| contributor author | Sadak, Muhammet Fatih | |
| contributor author | Lav, Abdullah Hilmi | |
| date accessioned | 2026-08-20T12:02:13Z | |
| date available | 2026-08-20T12:02:13Z | |
| date copyright | 2025/11/08 | |
| date issued | 2026 | |
| identifier other | JPEODX.PVENG-1880.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4313006 | |
| description abstract | AbstractThis 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | PCI Estimation Method for Evaluating Asphalt Deterioration Leveraging SAM and YOLOv8 | |
| type | Journal Article | |
| journal volume | 152 | |
| journal issue | 1 | |
| journal title | Journal of Transportation Engineering, Part B: Pavements | |
| identifier doi | 10.1061/JPEODX.PVENG-1880 | |
| journal fristpage | 04025058-1 | |
| journal lastpage | 04025058-11 | |
| page | 11 | |
| tree | Journal of Transportation Engineering, Part B: Pavements:;2026:;Volume ( 152 ):;issue: 001 | |
| contenttype | Fulltext | |