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    Vision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning

    Source: Journal of Structural Engineering:;2026:;Volume ( 152 ):;issue: 005::page 04026047-1
    Author:
    Bazrafshan, Pedram
    ,
    Osan, Rhythm
    ,
    Ebrahimkhanlou, Arvin
    DOI: 10.1061/JSENDH.STENG-15932
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe rapid and accurate assessment of structural damage in reinforced concrete shear walls (RCSWs) following seismic events remains a critical yet unresolved challenge in structural engineering. Current methods rely heavily on subjective visual ...
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      Vision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313523
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    • Journal of Structural Engineering

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    contributor authorBazrafshan, Pedram
    contributor authorOsan, Rhythm
    contributor authorEbrahimkhanlou, Arvin
    date accessioned2026-08-20T12:29:01Z
    date available2026-08-20T12:29:01Z
    date copyright2026/02/27
    date issued2026
    identifier otherJSENDH.STENG-15932.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313523
    description abstractAbstractThe rapid and accurate assessment of structural damage in reinforced concrete shear walls (RCSWs) following seismic events remains a critical yet unresolved challenge in structural engineering. Current methods rely heavily on subjective visual ...
    publisherAmerican Society of Civil Engineers
    titleVision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning
    typeJournal Article
    journal volume152
    journal issue5
    journal titleJournal of Structural Engineering
    identifier doi10.1061/JSENDH.STENG-15932
    journal fristpage04026047-1
    journal lastpage04026047-14
    page14
    treeJournal of Structural Engineering:;2026:;Volume ( 152 ):;issue: 005
    contenttypeFulltext
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