[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126137-en":3,"doc-seo-126137-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126137,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Infrastructure automated defect detection with machine learning - a systematic review","Infrastructure defects create major public safety risks and often trigger expensive repairs when not identified early. Machine learning has improved infrastructure inspection capabilities, yet a comprehensive synthesis and practical mapping of these methods across different infrastructure types remains limited. This systematic literature review (SLR) analyzes 123 papers to consolidate current Infrastructure Automated Defect Detection (IADD) approaches. Results show frequent use of deep learning, especially CNN variants, and emphasize the need for standardized, comprehensive datasets and further work on defect severity assessment.","International Journal of Construction  \nManagement  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tjcm20)[www.tandfonline.com/journals/tjcm20](homepage: www.tandfonline.com/journals/tjcm20)  \nInfrastructure automated defect detection with machine learning: a systematic review  \nSaeed Tale bi, Song Wu, Arijit Sen, Nazanin Zakizadeh, Quanbin Sun & Joseph Lai  \nTo cite this article: Saeed Tale bi, Song Wu, Arijit Sen, Nazanin Zakizadeh, Quanbin Sun & Joseph Lai (21 Apr 2025): Infrastructure automated defect detection with machine learning: a systematic review, International Journal of Construction Management, DOI:  \n10. 1080/15623599 .2025.2491622  \nTo link to this article: [https://doi.org/10.1080/15623599.2025.2491622](https://doi.org/10.1080/15623599.2025.2491622)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group  \n\n|  Published online: 21 Apr 2025. |  |\n| --- | --- |\n|  | Submit your article to this journal  |\n|  | Article views: 42 |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tjcm20](https://www.tandfonline.com/action/journalInformation?journalCode=tjcm20)  \nINTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT [https://doi.org/10.1080/15623599.2025.2491622](https://doi.org/10.1080/15623599.2025.2491622)  \nInfrastructure automated defect detection with machine learning: a systematic review  \nSaeed Talebia, Song Wub, Arijit Senb, Nazanin Zakizadehc, Quanbin Sund and Joseph Laie  \naCollege of Built Environment, Birmingham City University, Birmingham, UK; bSchool of Architecture, Design and the Built Environment, Nottingham Trent University, Nottingham, UK; cThe School of Built Environment and Geography, Kingston University, UK; dCollege of Computing, Birmingham City University, Birmingham, UK; eDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong  \nABSTRACT  \nInfrastructure 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.  \nARTICLE HISTORY  \nReceived 25 August 2024 Accepted 5 April 2025  \nKEYWORDS  \nMachine learning;  \nautomated defect detection; infrastructure; image processing; classification algorithms; infrastructure defects  \nIntroduction  \nCritical infrastructures globally are frequently exposed to severe physical stress from acute and chronic catastrophes such as earthquakes, floods, and ageing deterioration (Munawar et al. 2021). Managing these infrastructures often falls under the purview of municipal b","cbCaivkfcRSnRWsz","https://ap.wps.com/l/cbCaivkfcRSnRWsz","pdf",2341882,5,1,13,"English","en",105,"# Introduction\n## Condition monitoring and defect detection\n## Traditional visual inspections and limitations\n## Shift to Infrastructure Automated Defect Detection (IADD)\n# Machine learning methods in IADD\n## Image analysis approaches and motivations\n## Common model types and applications","[{\"question\":\"Why is infrastructure automated defect detection important for public safety and cost control?\",\"answer\":\"Infrastructure defects can lead to safety hazards if not detected early, and undetected issues often result in costly repairs. Automated detection helps identify defects sooner and support safer asset management.\"},{\"question\":\"How does the study conduct the systematic literature review and what does it analyze?\",\"answer\":\"The research applies a systematic literature review (SLR) approach and analyzes 123 papers covering machine learning methods used in IADD.\"},{\"question\":\"What do the findings suggest about machine learning models and dataset needs for IADD?\",\"answer\":\"The review finds widespread use of deep learning, particularly convolutional neural network (CNN) variants, across roads, bridges, and sewers. It also highlights that standardized, comprehensive datasets are critical for training and testing more effectively.\"}]","Infrastructure automated defect detection with machine learning - a systematic review | PDF",1785903349,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"infrastructure-automated-defect-detection-with-machine-learning-a-systematic-review","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/infrastructure-automated-defect-detection-with-machine-learning-a-systematic-review/126137/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is infrastructure automated defect detection important for public safety and cost control?","Question",{"text":77,"@type":78},"Infrastructure defects can lead to safety hazards if not detected early, and undetected issues often result in costly repairs. Automated detection helps identify defects sooner and support safer asset management.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the study conduct the systematic literature review and what does it analyze?",{"text":82,"@type":78},"The research applies a systematic literature review (SLR) approach and analyzes 123 papers covering machine learning methods used in IADD.",{"name":84,"@type":75,"acceptedAnswer":85},"What do the findings suggest about machine learning models and dataset needs for IADD?",{"text":86,"@type":78},"The review finds widespread use of deep learning, particularly convolutional neural network (CNN) variants, across roads, bridges, and sewers. 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