基于故障树和模糊BN的地铁工程技术接口施工风险评估

doi:10.16265/j.cnki.issn1003-3033.2025.11.0964

摘要/Abstract

摘要：

为量化城市地铁工程技术接口施工风险并甄别关键风险因素,提出一种基于故障树和模糊贝叶斯网络(BN)的地铁工程技术接口施工风险评估方法。首先,从人员、材料设备、技术、环境、管理5个方面识别出地铁工程技术接口施工风险因素;然后,运用故障树模型梳理施工风险因素之间的逻辑关系,构建地铁工程技术接口施工风险BN模型,并基于模糊集理论和专家经验评估各施工风险因素发生概率;最后,以北京地铁17号线北段工程为例,进行施工风险仿真评估,验证文中所提风险评估方法的科学性和有效性。研究结果表明:在地铁工程技术接口施工阶段,风险发生的概率为65%,处于中风险等级,且与工程技术接口实际施工的情况相匹配;通过逆向诊断推理,可快速识别出影响较大的风险因素组合,提高技术接口施工风险事件的诊断效率;通过敏感性分析得出,技术接口施工人员安全意识薄弱、接口施工管理制度的落实情况差是导致工程技术接口施工风险发生的关键致险因素。通过构建故障树模型,清晰梳理了风险因素间的逻辑关系,再转化为模糊BN模型,实现了对风险概率的计算与评估。

关键词: 故障树, 模糊贝叶斯网络(BN), 地铁工程技术接口, 施工风险, 风险因素

Abstract:

In order to quantify the construction risk of urban subway engineering technology interface and screen the key risk factors, a construction risk assessment method of subway engineering technology interface based on fault tree and fuzzy BN was proposed. Firstly, the construction risk factors of metro engineering technology interface were identified from five aspects: personnel, materials and equipment, technology, environment, and management. Secondly, the fault tree model was used to sort out the logical relationship between the construction risk factors, construct a Bayesian network model of the construction risk of metro engineering technology interface, and evaluate the probability of the occurrence of each construction risk factor based on the theory of fuzzy sets and the experience of the experts. Lastly, BN was used to assess the construction risk of Beijing Metro Line 17, which was the largest and most important construction risk factor in China. Finally, BN was used to simulate and evaluate the construction risk of the northern section of Beijing Metro Line 17 as an example. The results show that the probability of risk occurrence in the construction phase of subway engineering technology interface is 65%, which is in the medium risk status level and matches the actual construction of the engineering technology interface. Through reverse diagnostic reasoning, the combination of risk factors with greater impact can be quickly identified, and the efficiency of the diagnosis of the construction risk events of the technical interface can be improved. Meanwhile, through the sensitivity analysis, it can be seen that the weak safety awareness of the technical interface construction personnel and the implementation of the interface construction management system are the key risk factors leading to the occurrence of engineering and technical interface construction risks. The assessment method can provide theoretical support for the prevention and management of metro engineering technology interface construction risk. By constructing a fault tree model to clearly sort out the logical relationships between risk factors, and then converting it into a fuzzy BN model, the accurate calculation and evaluation of risk probabilities are realized. This method has been verified in practical engineering cases, enabling the precise identification of key risk factors and providing a clear direction for risk management and control.

Key words: fault tree, fuzzy Bayesian network (BN), metro engineering technology interface, construction risk assessment, risk factor

中图分类号:

X948

闫林君, 刘晶晶, 王亚妮, 陈慧鑫. 基于故障树和模糊BN的地铁工程技术接口施工风险评估[J]. 中国安全科学学报, 2025, 35(11): 24-31.

YAN Linjun, LIU Jingjing, WANG Yani, CHEN Huixin. Construction risk assessment of metro engineering technical interface based on fault tree and fuzzy BN[J]. China Safety Science Journal, 2025, 35(11): 24-31.

