Safety risk evolution reasoning research of subway station construction under heavy rainfall

doi:10.16265/j.cnki.issn1003-3033.2023.06.0383

Abstract

Abstract:

Heavy rainfall is prone to cause safety accidents in subway station construction, so it is necessary to uncover the disaster-causing mechanism of such projects under heavy rainfall and evaluate the construction safety risks. A fault tree with 2 top events, 27 intermediate events and 47 fundamental events was obtained through BT analysis. The improved IFPBN safety risk evaluation reasoning model was obtained from two aspects of node fuzzy polymorphism and intuitionistic fuzzy optimization based on Bayesian Network (BN) theory. Model validation was conducted for Guangzhou Metro Line 21 using deductive reasoning. The results show that the calculation results of the optimized model are consistent with the actual situation, and are more accurate and efficient. The constructed risk evolution structure, heavy rainfall level, people's unsafe behavior and unsafe state of project environment are important macro factors affecting the construction safety risk of subway station under heavy rainfall. The chaos of emergency organization, lack of safety awareness and the support instability are important micro factors.

Key words: heavy rainfall, subway stations, construction safety risk, evolution reasoning, intuitionistic fuzzy polymorphic Bayesian network (IFPBN)

CHEN Wei, TIAN Yishuai, ZHAO Zhuoya, WANG Yanhua, GUO Daoyuan. Safety risk evolution reasoning research of subway station construction under heavy rainfall[J]. China Safety Science Journal, 2023, 33(6): 135-143.

Figures/Tables 16

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Fig.5

Tab.2

Fig.6

Tab.3

Integration of intuitive fuzzy opinions for node X1

参数符号	数值	参数符号	数值
$< μ 111, γ 111 >$	<0.7,0.2>	P₁₁	<0.733 5,0.156 9>
$< μ 211, γ 211 >$	<0.8,0.1>	P₁₂	<0.528 7,0.303 0>
$< μ 311, γ 311 >$	<0.7,0.2>	P₁₃	<0.218 7,0.680 5>
$< μ 411, γ 411 >$	<0.8,0.1>	F₁₁	0.109 6
$< μ 511, γ 511 >$	<0.7,0.2>	F₁₂	0.168 5
$< μ 611, γ 611 >$	<0.7,0.2>	F₁₃	0.119 6
︙	︙	$f (T 1 S 0)$	0.326 6
$< μ 513, γ 513 >$	<0.2,0.7>	$f (T 1 S 1)$	0.343 9
$< μ 613, γ 613 >$	<0.1,0.8>	$f (T 1 S 2)$	0.329 5

Tab.3

Tab.4

Tab.5

Tab.6

Fig.7

Fig.8

Fig.9

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代号	事件	代号	事件	代号	事件	代号	事件
T₂	强降雨后承灾风险	C₁	地质条件	X₉	设备安装不符合规定	X₂₈	车站及隧道侧墙防渗较差
T₁	强降雨前御灾风险	C₂	设计施工	X₁₀	未按规定匹配必需设备	X₂₉	可燃气体泄漏
F	强降雨等级	C₃	挡水措施	X₁₁	设备选型不符合要求	X₃₀	氧含量减少
A₁	人的不安全行为	C₄	排水措施	X₁₂	保养维修不到位	X₃₁	交通拥堵
A₂	机械设备的不安全状态	C₅	电力设备	X₁₃	设备保护措施不足	X₃₂	城市排水系统瘫痪
A₃	主体工程环境不安全状态	C₆	渗涌措施	X₁₄	不利地质条件	X₃₃	政府安全主管部门监督不足
A₄	管理缺陷	C₇	地下气体	X₁₅	地质富水性	X₃₄	应急响应强制性较差
B₁	站内作业人员	C₈	应急响应行为	X₁₆	围岩等级较低	X₃₅	企业安全文化不足

代号	事件	代号	事件	代号	事件	代号	事件
B₂	站外作业人员	C₉	现场管理行为	X₁₇	支护不稳定	X₃₆	应急预案编制不合理
B₃	设备状态	C₁₀	现场监管行为	X₁₈	开挖深度过深	X₃₇	救援物资不充分
B₄	设备型号	C₁₁	施工组织设计	X₁₉	开挖方法选择不当	X₃₈	事故监测不到位
B₅	设备保护	X₁	应急组织混乱	X₂₀	挡水墙未起作用	X₃₉	设计勘察不到位
B₆	车站主体工程	X₂	安全意识缺乏	X₂₁	挡水墙被冲破	X₄₀	设计方案不合理
B₇	地下积水与地下环境	X₃	应急能力不足	X₂₂	缺少排水泵	X₄₁	安全技术交底不彻底
B₈	地上积水与地上环境	X₄	恐慌心理	X₂₃	排水系统瘫痪	X₄₂	未及时处理施工安全隐患
B₉	政府安全主管部门行为	X₅	站外救援不及时	X₂₄	照明设备系统瘫痪、停电	X₄₃	安全管理不到位
B₁₀	地铁工程建设部门行为	X₆	站外监测不准确	X₂₅	通风系统瘫痪、环境缺氧	X₄₄	施工材料、设备质量不合格
B₁₁	设计部门行为	X₇	信息传递不及时	X₂₆	通讯系统瘫痪、信号失联	X₄₅	赶工期而忽视安全
B₁₂	监管部门行为	X₈	设备设施存在缺陷问题	X₂₇	车站及隧道顶板防渗较差	X₄₆	施工未设置相关安全设施

多态级别	划分标准
多态级别	文本预报方式	12 h降雨量/mm	24 h降雨量/mm
S₀	小雨	[0,15)	[0,25)
S₁	中雨、大雨、暴雨	[15,70)	[25,100)
S₂	大暴雨、特大暴雨	[70,+∞)	[100,+∞)

根节点X_i故障状态	隶属度
根节点X_i故障状态	0	0.5	1
X₁=0.6	0	1	0
X₉=0.8	0	1/3	2/3
X₁₄=0.7	0	2/3	1/3
X₂₀=0.8	0	1/3	2/3
X₂₁=0.9	0	0	1
X₂₃=0.8	0	1/3	2/3
X₃₂=0.6	0	1	0
X₃₅=0.7	0	2/3	1/3
X₄₀=0.8	0	1/3	2/3
X₄₂=0.7	0	2/3	1/3
其余X_i=0	1	0	0

节点事件	P(B₇=S₁)=1			P(B₇=S₂)=1
节点事件	0.5	1	故障状态	0.5	1	故障状态
C₃	0.384	0.213	中等	0.034	0.772	严重
C₄	0.642	0.156	中等	0.430	0.224	中等
C₅	0.432	0.112	中等	0.123	0.648	严重
C₆	0.136	0.072	无故障	0.408	0.355	中等
C₇	0.086	0.012	无故障	0.231	0.098	无故障