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    Causality Constrained Deep Learning for Explainable Risk Analysis of Bridge Pier Settlement

    Source: Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002::page 04025156-1
    Author:
    Qi, Yafei
    ,
    Liu, Wenli
    ,
    Qu, Yao
    ,
    Liu, Tianxiang
    ,
    Shao, Yixiao
    ,
    Liu, Fenghua
    DOI: 10.1061/JCCEE5.CPENG-7330
    Publisher: American Society of Civil Engineers
    Abstract: AbstractDeep learning (DL) has significantly enhanced risk management in highway bridge operation. Although DL models exhibit strong predictive capabilities, their outputs derive from “black box” processes and cannot characterize uncertainty, leading to ...
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      Causality Constrained Deep Learning for Explainable Risk Analysis of Bridge Pier Settlement

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4314513
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    • Journal of Computing in Civil Engineering

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    contributor authorQi, Yafei
    contributor authorLiu, Wenli
    contributor authorQu, Yao
    contributor authorLiu, Tianxiang
    contributor authorShao, Yixiao
    contributor authorLiu, Fenghua
    date accessioned2026-08-20T21:28:26Z
    date available2026-08-20T21:28:26Z
    date copyright2025/11/27
    date issued2026
    identifier otherJCCEE5.CPENG-7330.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314513
    description abstractAbstractDeep learning (DL) has significantly enhanced risk management in highway bridge operation. Although DL models exhibit strong predictive capabilities, their outputs derive from “black box” processes and cannot characterize uncertainty, leading to ...
    publisherAmerican Society of Civil Engineers
    titleCausality Constrained Deep Learning for Explainable Risk Analysis of Bridge Pier Settlement
    typeJournal Article
    journal volume40
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-7330
    journal fristpage04025156-1
    journal lastpage04025156-20
    page20
    treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002
    contenttypeFulltext
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