图/表 12

图1

图2

图3

表1

表2

图4

表3

表4

根节点R15的模糊概率

风险等级	高风险,不可接受	中风险, 可接受	低风险, 忽略
专家1	(0.05,0.15, 0.25)	(0.75,0.80, 0.85)	(0.25,0.35, 0.45)
专家2	(0.05,0.15, 0.25)	(0.75,0.80, 0.85)	(0.25,0.35, 0.45)
专家3	(0.25,0.35, 0.45)	(0.55,0.65, 0.75)	(0.25,0.35, 0.45)
专家4	(0.05,0.15, 0.25)	(0.75,0.80, 0.85)	(0.25,0.35, 0.45)
$P ˜ i j$	(0.094,0.194, 0.294)	(0.706,0.767, 0.828)	(0.250,0.350, 0.450)

表4

表5

图5

图6

图7

参考文献 21

[1]	郭杰. 城市轨道交通工程接口管理研究[D]. 北京: 中国铁道科学研究院, 2012.
	GUO Jie. Research on interface management in urban rail transit project[D]. Beijing: China Academy of Railway Science, 2012.
[2]	AL-HAMMAD A. Interface problems between owners and maintenance contractors in Saudi Arabia[J]. Journal of Performance of Constructed Facilities, 1995, 9(3): 194-205. doi: 10.1061/(ASCE)0887-3828(1995)9:3(194)
[3]	AL-HAMMAD A, AL-HAMMAD I. Interface problems between building owners and designers[J]. Journal of Performance of Constructed Facilities, 1996, 10(3): 123-126. doi: 10.1061/(ASCE)0887-3828(1996)10:3(123)
[4]	AL-HAMMAD A. Common interface problems among various construction parties[J]. Journal of Performance of Constructed Facilities, 2000, 14(2): 71-74. doi: 10.1061/(ASCE)0887-3828(2000)14:2(71)
[5]	SAMIN S, MAHDI S, CARL T, et al. Interface management model for megacapital projects[C]. Construction Research Congress 2012:Construction Challenges in a FlatWorld. West Lafayette: ASCE, 2012: 447-456.
[6]	NILSEN M A, FALK K, HAUGEN T A. Reducing project cost growth through early implementation of interface management[J]. INCOSE International Symposium, 2018, 28(1): 96-114. doi: 10.1002/iis2.2018.28.issue-1
[7]	琚倩茜, 姜红丙. 城市轨道交通工程关键接口识别方法研究[J]. 铁道科学与工程学报, 2020, 17(10): 2672-2679.
	JU Qianxi, JIANG Hongbing. Research on key interface points identification for metro projects[J]. Journal of Railway Science and Engineering, 2020, 17(10): 2672-2679.
[8]	任银龙, 胡所亭, 班新林, 等. 艰险山区铁路桥梁与四电工程技术接口管理协同度评价研究[J]. 铁道建筑, 2021, 61(7): 46-53.
	REN Yinlong, HU Suoting, BAN Xinlin, et al. Evaluation of synergistic degree of interface management between railroad bridge and four electric engineering technology in difficult mountainous area[J]. Railway Construction, 2021, 61(7): 46-53.
[9]	霍雨雨, 鲍学英, 李爱春, 等. 艰险山区铁路桥隧技术接口工序资源冲突研究[J]. 铁道科学与工程学报, 2022, 19(7): 2107-2116.
	HUO Yuyu, BAO Xueying, LI Aichun, et al. On resource conflict of technical interface process of railway bridge and tunnel in hard and dangerous mountainous area[J]. Journal of Railway Science and Engineering, 2022, 19(7): 2107-2116.
[10]	SCHRODER R J. Fault trees for reliability analysis[J]. Microelectronics Reliability, 1970, 9(6): 449-450.
[11]	ZHOU Qingji, WONG Y D, LOH H S, et al. A fuzzy and Bayesian network CREAM model for human reliability analysis:the case of tanker shipping[J]. Safety Science, 2018, 105: 149-157. doi: 10.1016/j.ssci.2018.02.011
[12]	陈雍君, 李晓健, 张丽, 等. 故障树和模糊贝叶斯网络在管廊运维风险评估中的应用研究[J]. 安全与环境学报, 2024, 24(3):857-866.
	CHEN Yongjun, LI Xiaojian, ZHANG Li, et al. Application of fault tree and fuzzy Bayesian network in risk assessment of pipeline corridor operation and maintenance[J]. Journal of Safety and Environment, 2024, 24(3): 857-866.
[13]	陈雍君, 李晓健, 吴光晔, 等. 基于故障树和贝叶斯网络的管廊运维风险评估[J]. 地下空间与工程学报, 2024, 20(3):1016-1025,1050. doi: 10.20174/j.JUSE.2024.03.31
	CHEN Yongjun, LI Xiaojian, WU Guangye, et al. Operation and maintenance risk assessment of underground utility tunnels based on FTA and fuzzy BN[J]. Journal of Underground Space and Engineering, 2024, 20(3):1016-1025,1050.
[14]	王峰. 高速铁路工程系统接口技术研究[D]. 北京: 中国铁道科学研究院, 2013.
	WANG Feng. Research of system interface technology in high-speed railway engineering[D]. Beijing: China Academy of Railway Science, 2013.
[15]	ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3):338-353. doi: 10.1016/S0019-9958(65)90241-X
[16]	吴贤国, 吴克宝, 沈梅芳, 等. 基于N-K模型的地铁施工安全风险耦合研究[J]. 中国安全科学学报, 2016, 26(4): 96-101.
	WU Xianguo, WU Kebao, SHEN Meifang, et al. Research on coupling of safety risks in metro constructuion based on N-K model[J]. Journal of Safety and Environment, 2016, 26(4): 96-101.
[17]	LI Xiao, ZHU Jianming. Research on graded management of construction risk factors of a subway station[M]. Amsterdam: IOS Press, 2021: 33-37.
[18]	TANG Yongjun. Civil engineering and surrounding environment risk analysis based on ALARP principle[J]. IOP Conference Series: Earth and Environmental Science, 2018, 170(3): DOI:10.1088/1755-1315/170/3/032143.
[19]	JENSEN F V. An Introduction to Bayesian networks[M]. London: UCL Press, 1996: 77-94.
[20]	秦荣水, 石晨晨, 陈超, 等. 基于模糊贝叶斯网络的城市商业综合体火灾风险分析[J]. 中国安全科学学报, 2023, 33(12): 176-182. doi: 10.16265/j.cnki.issn1003-3033.2023.12.0879
	QIN Rongshui, SHI Chenchen, CHEN Chao, et al. Risk analysis on fire accident of urban commercial complex based on fuzzy Bayesian network[J]. Journal of Safety and Environment, 2023, 33(12): 176-182.
[21]	李敏, 林志军, 鲁义, 等. 基于模糊贝叶斯网络的煤矿瓦斯爆炸风险评估[J]. 煤炭学报, 2023, 48(增2): 626-637.
	LI Min, LIN Zhijun, LU Yi, et al. Risk assessment of gas explosion in coal mines based on fuzzy Bayesian network[J]. Coal Journal, 2023, 48(S2): 626-637.

