An integrated framework for managing fire resilience of metro station system: Identification, assessment, and optimization

Tang, Yuchun; Bi, Wei; Varga, Liz; Dolan, Tom

doi:10.1016/j.ijdrr.2022.103037

Cited by 28 publications

(17 citation statements)

References 79 publications

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“…After a safety accident occurs, prompt emergency management should be carried out to minimize casualties and accident losses. It is essential to improve the emergency management mechanisms and emergency plans, ensuring the adequacy of material reserves and other resources [64]. Building a risk management information system enables dynamic risk assessment of the information monitored in the construction of prefabricated buildings and the release of construction safety warning information, as well as monitoring construction quality to ensure project quality [65].…”

Section: Resultsmentioning

confidence: 99%

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Wang,

Chen,

Song

et al. 2024

Buildings

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Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

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Section: Resultsmentioning

confidence: 99%

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Wang,

Chen,

Song

et al. 2024

Buildings

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show abstract

“…During the full development stage, a subway station should activate active emergency response equipment, including fire shutters, fire doors and fans, to control the spread of fire and reduce property damage (Jeon & Hong, 2009). Moreover, firefighting decision-making capability, such as resource allocation and strategies, is significant for controlling fire spread (Tang et al, 2022). Firefighters with high emergency decision-making capability can reduce the combustion time of fire, and consequently, the damages caused by fire.…”

Section: Scenario Elements In the Full Development To Extinguishment ...mentioning

confidence: 99%

“…9 Reliability of active emergency response equipmentJeon and Hong (2009) S 4 E 10 Good firefighting decision-making capabilityTang et al (2022) …”

mentioning

confidence: 99%

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

Li,

Yuan,

Zhang

et al. 2024

Journal of Civil Engineering and Management

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Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.

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“…In terms of resilience evaluation methods, scholars generally adopt the network analytic hierarchy process (ANP) [22][23][24][25], TOPSIS method [26][27][28][29][30][31], entropy weight method [9,[32][33][34][35], cloud model [36][37][38][39][40] and Bayesian network [41][42][43][44] to construct a resilience evaluation model.…”

Section: Highmentioning

confidence: 99%

Resilience Evaluation of High-Speed Railway Subgrade Construction Systems in Goaf Sites

et al. 2022

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When the high-speed railway construction project passes through goaf sites, the uncertain impacts from internal and external environments faced by the system are gradually characterized by complexity and variability, and the disastrous consequences are becoming increasingly prominent. The risk resistance ability and accident recovery ability of the high-speed railway subgrade construction system are crucial for improving the safety management level of the construction site. Based on the concept of resilience, this paper discusses the connotation of resilience applicable to the construction system of high-speed railway foundations in goaf sites, and an evaluation index system including 25 indicators is constructed. Then, the resilience evaluation model is constructed by ANP, entropy weight method and fuzzy comprehensive evaluation method. Taking the construction system of Taijiao high-speed railway subgrade in the underlying goaf as an example, this model is verified. The verification results show that the grade of construction system is II (high resilience). The evaluation result is consistent with the actual engineering situation, and the evaluation model is effective. It can be used as a theoretical basis for safety management of high-speed railway construction projects, and a full process analysis method based on resilience theory is established.

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An integrated framework for managing fire resilience of metro station system: Identification, assessment, and optimization

Cited by 28 publications

References 79 publications

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

Resilience Evaluation of High-Speed Railway Subgrade Construction Systems in Goaf Sites

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