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Multisource Feature Aggregation Approach for Predicting Extreme Pitting Depth Distributions of In Situ Bridge Cable Wires

dc.contributor.authorZhang, He
dc.contributor.authorXin, Yuchen
dc.contributor.authorYao, Linjie
dc.contributor.authorZheng, Xianglong
dc.contributor.authorZhang, Zhicheng
dc.date.accessioned2026-08-20T21:22:15Z
dc.date.available2026-08-20T21:22:15Z
dc.date.copyright2026/05/21
dc.date.issued2026
dc.identifier.otherJBENF2.BEENG-8078.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314350
dc.description.abstractAbstract Extreme pitting depths in bridge cable steel wires serve as fatigue crack initiation sites, making accurate assessment crucial for failure risk prediction. Existing methods require rust removal and cable disassembly, which not only disrupts ...
dc.publisherAmerican Society of Civil Engineers
dc.titleMultisource Feature Aggregation Approach for Predicting Extreme Pitting Depth Distributions of In Situ Bridge Cable Wires
dc.typeJournal Article
dc.journal.volume31
dc.journal.issue8
dc.journal.titleJournal of Bridge Engineering
dc.identifier.doi10.1061/JBENF2.BEENG-8078
dc.journal.fristpage04026037-1
dc.journal.lastpage04026037-18
dc.page18
dc.treeJournal of Bridge Engineering:;2026:;Volume ( 031 ):;issue: 008
dc.contenttypeFulltext


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