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    Network-Level Infrastructure Asset Management with Multiagent Actor–Critic Reinforcement Learning: A Case of Highway Bridges

    Source: Journal of Infrastructure Systems:;2025:;Volume ( 031 ):;issue: 004::page 04025026-1
    Author:
    Asghari, Vahid
    ,
    Jahanbiglari, Ava
    ,
    Hsu, Shu-Chien
    DOI: 10.1061/JITSE4.ISENG-2607
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe network-level infrastructure asset management (NL-IAM) problem is excessively large and complex due to a large number of decision parameters, possible strategies, and underlying uncertainties. Although it often leads to far-from-reality ...Practical ApplicationsManaging infrastructure networks is essential for economic stability and community resilience, but uncertainties such as aging, deterioration, and cost fluctuations make it challenging. This study introduces a decision-making ...
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      Network-Level Infrastructure Asset Management with Multiagent Actor–Critic Reinforcement Learning: A Case of Highway Bridges

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4311801
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    contributor authorAsghari, Vahid
    contributor authorJahanbiglari, Ava
    contributor authorHsu, Shu-Chien
    date accessioned2026-08-20T11:10:44Z
    date available2026-08-20T11:10:44Z
    date copyright2025/07/29
    date issued2025
    identifier otherJITSE4.ISENG-2607.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311801
    description abstractAbstractThe network-level infrastructure asset management (NL-IAM) problem is excessively large and complex due to a large number of decision parameters, possible strategies, and underlying uncertainties. Although it often leads to far-from-reality ...Practical ApplicationsManaging infrastructure networks is essential for economic stability and community resilience, but uncertainties such as aging, deterioration, and cost fluctuations make it challenging. This study introduces a decision-making ...
    publisherAmerican Society of Civil Engineers
    titleNetwork-Level Infrastructure Asset Management with Multiagent Actor–Critic Reinforcement Learning: A Case of Highway Bridges
    typeJournal Article
    journal volume31
    journal issue4
    journal titleJournal of Infrastructure Systems
    identifier doi10.1061/JITSE4.ISENG-2607
    journal fristpage04025026-1
    journal lastpage04025026-15
    page15
    treeJournal of Infrastructure Systems:;2025:;Volume ( 031 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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