Identification of Key Risk Factors for Resident Evacuation Based on BC‐FRAM

Abstract The community evacuation process is a complex system, coupled and linked by multiple interactions, and traditional safety engineering methods are unable to explain how multiple causes can lead to an incident. By introducing the concept of mediated centrality and constructing the BC‐FRAM critical risk factor identification model for residential evacuation, the BC‐FRAM model solves the problem that traditional functional resonance methods can ignore risk modules with low performance fluctuations but high impact levels in complex networks. The ability of the BC‐FRAM model to identify key risk factors in community evacuation is improved by a factor of 1 and the ability to identify failed functional connections is improved by 44.4% over the traditional FRAM model.

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Publication Details

Journal
ce/papers
Published
2025-03-01
DOI
https://doi.org/10.1002/cepa.3285
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
0.00

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Identification of Key Risk Factors for Resident Evacuation Based on BC‐FRAM

Jiancheng Shi, Jinqiu Hu, Ziqi Lu, Shangrui Xiao et al.
ce/papers
Risk and Safety Analysis
article

Identification of Key Risk Factors for Resident Evacuation Based on BC‐FRAM

Jiancheng Shi, Jinqiu Hu, Ziqi Lu, Shangrui Xiao, Xingtao Li
article en

Abstract

Abstract The community evacuation process is a complex system, coupled and linked by multiple interactions, and traditional safety engineering methods are unable to explain how multiple causes can lead to an incident. By introducing the concept of mediated centrality and constructing the BC‐FRAM critical risk factor identification model for residential evacuation, the BC‐FRAM model solves the problem that traditional functional resonance methods can ignore risk modules with low performance fluctuations but high impact levels in complex networks. The ability of the BC‐FRAM model to identify key risk factors in community evacuation is improved by a factor of 1 and the ability to identify failed functional connections is improved by 44.4% over the traditional FRAM model.

ce/papersVol. 8(2)
China University of Petroleum, Beijing (CN), State Development & Investment Corporation (China) (CN)
National Natural Science Foundation of China, PetroChina Company Limited
Openalex Percentile: Top 95%
Risk and Safety Analysis
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