Deep learning in crack detection: A comprehensive scientometric review

Wu, Y; Li, S; Li, J; Yu, Y; Li, J; Li, Y

Deep learning in crack detection: A comprehensive scientometric review

Wu, Y Li, S Li, J

Yu, Y Li, J

Li, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Infrastructure Intelligence and Resilience, 2025, 4, (3), pp. 100144
Issue Date:: 2025-09-01

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Field	Value	Language
dc.contributor.author	Wu, Y
dc.contributor.author	Li, S
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.contributor.author	Yu, Y
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048
dc.contributor.author	Li, Y https://orcid.org/0000-0002-6720-8493
dc.date.accessioned	2025-04-13T23:53:29Z
dc.date.available	2025-04-13T23:53:29Z
dc.date.issued	2025-09-01
dc.identifier.citation	Journal of Infrastructure Intelligence and Resilience, 2025, 4, (3), pp. 100144
dc.identifier.issn	2772-9915
dc.identifier.issn	2772-9915
dc.identifier.uri	http://hdl.handle.net/10453/186859
dc.description.abstract	Cracks represent one of the common forms of damage in concrete structures and pavements, leading to safety issues and increased maintenance costs. Therefore, timely crack detection is crucial for preventing further damage and ensuring the safety of these structures. Traditional manual inspection methods are limited by factors such as time consumption, subjectivity, and labor intensity. To address these challenges, deep learning-based crack detection technologies have emerged as promising solutions, demonstrating satisfactory performance and accuracy. However, the field still lacks comprehensive scientometric analyses and critical surveys of existing works, which are vital for identifying research gaps and guiding future studies. This paper conducts a bibliometric and critical analysis of the collected literature, providing novel insights into current research trends and identifying potential areas for future investigation. Analytical tools, including VOSviewer and CiteSpace, were employed for in-depth analysis and visualization. This study identifies key research gaps and proposes future directions, focusing on advancements in model generalization, computational efficiency, dataset standardization, and the practical application of crack detection methods.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Infrastructure Intelligence and Resilience
dc.relation.isbasedon	10.1016/j.iintel.2025.100144
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Deep learning in crack detection: A comprehensive scientometric review
dc.type	Journal Article
utslib.citation.volume	4
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-13T23:53:27Z
pubs.issue	3
pubs.publication-status	Accepted
pubs.volume	4
utslib.citation.issue	3

Abstract:

Cracks represent one of the common forms of damage in concrete structures and pavements, leading to safety issues and increased maintenance costs. Therefore, timely crack detection is crucial for preventing further damage and ensuring the safety of these structures. Traditional manual inspection methods are limited by factors such as time consumption, subjectivity, and labor intensity. To address these challenges, deep learning-based crack detection technologies have emerged as promising solutions, demonstrating satisfactory performance and accuracy. However, the field still lacks comprehensive scientometric analyses and critical surveys of existing works, which are vital for identifying research gaps and guiding future studies. This paper conducts a bibliometric and critical analysis of the collected literature, providing novel insights into current research trends and identifying potential areas for future investigation. Analytical tools, including VOSviewer and CiteSpace, were employed for in-depth analysis and visualization. This study identifies key research gaps and proposes future directions, focusing on advancements in model generalization, computational efficiency, dataset standardization, and the practical application of crack detection methods.

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http://hdl.handle.net/10453/186859