摘要
大坝风险评估对大坝风险控制和安全管理具有重要意义。贝叶斯网络作为不确定性表达和概率推理的工具,是大坝风险评估的有效手段。以贝叶斯网络方法为基础,以某水电站大坝为工程实例,结合工程特性和运行环境,通过对该大坝风险因素的识别和风险因素关联性的确定,构建大坝风险评估的贝叶斯网络;然后以专家知识和工程经验为依据,确定各风险因素之间的关联程度,在此基础上利用贝叶斯网络正向推理功能推求该水电站大坝安全的总风险,并基于风险标准对该大坝安全风险进行评估,利用贝叶斯网络逆向推理功能计算各风险因素的重要度,以确定出威胁大坝安全的关键风险因素,为该大坝风险管理提供决策支持。此推理模型的应用为大坝风险评估提供了一条新的有效途径。
Risk assessment of dams is of great significance to risk control and safety management of dams.As a tool of uncertainty expression and probabilistic reasoning,Bayesian network is an effective method for dam risk assessment.Based on the Bayesian network method,taking a hydropower dam as a project case,combining the engineering characteristics and operating environment,the Bayesian network for dam risk assessment is constructed by identifying the dam risk factors and determining the correlation of the risk factors.The correlation degrees of dam risk factors are determined by expert knowledge and engineering experience.On this basis,the total safety risk of the dam is deduced by using the forward inference function of Bayesian network,and then the safety risk of the dam is evaluated based on the risk standard.Moreover,the importance of each risk factor is calculated by using the inverse inference function of Bayesian network to determine the key risk factors threatening the dam safety,and to provide decision support for dam risk management.The application of Bayesian network inference model enriches the risk assessment method of dams.
作者
唐贤琪
施玉群
杨海云
陈泽钦
何金平
TANG Xianqi;SHI Yuqun;YANG Haiyun;CHEN Zeqin;HE Jinping(School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,China;State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China;Electric Power Research Institute of State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350007,China)
出处
《武汉大学学报（工学版）》
CAS
CSCD
北大核心
2024年第2期152-158,共7页
Engineering Journal of Wuhan University
基金
国网福建省电力有限公司科技项目(编号:52130417000M)
国家重点研究发展计划项目(编号:2018YFC0406906)。
关键词
大坝
风险评估
贝叶斯网络推理
失事概率
节点重要度
dam
risk assessment
Bayesian network inference
failure probability
node importance
