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dc.contributor.authorSadak, Muhammet Fatih
dc.contributor.authorLav, Abdullah Hilmi
dc.date.accessioned2026-08-20T12:02:13Z
dc.date.available2026-08-20T12:02:13Z
dc.date.copyright2025/11/08
dc.date.issued2026
dc.identifier.otherJPEODX.PVENG-1880.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313006
dc.description.abstractAbstractThis study presents a deep-learning-based approach to estimating the pavement condition index (PCI) to evaluate asphalt deterioration. Current machine learning and deep learning models are integrated to optimize traditional methods, which are ...Practical ApplicationsRoad agencies need fast, accurate, and cost-effective ways to monitor pavement health and prioritize maintenance. Traditional methods rely on manual surveys to detect damage and calculate the pavement condition index (PCI), which can ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePCI Estimation Method for Evaluating Asphalt Deterioration Leveraging SAM and YOLOv8
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue1
dc.journal.titleJournal of Transportation Engineering, Part B: Pavements
dc.identifier.doi10.1061/JPEODX.PVENG-1880
dc.journal.fristpage04025058-1
dc.journal.lastpage04025058-11
dc.page11
dc.treeJournal of Transportation Engineering, Part B: Pavements:;2026:;Volume ( 152 ):;issue: 001
dc.contenttypeFulltext


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