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化工园区地震级联灾害情景构建与演化分析 被引量:2

Construction and evolution analysis of seismic cascading disaster scenario in chemical industry park
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摘要 地震灾害所引发的化工事故是一种低概率、高风险的非常规突发事件,一旦发生,所造成的事故后果严重程度远超过化工行业生产安全事故。本文为了厘清化工园区地震级联灾害的演化过程,通过对往年国内外相关灾害事件的统计分析,运用D-S证据理论进行专家评分值的数据融合,从而确定贝叶斯网络结构和各节点变量的条件概率;使用贝叶斯模型的推理功能,计算化工园区地震级联灾害情景演化网络中各节点的后验概率值。通过对比分析单一化工设备损坏以及各直接灾害事件耦合作用下造成不同事故的发生概率,确定了储罐破坏和火灾是级联灾害中导致灾害进一步扩大的主要影响因素,为化工园区提供震前预防、震时应急的侧重点。 The chemical accident caused by the earthquake disaster is a low-probability and high-risk unconventional emergency.Once it occurs,the severity of the consequence caused by accidents far exceeds that of the production safety accidents in the chemical industry.In order to clarify the evolution process of the seismic cascading disaster in the chemical industry park,through the statistical analysis of previous domestic and overseas relevant accidents,this paper uses D-S evidence theory to fuse the data of expert scores,and thus determine the Bayesian network structure and the conditional probability of each node variable.Using the reasoning function of the Bayesian model,the posterior probability of each node in the seismic cascading disaster scenario evolution network of chemical industry park is calculated.By comparing and analyzing the probability of different accidents caused by the damage of single chemical equipment and the coupling effect of each direct disaster event,it determines that the damage of storage tank and fire are the main factors that lead to the further expansion of the disaster in the cascading disaster,so as to provide the key points of pre-earthquake prevention and emergency response in the chemical industry park.
作者 赵江平 王敏 杨震 张翌曼 ZHAO Jiangping;WANG Min;YANG Zhen;ZHANG Yiman(College of Resources Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,China)
出处 《自然灾害学报》 CSCD 北大核心 2021年第2期102-110,共9页 Journal of Natural Disasters
基金 陕西省社会科学基金年度项目(2019S014) 陕西省软科学研究计划项目(2019KRM091)。
关键词 化工园区 地震 级联灾害 D-S证据理论 贝叶斯网络 chemical park seismic cascading disaster D-S evidence theory the Bayesian network
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