强降雨下地铁车站施工安全风险演化推理研究

doi:10.16265/j.cnki.issn1003-3033.2023.06.0383

摘要/Abstract

摘要：

强降雨易引发地铁车站施工安全事故,为揭示此类工程受强降雨的致灾机制,评价施工安全风险,结合蝴蝶结(BT)分析法进行演化,得到包含2个顶事件、27个中间事件、47个基本事件的安全风险事故树;基于贝叶斯网络(BN)理论,改进节点模糊多态化与直觉模糊化2个方面,得到优化后的直觉模糊多态贝叶斯网络(IFPBN)安全风险演化推理模型;以广州21号线地铁工程为例,进行演绎推理应用,并验证模型有效性。结果表明:优化后的模型推理结果与实际情况基本相符,相较于常规推理模型更为精准、高效;所构建的风险演化结构中,强降雨等级、人的不安全行为和主体工程环境不安全状态是影响强降雨下地铁车站施工安全风险的重要宏观因素,应急组织混乱、安全意识缺乏与支护不稳定是重要微观因素。

关键词: 强降雨, 地铁车站, 施工安全风险, 演化推理, 直觉模糊多态贝叶斯网络(IFPBN)

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)

陈伟, 田仪帅, 赵卓雅, 王艳华, 郭道远. 强降雨下地铁车站施工安全风险演化推理研究[J]. 中国安全科学学报, 2023, 33(6): 135-143.

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.

