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An Intelligent Method for Identifying and Analyzing PCCP Wire-Break Signals Based on Prototype Testing and an Ensemble Deep Learning Model

dc.contributor.authorZhang, Ye
dc.contributor.authorYuan, Simin
dc.contributor.authorLi, Yanlong
dc.contributor.authorLi, Kangping
dc.contributor.authorZhou, Heng
dc.contributor.authorSun, Kaiyu
dc.date.accessioned2026-08-20T21:24:48Z
dc.date.available2026-08-20T21:24:48Z
dc.date.copyright2026/01/31
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-6666.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314411
dc.description.abstractAbstractWire breakage is the predominant cause of failure in prestressed concrete cylinder pipe (PCCP) within water diversion and transfer projects. This paper employs deep learning models to extract signal features and identify wire breakage types. A ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAn Intelligent Method for Identifying and Analyzing PCCP Wire-Break Signals Based on Prototype Testing and an Ensemble Deep Learning Model
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue3
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6666
dc.journal.fristpage04026014-1
dc.journal.lastpage04026014-18
dc.page18
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttypeFulltext


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