摘要
为了对隧道坍塌进行更加准确有效的风险评价,提出一种改进条件概率确定过程的贝叶斯网络隧道坍塌风险评价方法。结合工程实际,选取5个中间事件、23个基本事件构建隧道坍塌贝叶斯网络结构;通过联系云确定根节点事件属于各标准风险等级的隶属度,进而由隶属度确定节点先验概率;依据DS证据理论对部分复杂度最小的专家决策信息进行融合,结合节点事件权重定义“状态危险贡献值”对条件概率确定过程进行改进。将该方法应用到广西来宾市老山隧道工程,计算隧道坍塌风险等级概率并进行事故原因诊断。研究结果表明:该隧道坍塌风险级别为“高度风险”,与实际开挖情况一致;断层破碎带、围岩强度、施工开挖循环进尺、施工步距等是引发隧道坍塌的主要原因。最后与传统条件概率确定方法所得结果进行对比分析,表明本文方法较传统方法在提高计算效率的同时准确性也更高。
To achieve a more accurate and effective safety risk evaluation for tunnel collapse, a new method to evaluate the possibility of tunnel collapse was proposed by improving the conditional probability determination process of Bayesian networks. 5 intermediate events and 23 basic events were selected to construct the Bayesian network structure of tunnel collapse. The membership degree of the root node event belonging to each standard risk level was determined by connection clouds, and then the prior probability of the node was determined by the membership degree. Part of expert decision information with minimum complexity was fused according to DS evidence theory, and the conditional probability determination process was improved by combining the weights of node events and the "state risk contribution value". This method was applied to Laoshan tunnel project in Labin,Guangxi Province, and to calculate the risk probability of tunnel collapse and diagnose the cause of the accident.The results show that the risk level of tunnel collapse belongs to "high risk". The fault fracture zone, intensity of surrounding rock, excavation cycle footage and construction step are the main reasons for tunnel collapse. Finally,this method is compared with traditional method, showing that the method presented in this paper has higher computational efficiency and accuracy than traditional method.
作者
陈钊
袁航
黄鹏宇
周子龙
王秉
CHEN Zhao;YUAN Hang;HUANG Pengyu;ZHOU Zilong;WANG Bing(Guangxi Beitou Highway Construction and Investment Group Co.Ltd.,Nanning 530028,China;School of Civil Engineering,Central South University,Changsha 410083,China;School of Resources and Safety Engineering,Central South University,Changsha 410083,China)
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第1期327-340,共14页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(51534008)
国家社会科学基金重大项目(20&ZD120)。
关键词
隧道坍塌
风险评价
贝叶斯网络
联系云
DS证据理论
tunnel collapse
risk evaluation
Bayesian networks
connection clouds
DS evidence theory
