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Pavement Performance Prediction for Continuous Road Sections Considering Spatial Correlation: A Deep Learning–Based Framework

dc.contributor.authorZeng, Qingwei
dc.contributor.authorHe, Xinyang
dc.contributor.authorYang, Shunxin
dc.contributor.authorCui, Qixuan
dc.contributor.authorXu, Chang
dc.date.accessioned2026-08-20T21:23:37Z
dc.date.available2026-08-20T21:23:37Z
dc.date.copyright2025/06/16
dc.date.issued2025
dc.identifier.otherJCCEE5.CPENG-6426.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314379
dc.description.abstractAbstractThe accuracy of pavement performance prediction models directly affects the development of pavement maintenance and rehabilitation (M&R) decision plans. However, existing pavement performance prediction models ignore the interactions of adjacent ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePavement Performance Prediction for Continuous Road Sections Considering Spatial Correlation: A Deep Learning–Based Framework
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue5
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6426
dc.journal.fristpage04025067-1
dc.journal.lastpage04025067-13
dc.page13
dc.treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 005
dc.contenttypeFulltext


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