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Artificial Intelligence for Building Defect Detection: A Systematic Review of Techniques and Benefits.

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Title: Artificial Intelligence for Building Defect Detection: A Systematic Review of Techniques and Benefits.
Authors: G, Angelin Lincy1 g.angelinlincy@gmail.com, Chellappa, Vigneshkumar2 vkumar.chellappa@polyu.edu.hk
Source: Journal of Structural Design & Construction Practice. Aug2026, Vol. 31 Issue 3, p1-9. 9p.
Subjects: Building failures, Artificial intelligence, Machine learning, Computer vision, Natural language processing
Abstract: The adoption of artificial intelligence (AI) techniques for detecting building defects has significantly transformed traditional inspection methods in recent years. However, the application of AI techniques for detecting building defects and their benefits is limited. This study aimed to examine applications of AI in defect detection and their benefits through a systematic literature review (SLR). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guidelines, 39 journal documents were retrieved from the Scopus and Web of Science (WoS) databases. The study found that six AI techniques were employed: machine learning (ML), computer vision, natural language processing (NLP), knowledge-based systems (KBS), automated planning and Scheduling (APS), and optimization. The findings indicated that ML, computer vision, and NLP are emerging techniques that have been most widely used in recent years because of their increased accuracy, efficiency, and real-time monitoring capabilities. The study findings address the knowledge gap in the current literature and could assist practitioners and stakeholders in implementing AI techniques for detecting building defects. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Structural Design & Construction Practice is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Artificial Intelligence for Building Defect Detection: A Systematic Review of Techniques and Benefits.
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  Data: <searchLink fieldCode="AR" term="%22G%2C+Angelin+Lincy%22">G, Angelin Lincy</searchLink><relatesTo>1</relatesTo><i> g.angelinlincy@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Chellappa%2C+Vigneshkumar%22">Chellappa, Vigneshkumar</searchLink><relatesTo>2</relatesTo><i> vkumar.chellappa@polyu.edu.hk</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Structural+Design+%26+Construction+Practice%22">Journal of Structural Design & Construction Practice</searchLink>. Aug2026, Vol. 31 Issue 3, p1-9. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Building+failures%22">Building failures</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink>
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  Label: Abstract
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  Data: The adoption of artificial intelligence (AI) techniques for detecting building defects has significantly transformed traditional inspection methods in recent years. However, the application of AI techniques for detecting building defects and their benefits is limited. This study aimed to examine applications of AI in defect detection and their benefits through a systematic literature review (SLR). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guidelines, 39 journal documents were retrieved from the Scopus and Web of Science (WoS) databases. The study found that six AI techniques were employed: machine learning (ML), computer vision, natural language processing (NLP), knowledge-based systems (KBS), automated planning and Scheduling (APS), and optimization. The findings indicated that ML, computer vision, and NLP are emerging techniques that have been most widely used in recent years because of their increased accuracy, efficiency, and real-time monitoring capabilities. The study findings address the knowledge gap in the current literature and could assist practitioners and stakeholders in implementing AI techniques for detecting building defects. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Structural Design & Construction Practice is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1061/JSDCCC.SCENG-2070
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      – Code: eng
        Text: English
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        PageCount: 9
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      – SubjectFull: Building failures
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Computer vision
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      – SubjectFull: Natural language processing
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              Text: Aug2026
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              Y: 2026
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