重庆大学学报2026，Vol.49Issue(5)：107-117,11.DOI:10.11835/j.issn.1000-582X.2026.05.008
城市埋地供气管网系统抗震功能可靠性分析
Reliability analysis of the seismic performance of urban gas supply pipeline networks
摘要
Abstract
Gas supply systems are generally not allowed to operate under leakage conditions;pipeline networks with extensive leakage after earthquakes must be shut down immediately for inspection and repair.However,under low seismic intensity or minor pipeline damage,complete shutdown is often impractical due to urban gas demand,and the system may operate under slight leakage conditions.To evaluate the reliability of gas supply networks under such conditions,this study integrates post-earthquake pipeline failure states with a leakage model for buried gas pipelines and establishes a hydraulic analysis model that accounts for leakage.A three-state failure probability model is coupled with Monte Carlo method to randomly generate post-earthquake pipeline failure states.The functional reliability of the gas supply network under specified seismic conditions is then evaluated using a"pressure-driven"hydraulic analysis method,and the service status of user nodes after the earthquake is quantified.Based on this framework,the functional reliability of a low-pressure gas supply network under different seismic intensities is analyzed through 1 000 Monte Carlo simulations.The case study results show that the system maintains high seismic reliability under Ⅵ and Ⅶ earthquakes,while reliability decreases significantly under Ⅷ earthquakes.These findings provide a scientific basis for prost-earthquake reliability assessment and repair planning of urban gas supply pipeline networks.关键词
功能可靠性/泄漏/水力分析/Monte CarloKey words
gas supply/serviceability/leakage/hydraulic analysis/Monte Carlo method分类
建筑与水利引用本文复制引用
徐刚,佘阳阳,郑山锁,程思渊,史继宁,刘俊杰..城市埋地供气管网系统抗震功能可靠性分析[J].重庆大学学报,2026,49(5):107-117,11.基金项目
国家重点研发计划资助项目(2019YFC1509302) （2019YFC1509302）
国家自然科学基金资助项目(52278530) （52278530）
陕西省重点研发计划资助项目(2021ZDLSF06-10).Supported by National Key Research and Development Program Project(2019YFC1509302),National Natural Science Foundation of China(52278530),and Shaanxi Province Key Research and Development Program Project(2021ZDLSF06-10). （2021ZDLSF06-10）
