Risk assessment for physical hazard-bearing bodies based on consequences of hazardous chemical accidents and vulnerability

doi:10.16265/j.cnki.issn1003-3033.2026.05.1819

Abstract

Abstract:

To enhance the importance of buildings and bridges as physical hazard-bearing bodies for hazardous chemical accidents in regional risk assessment. In this paper, a physical vulnerability assessment model was established, and a risk assessment method was proposed, which considered the hazardous chemical accident consequences and the physical vulnerability of hazard-bearing bodies. Firstly, areal locations of hazardous atmospheres (ALOHA) was used to simulate the possible risk footprints of hazards. Secondly, a physical vulnerability assessment model including exposure, sensitivity and adaptability was established. Density of structures and distance from the accident center supply were selected as the exposure dimension layer. The age of the structures, building height, seismic grade of building and bridge length were selected as sensitivity dimension layer. Emergency shelter area, road area and infrastructure maintenance funds were selected as the adaptability index layer, and the driving force factors of physical hazard-bearing body vulnerability were analyzed through the geographical detector. Finally, arc geographic information system (ArcGIS) was used to superimpose the accident consequence map and the physical vulnerability map to generate a comprehensive risk map to realize the comprehensive risk visualization of hazard-bearing body. This method was applied to the risk assessment of physical hazard-bearing bodies in a town of Tianjin. The results show that the density of structures and the distance from the accident center have the strongest explanatory power for the vulnerability of physical hazard-bearing bodies. The explanatory power of these two factors is 0.515 and 0.464, respectively. High-risk areas result from the spatial overlap of high hazard and high vulnerability. The comprehensive regional risk resulting from the combination of accident consequences and vulnerability exhibits significant spatial variation. On the accident consequence map, the eastern part of the town near the hazard release point is the most dangerous area. However, owing to the low vulnerability of disaster-bearing bodies in the surrounding area of the release point, it is classified as a medium-risk zone on the comprehensive risk map.

Key words: consequences of hazardous chemical accidents, vulnerability, physical hazard-bearing body, risk assessment, hazard footprint

CLC Number:

X937

Guan Wenling, Wang Yutong, Wang Li, Ren Changxing, Dong Chengjie. Risk assessment for physical hazard-bearing bodies based on consequences of hazardous chemical accidents and vulnerability[J]. China Safety Science Journal, 2026, 36(5): 105-112.

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References 16

[1]	中国化学品安全协会. 天津港“8·12”瑞海公司危险品仓库火灾爆炸事故调查报告[EB/OL].(2016-02-08). https://www.chemicalsafety.org.cn/shiguxinxi/shiguanli/detail/wd70y2mqq7nv6qp1.
[2]	8·4黎巴嫩首都爆炸事故[EB/OL].[2025-11-28]. https://baike.baidu.com/item/8·4黎巴嫩首都爆炸事故/53121701.
[3]	Liu Wei, Yan Shuaixing, He Siming, et al. Landslide damage incurred to buildings: a case study of Shenzhen landslide[J]. Engineering Geology, 2018, 247: 69-83. doi: 10.1016/j.enggeo.2018.10.025
[4]	Luo Hongyu, Zhang Lulu, Zhang Limin. Progressive failure of buildings under landslide impact[J]. Landslides, 2019, 16(7): 1327-1340. doi: 10.1007/s10346-019-01164-0
[5]	Ahamed T, Duan J G, Jo H. Flood-fragility analysis of instream bridges-consideration of flow hydraulics, geotechnical uncertainties, and variable scour depth[J]. Structure and Infrastructure Engineering, 2021, 17(11): 1494-1507. doi: 10.1080/15732479.2020.1815226
[6]	刘曙光, 郑伟强, 钟桂辉, 等. 基于脆弱性曲线的寿溪河流域村镇建筑洪灾风险评估[J]. 同济大学学报(自然科学版), 2024, 52(1):68-76.
	Liu Shuguang, Zheng Weiqiang, Zhong Guihui, et al. Flood risk assessment of rural buildings in shouxihe basin based on vulnerability curve[J]. Journal of Tongji University (Natural Science), 2024, 52(1):68-76.
[7]	Ferreira T M, Maio R, Costa A A, et al. Seismic vulnerability assessment of stone masonry façade walls: calibration using fragility-based results and observed damage[J]. Soil Dynamics and Earthquake Engineering, 2017, 103: 21-37. doi: 10.1016/j.soildyn.2017.09.006
[8]	Meyers-Angulo J E, Martínez-Cuevas S, Gaspar-Escribano J M. Classifying buildings according to seismic vulnerability using Cluster-ANN techniques: application to the city of Murcia, Spain[J]. Bulletin of Earthquake Engineering, 2023, 21(7): 3581-3622. doi: 10.1007/s10518-023-01671-5
[9]	关文玲, 李彦霏, 董呈杰, 等. 城镇燃气抢修站选址优化模型[J]. 工业安全与环保, 2025, 51(3):70-75.
	Guan Wenling, Li Yanfei, Dong Chengjie, et al. Urban gas emergency repair station location optimization model[J]. Industrial Safety and Environmental Protection, 2025, 51(3):70-75.
[10]	王轶宏, 翟越, 李艳, 等. 城市燃气管网泄漏蒸气云爆炸事故风险评估[J]. 中国安全科学学报, 2023, 33(2):194-201. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0285
	Wang Yihong, Zhai Yue, Li Yan, et al. Risk assessment of vapor cloud explosion accident ofurban gas pipe network under leakage condition[J]. China Safety Science Journal, 2023, 33(2):194-201. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0285
[11]	Polsky C, Neff R, Yarnal B. Building comparable global change vulnerability assessments: the vulnerability scoping diagram[J]. Global Environmental Change, 2007, 17(3/4): 472-485. doi: 10.1016/j.gloenvcha.2007.01.005
[12]	Panahi M, Rezaie F, Meshkani S A. Seismic vulnerability assessment of school buildings in Tehran city based on AHP and GIS[J]. Natural Hazards and Earth System Sciences, 2014, 14(4): 969-979.
[13]	Rahman N, Ansary M A, Islam I. GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh[J]. International Journal of Disaster Risk Reduction, 2015, 13: 291-300. doi: 10.1016/j.ijdrr.2015.07.003
[14]	周海怡, 鲍全贵, 叶茂, 等. 基于组合赋权法的城市桥梁可靠性模糊综合评价[J]. 中国安全科学学报, 2023, 33(增刊1):156-161. doi: 10.16265/j.cnki.issn1003-3033.2023.S1.2494
	Zhou Haiyi, Bao Quangui, Ye Mao, et al. Fuzzy comprehensive evaluation of urban bridge reliability based on combination[J]. China Safety Science Journal, 2023, 33(S1):156-161. doi: 10.16265/j.cnki.issn1003-3033.2023.S1.2494
[15]	王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(1):116-134. doi: 10.11821/dlxb201701010
	Wang Jinfeng, Xu Chengdong. Geodetector: principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
[16]	Guan Wengling, Liu Qingwen, Dong Chengjie. Risk assessment method for industrial accident consequences and human vulnerability in urban areas[J]. Journal of Loss Prevention in the Process Industries, 2022, 76: DOI:10.1016/j.jlp.2022.104745.

