Integrating FRAM and BN for enhanced resilience evaluation in construction emergency response : A scaffold collapse case study
© 2024 The Authors..
The construction system's complexity can generate substantial uncertainties during emergencies. Resilience, as a new perspective on emergency response, can significantly mitigate these challenges. This paper introduces an innovative model to assess the resilience of construction emergency response processes utilizing a scaffold collapse scenario as a demonstrative case study. Grounded in resilience engineering, our model integrates the merits of the Functional Resonance Analysis Method (FRAM) with the probabilistic strengths of Bayesian Networks (BNs). The process commences with FRAM, mapping out the emergency response in qualitative terms by identifying functions, variabilities, and couplings. This culminates in a topological network which serves as a foundational structure for the directed Complex Network (CN) and the BN model. Thereafter, the Delphi method and the modified K-shell (MKS) decomposition algorithm guide the computation of prior probabilities for root nodes and the conditional probability table within the BN model. Subsequently, the BN model is subjected to a simulation using the AgenaRisk software, executing both forward and backward propagation as well as sensitivity analyses. Our findings pinpoint "Intersectoral Coordination and Linkage" as the most crucial function, with rapidity being the most sensitive aspect influencing resilience during a scaffold collapse emergency response process.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 3 vom: 15. Feb., Seite e25342 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Zihao [VerfasserIn] |
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| Links: |
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| Themen: |
Bayesian networks |
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| Anmerkungen: |
Date Revised 16.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e25342 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368466191 |
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| 245 | 1 | 0 | |a Integrating FRAM and BN for enhanced resilience evaluation in construction emergency response |b A scaffold collapse case study |
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| 520 | |a © 2024 The Authors. | ||
| 520 | |a The construction system's complexity can generate substantial uncertainties during emergencies. Resilience, as a new perspective on emergency response, can significantly mitigate these challenges. This paper introduces an innovative model to assess the resilience of construction emergency response processes utilizing a scaffold collapse scenario as a demonstrative case study. Grounded in resilience engineering, our model integrates the merits of the Functional Resonance Analysis Method (FRAM) with the probabilistic strengths of Bayesian Networks (BNs). The process commences with FRAM, mapping out the emergency response in qualitative terms by identifying functions, variabilities, and couplings. This culminates in a topological network which serves as a foundational structure for the directed Complex Network (CN) and the BN model. Thereafter, the Delphi method and the modified K-shell (MKS) decomposition algorithm guide the computation of prior probabilities for root nodes and the conditional probability table within the BN model. Subsequently, the BN model is subjected to a simulation using the AgenaRisk software, executing both forward and backward propagation as well as sensitivity analyses. Our findings pinpoint "Intersectoral Coordination and Linkage" as the most crucial function, with rapidity being the most sensitive aspect influencing resilience during a scaffold collapse emergency response process | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayesian networks | |
| 650 | 4 | |a Construction emergency response | |
| 650 | 4 | |a FRAM | |
| 650 | 4 | |a Resilience evaluation | |
| 650 | 4 | |a Scaffold collapse | |
| 700 | 1 | |a She, Jianjun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhijian |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Jiewen |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Song |e verfasserin |4 aut | |
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