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contributor authorSadak, Muhammet Fatih
contributor authorLav, Abdullah Hilmi
date accessioned2026-08-20T12:02:13Z
date available2026-08-20T12:02:13Z
date copyright2025/11/08
date issued2026
identifier otherJPEODX.PVENG-1880.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313006
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 ...
publisherAmerican Society of Civil Engineers
titlePCI Estimation Method for Evaluating Asphalt Deterioration Leveraging SAM and YOLOv8
typeJournal Article
journal volume152
journal issue1
journal titleJournal of Transportation Engineering, Part B: Pavements
identifier doi10.1061/JPEODX.PVENG-1880
journal fristpage04025058-1
journal lastpage04025058-11
page11
treeJournal of Transportation Engineering, Part B: Pavements:;2026:;Volume ( 152 ):;issue: 001
contenttypeFulltext


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