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When Roads Go Underwater: AI-Enhanced Digital Twin–Driven Flood Resilience for Roadway Serviceability Assessment

dc.contributor.authorShariatfar, Moeid
dc.contributor.authorYin, Yangtian
dc.contributor.authorLee, Yong-Cheol
dc.contributor.authorChoi, Kunhee
dc.date.accessioned2026-08-20T11:50:23Z
dc.date.available2026-08-20T11:50:23Z
dc.date.copyright2026/01/31
dc.date.issued2026
dc.identifier.otherJMENEA.MEENG-7046.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312741
dc.description.abstractAbstractExtreme flooding poses escalating risks to roadway infrastructure, threatening structural integrity, operational reliability, and public safety. Existing flood-monitoring approaches primarily utilize sparse sensor networks, thus providing limited ...
dc.publisherAmerican Society of Civil Engineers
dc.titleWhen Roads Go Underwater: AI-Enhanced Digital Twin–Driven Flood Resilience for Roadway Serviceability Assessment
dc.typeJournal Article
dc.journal.volume42
dc.journal.issue3
dc.journal.titleJournal of Management in Engineering
dc.identifier.doi10.1061/JMENEA.MEENG-7046
dc.journal.fristpage04026004-1
dc.journal.lastpage04026004-13
dc.page13
dc.treeJournal of Management in Engineering:;2026:;Volume ( 042 ):;issue: 003
dc.contenttypeFulltext


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