基于HFACS-BN的地铁车站施工高处坠落可能性评价

doi:10.16265/j.cnki.issn 1003-3033.2021.06.012

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (6): 90-98.doi: 10.16265/j.cnki.issn 1003-3033.2021.06.012

基于HFACS-BN的地铁车站施工高处坠落可能性评价

王军武¹ 教授, 王梦雨¹, 刘登辉², 吴寒¹, 胡蝶¹

1 武汉理工大学土木工程与建筑学院,湖北武汉 430070;
2 中国建筑一局(集团)有限公司,北京 100161

收稿日期:2021-03-16 修回日期:2021-05-02 出版日期:2021-06-28
作者简介:王军武 (1965—),男,江西德安人,博士,教授,博士生导师,主要从事土木工程建造与管理等方面的研究。E-mail:junwuwang@163.com。
基金资助:
国家重点研发计划项目(2018YFC0704301);武汉市城乡建设局科技计划项目(201943)。

Evaluation of falling possibility at height in subway station construction based on HFACS-BN

WANG Junwu¹, WANG Mengyu¹, LIU Denghui², WU Han¹, HU Die¹

1 School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan Hubei 430070, China;
2 China Construction First Group Corporation Limited, Beijing 100161, China

Received:2021-03-16 Revised:2021-05-02 Published:2021-06-28

摘要/Abstract

摘要： 为准确预测地铁车站施工高处坠落可能性,构建基于HFACS-BN的地铁车站施工高处坠落可能性评价模型。首先,基于人为因素分析和分类系统(HFACS)框架,识别地铁车站施工高处坠落致因因素,并将其映射为贝叶斯网络(BN);然后,将基于正态分布权重的群决策法、模糊最佳最差法(fuzzy-BWM)、全局相对值等方法相结合,并应用到BN中获取根节点的先验概率和中间节点的条件概率,依托BN的正向推理计算高处坠落节点处于各状态下的概率,敏感性分析辨识关键致因因素;最后,选取成都地铁11号线3个车站进行实例分析。研究结果表明:回龙路站、钓鱼嘴站和芦角村站的高处坠落最可能状态分别为严重故障、无故障、无故障状态,均与实际施工情况基本一致;安全教育培训不到位、移除防坠落x措施、踩空踩滑、安全意识薄弱、站在不安全位置、照明不足为高处坠落事故的关键致因因素,应重点管控。

关键词: 人为因素分析和分类系统(HFACS), 贝叶斯网络(BN), 地铁车站施工, 高处坠落, 可能性评价

Abstract: In order to accurately predict falling possibility at height in subway station construction, an evaluation model of it based on HFCAS-BN was constructed. Firstly, based on HFACS framework, causes of falling at height were identified, and the framework was mapped into BN. Secondly, group decision-making method based on normal distribution weight, fuzzy-BWM and global relative value were incorporated into BN to obtain prior probability of root nodes and conditional probability of intermediate nodes. Then, probability of falling nodes in various states was predicted and key causes were identified by using BN forward reasoning and sensitivity analysis. Finally, three stations of Chengdu Subway Line 11 were selected for case analysis. The results show that evaluations of most possible state of Huilonglu Stop, Diaoyuzui Stop and Lujiaocun Stop, respectively severe fault, fault free and fault free, are basically consistent with actual construction situation. Key causes of falling accidents include inadequate safety education and training, removal of falling prevention measures, losing one's footing, poor safety awareness, standing on unsafe places and inadequate lighting, so major efforts should be made on them.

Key words: human factors analysis and classification system (HFACS), Bayesian network (BN), subway station construction, falling at height, evaluation of possibility

中图分类号:

X948

王军武, 王梦雨, 刘登辉, 吴寒, 胡蝶. 基于HFACS-BN的地铁车站施工高处坠落可能性评价[J]. 中国安全科学学报, 2021, 31(6): 90-98.

WANG Junwu, WANG Mengyu, LIU Denghui, WU Han, HU Die. Evaluation of falling possibility at height in subway station construction based on HFACS-BN[J]. China Safety Science Journal, 2021, 31(6): 90-98.

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基于HFACS-BN的地铁车站施工高处坠落可能性评价

Evaluation of falling possibility at height in subway station construction based on HFACS-BN

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