), 张国华1, 王成汤1,2, 钟国强3 1.中国科学院武汉岩土力学研究所 岩土力学与工程国家重点实验室, 武汉 430071
2.中国科学院大学, 北京100049
3.山东省交通规划设计院, 济南 250031
收稿日期:2019-01-14出版日期:2020-08-28发布日期:2020-08-18通讯作者:王浩E-mail:hwang@whrsm.ac.cn作者简介:陈 舞(1994-),男,江西省萍乡市人,博士生,现主要从事隧道安全风险评价方法方面的研究基金资助:国家自然科学基金重点项目(41731284);国家自然科学基金面上项目(51579235);国家自然科学基金面上项目(41472288)Evaluation of Tunnel Collapse Susceptibility Based on T-S Fuzzy Fault Tree and Bayesian Network
CHEN Wu1,2, WANG Hao1(), ZHANG Guohua1, WANG Chengtang1,2, ZHONG Guoqiang3 1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Shandong Provincial Communication Planning and Design Institute, Jinan 250031, China
Received:2019-01-14Online:2020-08-28Published:2020-08-18Contact:WANG Hao E-mail:hwang@whrsm.ac.cn摘要/Abstract
摘要： 坍塌是钻爆法隧道施工过程中常见的灾害事故之一.为了有效预防坍塌事故,为隧道施工安全风险分析和管理提供决策依据,将T-S模糊故障树和贝叶斯网络进行互补融合,提出了一种基于两者的钻爆法施工隧道坍塌可能性评价方法.根据T-S模糊故障树向贝叶斯网络转化的方法来确定贝叶斯网络模型和条件概率表;利用模糊数和模糊子集分别描述节点的故障状态和故障概率;运用贝叶斯网络双向推理算法进行计算.该方法可以利用根节点的故障概率模糊子集和施工中实际故障状态两种不同的正向推理方式计算隧道坍塌可能性,并且可以依据根节点的重要度分析结果排查故障,同时可以通过反向推理计算根节点的后验概率诊断故障.两个工程实例的应用结果表明,该方法能够科学、合理地评价隧道坍塌的可能性并确定关键致险因子,可作为隧道施工安全保障和管理的决策工具.
关键词: 钻爆法施工隧道, 坍塌可能性, T-S模糊故障树, 贝叶斯网络, 模糊数, 模糊子集, 重要度
Abstract: Collapse is one of the common disasters in the process of drilling and blasting tunnel construction. In order to effectively prevent the collapse accident and provide decision-making basis for safety risk analysis and management of tunnel construction, the T-S fuzzy fault tree and Bayesian network were combined, and an evaluation method based on both of them was proposed to calculate tunnel collapse possibility. According to the transformation of T-S fuzzy fault tree to Bayesian network, the Bayesian network model and conditional probability table were determined. In addition, fuzzy number and fuzzy subset were used to describe the fault state and probability of nodes respectively, and the bidirectional reasoning algorithm of Bayesian network was used to calculate. This method can use two different forward inferences to calculate the possibility of tunnel collapse, including the fuzzy subset of root node fault probability and the actual fault state in construction. At the same time, it can check the fault according to the result of importance analysis of root nodes, and can calculate the posterior probability of fault diagnosis of root nodes by back inference. Finally, the application of two engineering examples shows that the method can scientifically evaluate the possibility of tunnel collapse and determine key risk factors. This method can be used as a decision-making tool for tunnel construction safety assurance and management.
Key words: construction tunnel by drilling and blasting, collapse possibility, T-S fuzzy fault tree, Bayesian network, fuzzy number, fuzzy subset, importance
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