Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Yu, Y; Rashidi, M; Samali, B; Mohammadi, M; Nguyen, TN; Zhou, X

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Yu, Y

Rashidi, M Samali, B Mohammadi, M Nguyen, TN Zhou, X

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Publisher:: SAGE PUBLICATIONS LTD
Publication Type:: Journal Article
Citation:: Structural Health Monitoring, 2022, 21, (5), pp. 2244-2263
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Rashidi, M
dc.contributor.author	Samali, B
dc.contributor.author	Mohammadi, M
dc.contributor.author	Nguyen, TN
dc.contributor.author	Zhou, X
dc.date.accessioned	2023-04-18T04:47:54Z
dc.date.available	2023-04-18T04:47:54Z
dc.date.issued	2022-09-01
dc.identifier.citation	Structural Health Monitoring, 2022, 21, (5), pp. 2244-2263
dc.identifier.issn	1475-9217
dc.identifier.issn	1741-3168
dc.identifier.uri	http://hdl.handle.net/10453/169955
dc.description.abstract	With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.
dc.language	English
dc.publisher	SAGE PUBLICATIONS LTD
dc.relation.ispartof	Structural Health Monitoring
dc.relation.isbasedon	10.1177/14759217211053546
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering
dc.subject.classification	Acoustics
dc.title	Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	09 Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T04:47:52Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	5

Abstract:

With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

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http://hdl.handle.net/10453/169955