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大跨度钢结构施工安全风险评价IHFACS-BN模型及应用 被引量:22

IHFACS-BN safety assessment model for large-span steel structure construction and its application
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摘要 为降低大跨度钢结构施工安全事故发生率,提出基于改进人为因素分析及分类系统(IHFACS)与贝叶斯网络(BN)的大跨度钢结构施工安全风险评价模型。首先,引入HFACS方法,结合工程实际改进HFACS法;然后,基于IHFACS全面识别施工安全风险,采用粗糙集(RS)方法构建评价指标体系;其次,构建BN模型,并根据现场数据结合模糊集方法确定BN各根节点的先验概率及节点间的条件概率分布,通过GeNIe软件计算系统失效概率,分析关键风险因素;最后,以昆明机场扩建工程为例,验证该模型的有效性。结果表明:模型计算结果与实际情况基本符合,此工程总体安全风险概率为57.62%;焊缝错边、气孔、夹渣与支撑胎架拆除不当的敏感度均超过20%,是大跨度钢结构施工应重点管控的关键风险因素。 In order to reduce construction accidents of long-span steel structures,a risk evaluation model for them based on IHFACS and BN was proposed. Firstly,HFACS method was introduced and improved based on engineering practice. Secondly,safety risks were comprehensively identified by using IHFACS,and an evaluation index system was constructed by adopting RS method. Then,according to field data and fuzzy set method,prior probability of each BN root node and conditional probability distribution between nodes were determined,the system’s failure probability was calculated by GeNIe software,and key risk factors were analyzed. Finally,with Kunming airport expansion project as an example,the model ’ s effectiveness and advantages were verified. The results show that its calculated outcomes are basically consistent with reality,and the overall safety risk probability of the project is 57. 62%. It is also found that misalignment of welds,pores,slag inclusion and improper removal of supporting tire frames all feature a susceptibility above 20%,and they make the key risk factors in large-span steel structure construction.
作者 胡韫频 李超 李宗亮 杨道合 HU Yunpin;LI Chao;LI Zongliang;YANG Daohe(School of Civil Engineering and Architecture,Wuhan University of Technology,Wuhan Hubei 430070,China;Wuhan Construction Safety Management Office,Wuhan Hubei 430015,China)
出处 《中国安全科学学报》 CAS CSCD 北大核心 2021年第8期147-154,共8页 China Safety Science Journal
基金 武汉市城建局科技计划项目(201935)。
关键词 大跨度钢结构 施工安全风险 改进人为因素分析及分类系统(IHFACS) 贝叶斯网络(BN) 粗糙集(RS) 模糊集 Long-span steel structure construction safety risk improved human factor analysis and classification system(IHFACS) Bayesian network(BN) rough set(RS) fuzzy set
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