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Anomaly Detection and Correction Framework for Pavement Maintenance and Rehabilitation Data Based on LSTM

dc.contributor.authorXiao, Feng
dc.contributor.authorSong, Qianhong
dc.contributor.authorGao, Jie
dc.contributor.authorYang, Shunxin
dc.contributor.authorChen, Huapeng
dc.date.accessioned2026-08-20T12:02:43Z
dc.date.available2026-08-20T12:02:43Z
dc.date.copyright2026/01/29
dc.date.issued2026
dc.identifier.otherJPEODX.PVENG-1967.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313019
dc.description.abstractAbstractPavement maintenance and rehabilitation (M&R) data anomalies critically undermine pavement management system (PMS) reliability, yet conventional artificial neural network (ANN)-based correction methods fail to capture inherent temporal ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAnomaly Detection and Correction Framework for Pavement Maintenance and Rehabilitation Data Based on LSTM
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue2
dc.journal.titleJournal of Transportation Engineering, Part B: Pavements
dc.identifier.doi10.1061/JPEODX.PVENG-1967
dc.journal.fristpage04026007-1
dc.journal.lastpage04026007-13
dc.page13
dc.treeJournal of Transportation Engineering, Part B: Pavements:;2026:;Volume ( 152 ):;issue: 002
dc.contenttypeFulltext


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