Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network

Zhang, X; Li, ZX; Shi, Y; Wu, C; Li, J

Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network

Zhang, X Li, ZX Shi, Y Wu, C

Li, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Building Engineering, 2022, 52
Issue Date:: 2022-07-15

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Field	Value	Language
dc.contributor.author	Zhang, X
dc.contributor.author	Li, ZX
dc.contributor.author	Shi, Y
dc.contributor.author	Wu, C https://orcid.org/0000-0001-6786-7934
dc.contributor.author	Li, J https://orcid.org/0000-0003-2457-1994
dc.date.accessioned	2023-03-26T21:53:26Z
dc.date.available	2023-03-26T21:53:26Z
dc.date.issued	2022-07-15
dc.identifier.citation	Journal of Building Engineering, 2022, 52
dc.identifier.issn	2352-7102
dc.identifier.issn	2352-7102
dc.identifier.uri	http://hdl.handle.net/10453/168428
dc.description.abstract	In this paper, fragility analysis for performance-based blast design of FRP-strengthened RC columns is carried out. The blast intensity levels, performance levels and performance objectives of the RC columns are defined. A simplified probabilistic risk assessment framework incorporating the performance-based design concept and fragility analysis is established. Since fragility analysis is the most important but time-consuming process of probabilistic assessment risk, an artificial neural network (ANN) based fragility analysis framework is proposed to improve its computational efficiency. Based on the rapid fragility analysis method, fragility curves of several typical RC columns with or without FRP strengthening are calculated to analyze their damage probabilities. This study provides avenues for engineers to estimate the failure probabilities of RC columns with or without FRP strengthening under blast loads and make decisions quickly.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Building Engineering
dc.relation.isbasedon	10.1016/j.jobe.2022.104364
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 1201 Architecture, 1202 Building
dc.title	Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	0905 Civil Engineering
utslib.for	1201 Architecture
utslib.for	1202 Building
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-26T21:53:25Z
pubs.publication-status	Published
pubs.volume	52

Abstract:

In this paper, fragility analysis for performance-based blast design of FRP-strengthened RC columns is carried out. The blast intensity levels, performance levels and performance objectives of the RC columns are defined. A simplified probabilistic risk assessment framework incorporating the performance-based design concept and fragility analysis is established. Since fragility analysis is the most important but time-consuming process of probabilistic assessment risk, an artificial neural network (ANN) based fragility analysis framework is proposed to improve its computational efficiency. Based on the rapid fragility analysis method, fragility curves of several typical RC columns with or without FRP strengthening are calculated to analyze their damage probabilities. This study provides avenues for engineers to estimate the failure probabilities of RC columns with or without FRP strengthening under blast loads and make decisions quickly.

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http://hdl.handle.net/10453/168428