Analysis of collapse risks under cut and cover method based on multi-state fuzzy Bayesian network

The collapse accidents under cut and cover method in metro station construction occurred frequently, leading to severe casualties and property damage. With increasing of metro station construction in China, more and more attention has been paid to collapse under cut and cover method. However, this s...

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Vydané v:PloS one Ročník 20; číslo 5; s. e0321382
Hlavní autori: Liu, Ping, Jin, Xueqiang, Shang, Yongtao, Zhu, Jiaolan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 07.05.2025
Predmet:
Accidents
Analysis
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
Biology and Life Sciences
Casualties
China
Collapse
Construction accidents & safety
Construction industry
Decision making
Design and construction
Earth Sciences
Engineering
Engineering and Technology
Failure
Fatalities
Forecasts and trends
Fuzzy Logic
Fuzzy sets
Geology
Health risks
Humans
Light rail transit
Medicine and Health Sciences
Methods
Nodes
Physical Sciences
Probability
Property damage
Railroads
Risk
Risk Assessment
Safety and security measures
Safety management
Sensitivity analysis
Social Sciences
Statistical analysis
Structural failures
Structure Collapse
Supervision
China
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:The collapse accidents under cut and cover method in metro station construction occurred frequently, leading to severe casualties and property damage. With increasing of metro station construction in China, more and more attention has been paid to collapse under cut and cover method. However, this subject is still not well studied and understood in China. To fill the research gap, this paper investigates collapse risk in the cut and cover method construction process using a multi-state fuzzy Bayesian network. Firstly, based on accident statistical analysis, 9 intermediate factors and 16 bottom factors of collapse were identified, and then a multi-state Fuzzy Bayesian network model was established based on these causative factors. Secondly, triangular fuzzy functions were utilized to fuzzily the data of nodes, and conditional probabilities were used to represent the uncertainty relationship between nodes. Additionally, an expert credibility-based survey method was employed to ensure the accuracy of node failure probability assessment. The method was applied to predict the risk of a case project using cut and cover method, and the results demonstrated that the probabilities of no-failure, moderate-failure, and severe-failure were 71%, 19%, and 10%, respectively. Sensitivity analyses of multi-states were performed to identify the key causal factors for moderate and severe collapse. The method can be used to predict the risk probability and key causal factors for collapse accidents. The result can provide decision support for cut and cover method construction, which could contribute to reducing the occurrence of collapse.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0321382