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    A Feature Selection Framework for Ground Motion Intensity Measures in the Rapid Seismic Damage Prediction of Structures Based on Ensemble Learning

    Source: Journal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 009::page 04025141-1
    Author:
    Zhang, Hui
    ,
    Yu, Dinghao
    ,
    Li, Gang
    ,
    Dong, Zhiqian
    DOI: 10.1061/JSENDH.STENG-13993
    Publisher: American Society of Civil Engineers
    Abstract: AbstractIn traditional fragility analysis for regional damage assessment, low-dimensional intensity measures (IMs), such as peak ground acceleration (PGA), are typically chosen to characterize ground motion (GM) and predict structural damage based on the ...
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      A Feature Selection Framework for Ground Motion Intensity Measures in the Rapid Seismic Damage Prediction of Structures Based on Ensemble Learning

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313287
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    • Journal of Structural Engineering

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    contributor authorZhang, Hui
    contributor authorYu, Dinghao
    contributor authorLi, Gang
    contributor authorDong, Zhiqian
    date accessioned2026-08-20T12:15:33Z
    date available2026-08-20T12:15:33Z
    date copyright2025/07/11
    date issued2025
    identifier otherJSENDH.STENG-13993.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313287
    description abstractAbstractIn traditional fragility analysis for regional damage assessment, low-dimensional intensity measures (IMs), such as peak ground acceleration (PGA), are typically chosen to characterize ground motion (GM) and predict structural damage based on the ...
    publisherAmerican Society of Civil Engineers
    titleA Feature Selection Framework for Ground Motion Intensity Measures in the Rapid Seismic Damage Prediction of Structures Based on Ensemble Learning
    typeJournal Article
    journal volume151
    journal issue9
    journal titleJournal of Structural Engineering
    identifier doi10.1061/JSENDH.STENG-13993
    journal fristpage04025141-1
    journal lastpage04025141-24
    page24
    treeJournal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 009
    contenttypeFulltext
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