语言变量	模糊集
非常高(VH)	(0.85,0.925,1.0)
高(H)	(0.75,0.8,0.85)
偏高(FH)	(0.55,0.65,0.75)
中等(M)	(0.45,0.5,0.55)
偏低(FL)	(0.25,0.35,0.45)
低(L)	(0.05,0.15,0.25)
非常低(VL)	(0,0.025,0.05)

项目	类别	权重	项目	类别	权重
教育背景	博士	1	从业年龄	>15a	1
	硕士	0.9		10~15a	0.9
	本科	0.8		5~10a	0.8
	大专	0.7		<5a	0.7

专家编号	教育背景	权重a	从业年龄/a	权重β
1	博士	1	>15	1
2	硕士	0.9	8	0.8
3	本科	0.8	11	0.9
4	硕士	0.9	12	0.9

风险节点	高风险	中风险	低风险	风险节点	高风险	中风险	低风险
R₁₁	0.07	0.66	0.27	R₄₁	0.15	0.59	0.26
R₁₂	0.08	0.62	0.30	R₄₂	0.14	0.60	0.26
R₁₃	0.25	0.61	0.14	R₄₃	0.15	0.70	0.15
R₁₄	0.08	0.63	0.28	R₄₄	0.17	0.58	0.25
R₁₅	0.15	0.58	0.27	R₄₅	0.11	0.62	0.26
R₂₁	0.15	0.61	.24	R₅₁	0.12	0.62	0.26
R₂₂	0.07	0.68	0.25	R₅₂	0.10	0.70	0.20
R₂₃	0.08	0.73	0.19	R₅₃	0.13	0.69	0.18
R₂₄	0.09	0.63	0.28	R₅₄	0.14	0.63	0.23
R₃₁	0.19	0.64	0.17	R₅₅	0.21	0.67	0.12
R₃₂	0.18	0.68	0.14	R₅₆	0.23	0.65	0.12
R₃₃	0.23	0.62	0.15	R₅₇	0.18	0.62	0.20
R₃₄	0.15	0.60	0.25