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    A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities

    Access Status
    In process
    Authors
    Pan, X.
    Yang, T.T.Y.
    Li, Jun
    Ventura, C.
    Málaga-Chuquitaype, C.
    Li, C.
    Su, R.K.L.
    Brzev, S.
    Date
    2025
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pan, X. and Yang, T.T.Y. and Li, J. and Ventura, C. and Málaga-Chuquitaype, C. and Li, C. and Su, R.K.L. et al. 2025. A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities. Archives of Computational Methods in Engineering.
    Source Title
    Archives of Computational Methods in Engineering
    DOI
    10.1007/s11831-025-10279-8
    ISSN
    1134-3060
    Faculty
    Faculty of Science and Engineering
    School
    School of Civil and Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/97439
    Collection
    • Curtin Research Publications
    Abstract

    Computer vision techniques have gained great traction in civil infrastructure inspection and monitoring. This paper conducted a systematic review of recent data-driven computer vision algorithms in structural damage detection published during the past 5 years. The theories of prevalent computer vision models are first reviewed with an emphasis on the progressive innovation in algorithms’ architecture. Then, recent applications of computer vision models for structural damage evaluation are discussed, which are classified into different structural categories by their material types (i.e., concrete, steel, masonry, timber) at three hierarchical levels including damage recognition, localization, and quantification. In particular, the paper also highlights the current state of using computer vision for damage assessment of timber structures, which remains under-explored compared to concrete and steel structures. Next, the paper scrutinizes existing structural damage inspection guidelines to identify key technological gaps between the capability of existing computer vision methods and manual inspection practices in the field. Finally, the paper summarizes existing challenges and recommends future research opportunities including the integration of computer vision methods with multimodal large language models, sensor-fusion, and mobile inspection approaches.

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