图/表 16

图1

图2

图3

图4

表1

续表1

图5

表2

图6

表3

节点X1直觉模糊意见整合

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

表3

表4

表5

表6

图7

图8

图9

参考文献 20

[1]	闫绪娴, 王俊丽, 范玲, 等. 韧性城市视角下地铁洪涝灾害风险分析:基于Bow-Tie-贝叶斯网络模型[J]. 灾害学, 2022, 37(2):36-43.
	YAN Xuxian, WANG Junli, FAN Ling, et al. Research on subway flood disaster from the perspective of resilient city:based on Bow-Tie-Bayesian network model[J]. Journal of Catastrophology, 2022, 37 (2): 36-43.
[2]	赵露薇, 王青娥. FCM下暴雨干扰地铁系统脆弱性演化分析[J]. 中国安全科学学报, 2022, 32(10):186-192. doi: 10.16265/j.cnki.issn1003-3033.2022.10.2221
	ZHAO Luwei, WANG Qing'e. FCE-based vulnerability evolution analysis of metro systems under storm disturbances[J]. China Safety Science Journal, 2022, 32 (10): 186-192. doi: 10.16265/j.cnki.issn1003-3033.2022.10.2221
[3]	LIU Hong, XIN Zhong, MIN Ouyang, et al. Vulnerability analysis of public transit systems from the perspective of urban residential communities[J]. Reliability Engineering and System Safety, 2019, 189 :143-156. doi: 10.1016/j.ress.2019.04.018
[4]	王军武, 吴寒, 杨庭友. 基于投影寻踪的地铁车站工程暴雨内涝脆弱性评价[J]. 中国安全科学学报, 2019, 29(9):1-7. doi: 10.16265/j.cnki.issn1003-3033.2019.09.001
	WANG Junwu, WU Han, YANG Tingyou. Vulnerability assessment of rainstorm waterlogging in subway station engineering based on projection pursuit[J]. China Safety Science Journal, 2019, 29 (9):1-7. doi: 10.16265/j.cnki.issn1003-3033.2019.09.001
[5]	JIAO Liudan, ZHU Yinghan, HUO Xiaosen, et al. Resilience assessment of metro stations against rainstorm disaster based on cloud model: a case study in Chongqing, China[J]. Natural Hazards, 2022, 116(2):2311-2337. doi: 10.1007/s11069-022-05765-2
[6]	LYU Haimin, SHEN Shuilong, ZHOU Annan, et al. Flood risk assessment of metro systems in a subsiding environment using the interval fahp-fca approach[J]. Sustainable Cities and Society, 2019, 50:DOI:10.1016/j.scs.2019.101682. doi: 10.1016/j.scs.2019.101682
[7]	陈伟, 乔治, 王伟震, 等. 暴雨灾害引发施工安全事故应急处置SD模型[J]. 中国安全科学学报, 2017, 27(6):169-174. doi: 10.16265/j.cnki.issn1003-3033.2017.06.029
	CHEN Wei, QIAO Zhi, WANG Weizhen, et al. SD model for emergency disposal of construction safety accidents caused by rainstorm disaster[J]. China Safety Science Journal, 2017, 27(6):169-174. doi: 10.16265/j.cnki.issn1003-3033.2017.06.029
[8]	SUN Lishan, HUANG Yuchen, CHEN Yanyan, et al. Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China[J]. Transportation Research Part A-Policy and Practice, 2018, 108(2):12-24. doi: 10.1016/j.tra.2017.12.008
[9]	权瑞松. 多情景视角的上海中心城区地铁暴雨内涝暴露性分析[J]. 地理科学, 2015, 35(4):471-475. doi: 10.13249/j.cnki.sgs.2015.04.471
	QUAN Ruisong. Exposure analysis of rainstorm waterlogging on subway in central urban area of Shanghai based on multiple scenario perspective[J]. Scientia Geographica Sinica, 2015, 35 (4): 471-475. doi: 10.13249/j.cnki.sgs.2015.04.471
[10]	马晴晴, 吴珊, 王昊, 等. 基于暴露性和敏感性的地表降雨积水影响下的地铁站脆弱性研究[J]. 水利水电技术(中英文), 2022, 53(10):12.
	MA Qingqing, WU Shan, WANG Hao, et al. Exposed property and sensitivity-based study on vulnerability of subway station under impact of surface rainfall ponding[J]. Water Conservancy and Hydropower Technology, 2022, 53(10):74-85.
[11]	HOU Jingwei, DU Yixian. Spatial simulation of rainstorm waterlogging based on a water accumulation diffusion algorithm[J]. Geomatics Natural Hazards & Risk, 2020, 11(1):71-87.
[12]	ZHOU Zhipeng, LIU Song, QI Haonan. Mitigating subway construction collapse risk using Bayesian network modeling[J]. Automation In Construction, 2022, 143:DOI:10.1016/j.autcon.2022.104541. doi: 10.1016/j.autcon.2022.104541
[13]	ZHANG Kaihua, MATTHEW C. Predicting growth and interaction of multiple cracks in structural systems using dynamic bayesian networks[J]. Marine Structures, 2022, 86:DOI:10.1016/j.marstruc.2022.103271. doi: 10.1016/j.marstruc.2022.103271
[14]	SHARAFA A, LATIF K, SEO J. Risk analysis of TBM tunneling projects based on generic bow-tie risk analysis approach in difficult ground conditions[J]. Tunnelling and Underground Space Technology, 2021, 111:DOI:10.1016/j.tust.2021.103860. doi: 10.1016/j.tust.2021.103860
[15]	MOHAMED M, TRAN D Q. Risk-based inspection for concrete pavement construction using fuzzy sets and Bayesian networks[J]. Automation In Construction, 2021, 128:DOI:10.1016/j.autcon.2021.103761. doi: 10.1016/j.autcon.2021.103761
[16]	毕傲睿, 沈超, 朱洪云, 等. 基于直觉模糊信息的地下储气库安全性可拓评价模型研究[J]. 安全与环境工程, 2022, 29(4):172-180.
	BI Aorui, SHEN Chao, ZHU Hongyun, et al. Security extension evaluation of underground gas storage based on intuitionistic fuzzy information[J]. Safety and Environmental Engineering, 2022, 29 (4):172-180.
[17]	顾潮琪, 张才坤, 周德云, 等. 基于直觉模糊贝叶斯网络多态系统可靠性分析[J]. 西北工业大学学报, 2014, 32(5):744-748.
	GU Chaoqi, ZHANG Caikun, ZHOU Deyun, et al. Reliability analysis of multi-state systems based on intuitionistic fuzzy Bayesian networks[J]. Journal of Northwestern Polytechnical University, 2014, 32 (5): 744-748.
[18]	QIN Yong, ZHANG Zhenyu, LIU Xinwang, et al. Dynamic risk assessment of metro station with interval type-2 fuzzy set and TOPSIS method[J]. Journal of Intelligent and Fuzzy Systems, 2015, 29(1):93-106. doi: 10.3233/IFS-151573
[19]	CHEN Zhihe, YIN Lei, CHEN Xiaohong, et al. Research on the characteristics of urban rainstorm pattern in the humid area of southern China: a case study of Guangzhou city[J]. International Journal of Climatology, 2015, 35(14):4370-4386. doi: 10.1002/joc.4294
[20]	广州市人民政府. 生产安全事故应对处理信息[DB/OL].(2022-02-25). https://www.gz.gov.cn/zwgk/zdly/aqsc/scaqsgydclxx/content/mpost_8100118.html.

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