WANG Mingwu, DONG Hao, YE Hui, ZHOU Tianlong, JIN Juliang. A Connection Cloud-Evidence Theory Coupling Model for Prediction of Rockburst Intensity[J]. Applied Mathematics and Mechanics, 2018, 39(9): 1021-1029. doi: 10.21656/1000-0887.380286
Citation: WANG Mingwu, DONG Hao, YE Hui, ZHOU Tianlong, JIN Juliang. A Connection Cloud-Evidence Theory Coupling Model for Prediction of Rockburst Intensity[J]. Applied Mathematics and Mechanics, 2018, 39(9): 1021-1029. doi: 10.21656/1000-0887.380286

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

doi: 10.21656/1000-0887.380286
Funds:  The National Key R&D Plan(2016YFC0401303); The National Natural Science Foundation of China(41172274; 51579059)
  • Received Date: 2017-11-13
  • Rev Recd Date: 2018-01-31
  • Publish Date: 2018-09-15
  • 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.
  • [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.
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