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    Evaluating the Resilience of Graph Neural Network Architectures to Adversarial and Noisy Data in High-Stakes Construction Project Management

    Source: Journal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 004::page 04026029-1
    Author:
    Toğan, Vedat
    ,
    Mostofi, Fatemeh
    ,
    Tokdemir, Onur Behzat
    DOI: 10.1061/JCEMD4.COENG-17244
    Publisher: American Society of Civil Engineers
    Abstract: AbstractHigh-stakes mega-construction projects present a challenging environment for decision-support models, as they are exposed to risks from both deliberate attacks and unintentional errors. These vulnerabilities can degrade model performance, leading ...
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      Evaluating the Resilience of Graph Neural Network Architectures to Adversarial and Noisy Data in High-Stakes Construction Project Management

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4311131
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    contributor authorToğan, Vedat
    contributor authorMostofi, Fatemeh
    contributor authorTokdemir, Onur Behzat
    date accessioned2026-08-20T10:41:33Z
    date available2026-08-20T10:41:33Z
    date copyright2026/02/06
    date issued2026
    identifier otherJCEMD4.COENG-17244.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311131
    description abstractAbstractHigh-stakes mega-construction projects present a challenging environment for decision-support models, as they are exposed to risks from both deliberate attacks and unintentional errors. These vulnerabilities can degrade model performance, leading ...
    publisherAmerican Society of Civil Engineers
    titleEvaluating the Resilience of Graph Neural Network Architectures to Adversarial and Noisy Data in High-Stakes Construction Project Management
    typeJournal Article
    journal volume152
    journal issue4
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/JCEMD4.COENG-17244
    journal fristpage04026029-1
    journal lastpage04026029-16
    page16
    treeJournal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 004
    contenttypeFulltext
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