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基于复杂网络的燃气管线破裂灾害链风险分析 被引量:11

Risk analysis on rupture disaster chain of gas pipeline based on complex network
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摘要 为提高燃气管线突发事件应急处置决策水平和应急响应能力及效率,采用复杂网络理论和灾害链演化机理对燃气管线破裂灾害事件影响进行耦合分析,构建燃气管线破裂灾害链网络和风险评估模型,并计算得出燃气管线破裂灾害链风险度。为更准确地表达无传播路径的灾害事件之间的关系,将灾害网络中所有最短路径长度的最大值作为其最短路径长度,计算表明这种算法更符合灾害传播实际情况。结果表明:通过燃气管线破裂灾害链风险分析,能够为燃气管线灾害风险控制措施和方案制定提供参考,有利于提高燃气管线破裂灾害事件的应急处置能力和决策水平。 In order to improve the emergency disposal ability and decision-making level of gas pipeline rupture disaster events,the complex network theory was applied to carry out the coupling analysis on the rupture disaster event influence of gas pipeline.Taking the rupture event of gas pipeline as the original event,and based on the evolution mechanism of disaster chain and the complex network model,the disaster chain network and risk assessment model of the gas pipeline rupture were constructed on the basis of accident cases data analysis and expert review,and the risk degree of the disaster chain of gas pipeline rupture was calculated.In order to express the relationship between the disaster events without propagation path more accurately,the maximum value of all the shortest paths in the disaster network was taken as its shortest path,and the calculation indicated that this algorithm was more in line with the actual situation of disaster propagation.The results showed that the risk analysis on the disaster chain of gas pipeline rupture can provide reference for formulating the control measures and schemes of gas pipeline disaster risk,which is helpful to improve the ability and decision-making level of emergency disposal.
作者 刘海云 韩晓松 翟振岗 刘克会 LIU Haiyun;HAN Xiaosong;ZHAI Zhengang;LIU Kehui(Beijing Institute of New Technology Applications,Beijing 100094,China;Beijing Research Center of Underground Pipeline Comprehensive Management,Beijing 100094,China)
出处 《中国安全生产科学技术》 CAS CSCD 北大核心 2020年第9期37-42,共6页 Journal of Safety Science and Technology
基金 国家重点研发计划项目(2018YFC0704804) 北京市科学技术研究院海外人才计划项目(OTP-2018-005)。
关键词 燃气管线 灾害链 风险评估 复杂网络 gas pipeline disaster chain risk assessment complex network
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