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dc.contributor.authorBayat, Mahmoud
dc.contributor.authorKharel, Subham
dc.contributor.authorLi, Jianling
dc.date.accessioned2026-08-20T12:10:04Z
dc.date.available2026-08-20T12:10:04Z
dc.date.copyright2025/07/01
dc.date.issued2025
dc.identifier.otherJSDCCC.SCENG-1799.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313176
dc.description.abstractAbstractBridges are crucial to the global economy, facilitating mobility and accessibility. Effective monitoring and maintenance of these structures are vital due to aging, heavy traffic, and variable construction quality. While recent studies have ...Practical ApplicationsThe practical application of this research lies in its potential to enhance the accuracy and efficiency of predicting bridge deck condition ratings. Traditionally, condition ratings are assessed qualitatively by experts based on ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAn Autoencoder-Based Machine- and Deep-Learning Approach for Predicting Bridge Deck Conditions in Texas
dc.typeJournal Article
dc.journal.volume30
dc.journal.issue4
dc.journal.titleJournal of Structural Design and Construction Practice
dc.identifier.doi10.1061/JSDCCC.SCENG-1799
dc.journal.fristpage04025080-1
dc.journal.lastpage04025080-16
dc.page16
dc.treeJournal of Structural Design and Construction Practice:;2025:;Volume ( 030 ):;issue: 004
dc.contenttypeFulltext


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