Infrastructure automated defect detection with machine learning: a systematic review

Talebi, S, Wu, S ORCID: https://orcid.org/0000-0002-5633-2229, Sen, A ORCID: https://orcid.org/0000-0001-8967-9475, Zakizadeh, N, Sun, Q and Lai, J, 2025. Infrastructure automated defect detection with machine learning: a systematic review. International Journal of Construction Management. ISSN 1562-3599

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Abstract

Infrastructure defects pose significant public safety risks and, if undetected, can lead to costly repairs. While machine learning (ML) technologies have significantly enhanced the capabilities for inspecting infrastructure, a comprehensive synthesis of these advancements and their practical application across various infrastructures is lacking. This study addresses this gap by providing a literature review, offering a consolidated view of current ML methodologies in Infrastructure Automated Defect Detection (IADD). This research employs a systematic literature review (SLR) approach to analyse 123 papers on ML methodologies applied to IADD. The analysis reveals the wide use of deep learning architectures like Convolutional Neural Network and its variants, which perform well in defect detection across various infrastructures, including roads, bridges, and sewers. However, standardised, comprehensive datasets are critical to train and test these models more effectively. The study also highlights the importance of developing ML approaches that can accurately assess the severity of defects, an area currently underexplored but with significant implications for risk management in infrastructure. This SLR provides a consolidated perspective on ML technologies’ advancements and practical applications in IADD, and it offers substantial value to researchers, engineers, and policymakers engaged in infrastructure asset management.

Item Type:	Journal article
Publication Title:	International Journal of Construction Management
Creators:	Talebi, S., Wu, S., Sen, A., Zakizadeh, N., Sun, Q. and Lai, J.
Publisher:	Taylor & Francis
Date:	21 April 2025
ISSN:	1562-3599
Identifiers:	Number Type 10.1080/15623599.2025.2491622 DOI 2430762 Other
Rights:	© 2025 the author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Divisions:	Schools > School of Architecture, Design and the Built Environment
Record created by:	Jonathan Gallacher
Date Added:	28 Apr 2025 15:45
Last Modified:	28 Apr 2025 15:45
URI:	https://irep.ntu.ac.uk/id/eprint/53486

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