目标层	准则层	指标层	指标性质	指标说明
物理承灾体	暴露性	建构筑物密度x₁/m²	正向	单元区域内的建构筑物的面积
	暴露性	距事故中心距离x₂/m	负向	建构筑物到事故中心的距离。事故影响范围内的建构筑物距离危险源越近,遭受的冲击波超压等级越高
	敏感性	建筑抗震等级x₃	负向	单元内钢筋混凝土结构的建筑所占面积比例。钢筋混凝土结构兼具刚度-柔性协调性与优异的抗冲击能力
		建构筑物年代x₄	正向	单元区域内建构筑物使用年限
		建筑物高度x₅/m	正向	单元区域内建筑物的高度值
		桥梁长度x₆/m	正向	单元区域内桥梁结构从起点到终点的水平距离
	适应性	应急避难面积x₇/m²	负向	单元区域内应急避难场所面积
		道路面积x₈/m²	负向	单元区域内道路面积。交通便利性提升消防救援效率,并加速重建物资运输与设备通行
		基础设施维护资金x₉/元	负向	用于保障基础设施正常运行和修复的资金投入

判断依据	交互作用类型
q(X₁∩X₂)<min[q(X₁),(X₂)]	非线性减弱
min[q(X₁), (X₂)]<q(X₁∩X₂)< max[q(X₁),(X₂)]	单因子线性减缩
q(X₁∩X₂)>max[q(X₁),(X₂)]	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

物理承灾体脆弱性等级	V值	颜色
非常高	0.75≤V(a,b)≤1	深红色
高	0.5≤V(a,b)<0.75	红色
中	0.25≤V(a,b)<0.5	橙色
低	0<V(a,b)<0.25	黄色
无脆弱性	V(a,b)=0	浅黄色

脆弱性等级	高C₃	中C₂	低C₁	无危害
非常高	重	重	高	无风险
高	重	高	中	无风险
中	高	高	中	无风险
低	中	中	低	无风险
无承灾体	无风险	无风险	无风险	无风险

准则层	指标层	层次分析法权重值	熵权法权重值	组合权重值
暴露性	建构筑物密度	0.101	0.149	0.125
暴露性	距事故中心距离	0.265	0.121	0.193
敏感性	桥梁长度	0.133	0.095	0.114
	建筑高度	0.098	0.174	0.136
	建筑抗震等级	0.171	0.253	0.212
	建构筑物年代	0.135	0.071	0.103
适应性	应急避难面积	0.021	0.057	0.039
	道路面积	0.035	0.055	0.045
	基础设施维护资金	0.041	0.025	0.033