 文章访问数: 898
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- 被引次数: 0
出版历程
- 收稿日期: 2017-11-13
- 修回日期: 2018-01-31
- 刊出日期: 2018-09-15

A Connection Cloud-Evidence Theory Coupling Model for Prediction of Rockburst Intensity

School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, P.R.China

Funds: The National Key R&D Plan(2016YFC0401303)； The National Natural Science Foundation of China（41172274； 51579059）

摘要

摘要: 岩爆机理复杂影响因素众多且呈现多种不确定性，应用云模型预测岩爆问题虽能刻画指标的随机性和模糊性，但很难模拟非正态分布的评价指标及存在冲突数据融合失真问题.为克服这些缺陷，探讨了岩爆烈度的联系云-证据预测模型.该模型首先基于联系数定量表达评价指标，通过联系云构建评价矩阵，并应用D-S证据理论得到基本概率赋值，进而基于距离函数的组合权重与融合均值证据预测样本的岩爆等级.实例应用及与其他方法对比结果表明，该模型应用于岩爆预测是有效可行的，且克服了传统云模型和证据理论的不足，为岩爆烈度分级预测提供了一种新的途径.
- 联系云 /
- 证据理论 /
- 岩爆 /
- 预测 /
- 组合权重
Abstract: Rockburst mechanism is a complex problem involving various uncertain factors. Although the cloud model can deal with the randomness and fuzziness of indicators for prediction of rockburst intensity, it cannot simulate the state of evaluation indicators in a finite distribution interval and address the distortion of data fusion. Herein, a connection cloud-evidence theory coupling model was built to remedy these defects. In this model, evaluation indicators were firstly expressed quantitatively by connection numbers. Then the evaluation matrix was constructed with the connection cloud model and the basic probability assignment based on the evidence theory was obtained. Finally, with the combination weight obtained from a distance function, classification of the rockburst intensity was determined according to the mean evidence value. The case study and comparison with other methods show that, the proposed model is effective and feasible for the prediction of rockburst intensity. It can overcome the shortcomings of the normal cloud model and the evidence theory, making a novel method for comprehensive prediction of rockburst intensity.
- connection cloud /
- evidence theory /
- rockburst /
- prediction /
- combination weight

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参考文献(17)

[1]	许迎年, 徐文胜, 王元汉, 等. 岩爆模拟试验及岩爆机制研究[J]. 岩石力学与工程学报, 2002,21(10): 1462-1466.(XU Yingnian, XU Wensheng, WANG Yuanhan, et al. Simulation testing and mechanism studies on rockburst[J]. Chinese Journal of Rock Mechanics and Engineering,2002,21(10): 1462-1466.(in Chinese))
[2]	苏国韶, 张小飞, 燕柳斌. 基于案例推理的岩爆预测方法[J]. 采矿与安全工程学报, 2008,25(1): 63-67.(SU Guoshao, ZHANG Xiaofei, YAN Liubin. Rockburst prediction method based on case reasoning pattern recognition[J]. Journal of Mining and Safety Engineering,2008,25(1): 63-67.(in Chinese))
[3]	王迎超, 尚岳全, 孙红月, 等. 基于功效系数法的岩爆烈度分级预测研究[J]. 岩土力学, 2010,31(2): 529-534.(WANG Yingchao, SHANG Yuequan, SUN Hongyue, et al. Study of prediction of rockburst intensity based on efficacy coefficient method[J]. Rock and Soil Mechanics ,2010,31(2): 529-534.(in Chinese))
[4]	汪明武, 李丽, 金菊良. 岩爆预测的改进集对分析模型[J]. 岩土力学, 2008,28(S1): 511-518.(WANG Mingwu, LI Li, JIN Juliang. An improved set pair analysis model for the prediction of rockburst[J]. Rock and Soil Mechanics,2008,28(S1): 511-518.(in Chinese))
[5]	陈祥, 祈小博, 蔡新滨, 等. 可拓综合评价方法在岩爆判别中的应用[J]. 北京交通大学学报, 2009,33(1): 99-108.(CHEN Xiang, QI Xiaobo, CAI Xinbin, et al. Extensional evaluation method and its application in the judgments of rockburst[J]. Journal of Beijing Jiaotong University,2009,33(1): 99-108.(in Chinese))
[6]	罗磊, 曹平. 深部巷道岩爆加权距离判别法模型的分析和应用[J]. 中南大学学报(自然科学版), 2012,43(10): 222-226.(LUO Lei, CAO Ping. Model of weighted distance discriminant analysis and application for deep roadway[J]. Journal of Central South University(Science and Technology),2012,43(10): 222-226.(in Chinese))
[7]	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), 2017,48(5):1268-1275.(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),2017,48(5): 1268-1275.(in Chinese))
[8]	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): 2245-2252.(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 Rock Mechanics and Engineering,2017,39(12): 2245-2252.(in Chinese))
[9]	高玮. 基于蚁群聚类算法的岩爆预测研究[J]. 岩土工程学报, 2010,32(6): 874-880.(GAO Wei. Prediction of rock burst based on ant colony clustering algorithm[J]. Chinese Journal of Geotechnical Engineering,2010,32(6): 874-880.(in Chinese))
[10]	张乐文, 张德永, 李术才, 等. 基于粗糙集理论的遗传-RBF神经网络在岩爆预测中的应用[J]. 岩土力学, 2012,33(S1): 270-276.(ZHANG Lewen, ZHANG Deyong, LI Shucai, et al. Application of RBF neural network to rockburst prediction based on rough set theory[J]. Rock and Soil Mechanics,2012,33(S1): 270-276.(in Chinese))
[11]	汪明武, 魏东方, 周欣玮, 等. 基于联系矩阵的围岩稳定性组合评价模型[J]. 应用数学和力学, 2015,36(3): 294-302.(WANG Mingwu, WEI Dongfang, ZHOU Xinwei, et al. Connectional matrix-based combination evaluation method for surrounding rock stability[J]. Applied Mathematics and Mechanics,2015,36(3): 294-302.(in Chinese))
[12]	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(S1): 596-602.(ZHOU Keiping, LIN Yun, HU Jianhua, et al. Grading prediction of rockburst intensity based on entropy and normal cloud model[J]. Rock and Soil Mechanics,2016,37(S1): 596-602.(in Chinese))
[13]	贾义鹏, 吕庆, 尚岳全, 等. 基于证据理论的岩爆预测[J]. 岩土工程学报, 2014,36(6): 1079-1086.(JIA Yipeng, L Qing, SHANG Yuequan, et al. Rockburst prediction based on evidence theory[J]. Chinese Journal of Geotechnical Engineering,2014,36(6): 1079-1086.(in Chinese))
[14]	汪明武, 金菊良. 联系数理论与应用[M]. 北京: 科学出版社, 2017.(WANG Mingwu, JIN Juliang. The Theory and Applications of Connection Numbers [M]. Beijing: Science Press, 2017.(in Chinese))
[15]	LI D Y, HAN J W, SHI X Mi, et al. Knowledge representation and discovery based on linguistic atoms[J]. Knowledge-Based Systems,1988,10(7): 431-440.
[16]	YAN S D, XIAN N T. Damage identification of offshore platform based on D-S evidence theory[J]. Advanced Materials Research,2011,255: 314-318.
[17]	DENG Y, SHI W K, ZHU Z F, et al. Combining belief functions based on distance of evidence[J]. Decision Support Systems,2004,38(3): 489-493.