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    Deep-Learning Framework for Dynamic Deflection Prediction of Suspension Bridges under Temperature, Traffic, and Wind Loads

    Source: Journal of Bridge Engineering:;2026:;Volume ( 031 ):;issue: 002::page 04025100-1
    Author:
    Wang, Zhi-wei
    ,
    Zhang, Wen-ming
    ,
    Lu, Xiao-fan
    ,
    Fragkoulis, Vasileios C.
    ,
    Beer, Michael
    DOI: 10.1061/JBENF2.BEENG-7818
    Publisher: American Society of Civil Engineers
    Abstract: Abstract A data-driven predictive model for dynamic deflection response can aid the safe operation and maintenance of a suspension bridge. In particular, it lays the groundwork for bridge condition assessment and damage detection, dynamic reliability ...
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      Deep-Learning Framework for Dynamic Deflection Prediction of Suspension Bridges under Temperature, Traffic, and Wind Loads

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4314330
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    contributor authorWang, Zhi-wei
    contributor authorZhang, Wen-ming
    contributor authorLu, Xiao-fan
    contributor authorFragkoulis, Vasileios C.
    contributor authorBeer, Michael
    date accessioned2026-08-20T21:21:15Z
    date available2026-08-20T21:21:15Z
    date copyright2025/11/19
    date issued2026
    identifier otherJBENF2.BEENG-7818.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314330
    description abstractAbstract A data-driven predictive model for dynamic deflection response can aid the safe operation and maintenance of a suspension bridge. In particular, it lays the groundwork for bridge condition assessment and damage detection, dynamic reliability ...
    publisherAmerican Society of Civil Engineers
    titleDeep-Learning Framework for Dynamic Deflection Prediction of Suspension Bridges under Temperature, Traffic, and Wind Loads
    typeJournal Article
    journal volume31
    journal issue2
    journal titleJournal of Bridge Engineering
    identifier doi10.1061/JBENF2.BEENG-7818
    journal fristpage04025100-1
    journal lastpage04025100-21
    page21
    treeJournal of Bridge Engineering:;2026:;Volume ( 031 ):;issue: 002
    contenttypeFulltext
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