Construction risk assessment of metro engineering technical interface based on fault tree and fuzzy BN

doi:10.16265/j.cnki.issn1003-3033.2025.11.0964

Abstract

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

CLC Number:

X948

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.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Fig.4

Table 3

Table 4

Fuzzy probability of the root node R15

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)

Table 4

Table 5

Fig.5

Fig.6

Fig.7

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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