中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (7): 26-32.doi: 10.16265/j.cnki.issn1003-3033.2019.07.005

• 安全系统学 • 上一篇    下一篇

基于改进组合赋权的岩爆多维云模型预测研究

黄建1, 夏元友1 教授, 吝曼卿2   

  1. 1 武汉理工大学 土木工程与建筑学院,湖北 武汉 430070;
    2 武汉工程大学 资源与土木工程学院,湖北 武汉 430070
  • 收稿日期:2019-04-06 修回日期:2019-05-25 出版日期:2019-07-28 发布日期:2020-10-21
  • 作者简介:黄 建 (1994—),男,湖北襄阳人,硕士研究生,研究方向为岩土体地面与地下工程。E-mail: alps102@whut.edu.com。
  • 基金资助:
    国家自然科学基金青年基金资助(51504176);高等学校博士学科点专项科研基金资助(20110143110017)。

Study on prediction of rock burst by multi-dimensional cloud model based on improved combined weight

HUANG Jian1, XIA Yuanyou1, LIN Manqing2   

  1. 1 School of Civil Engineering,Wuhan University of Technology,Wuhan Hubei 430070,China;
    2 School of Resource and Civil Engineering, Wuhan Institute of Technology,Wuhan Hubei 430070,China
  • Received:2019-04-06 Revised:2019-05-25 Online:2019-07-28 Published:2020-10-21

摘要: 岩爆是地下开挖工程主要的地质灾害之一,其烈度分级预测是一个急需解决的世界性难题。针对其预测过程中的不确定性,选取切向应力与岩石单轴抗压强度比σθc、岩石单轴抗压强度与抗拉强度比σct、弹性变形能指数Wet建立评价指标体系,以改进熵权-基于指标相关性的指标权重确定方法(CRITIC)综合计算预测指标权重,结合不确定性人工智能理论,将逆向云发生器算法用于确立多维云模型的3个数字特征,生成所有预测指标的多维云模型。用国内外48组典型岩爆实例数据检验本文模型的准确性与有效性,并与基于权重融合的云模型、一维正态云模型的预测结果进行对比。结果表明:该模型应用于岩爆预测有更高的准确性。

关键词: 岩石力学, 岩爆预测, 多维云模型, 熵权, 逆向云发生器算法, 基于指标相关性的指标权重确定方法(CRITIC)

Abstract: Rockburst is one of the main geological disasters in underground excavation and the classification prediction of its intensity is a worldwide problem that needs to be solved urgently. In view of the uncertainty in prediction, the rock shear stress to uniaxial compressive strength ratio σθc, the rock uniaxial compressive strength to tension strength ratio σct and elastic energy index Wet were selected to define the rockburst evaluation indexes. The entropy weight combined with improved CRITIC method was adopted to determine the weighting coefficient of each evaluation index. Combined with the theory of artificial intelligence with uncertainty, the algorithm of backward cloud generator was used to establish 3 digital features of the multi-dimensional cloud model and generate the multi-dimensional cloud model including all the prediction indicators. Finally, the accuracy and validity of the proposed model were validated with case data of 48 groups of typical rockburst both at home and abroad. Furthermore, results obtained by the proposed model were compared with those got by cloud model based on weighted fusion and one-dimensional cloud model. The results show that the proposed model has higher accuracy in rock burst prediction.

Key words: rock mechanics, rockburst prediction, multidimensional cloud model, entropy weight, algorithm of reverse cloud generator, criteria importance though intercriteria correlation(CRITIC)

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