Show simple item record

Field Data–Driven Machine-Learning Approach for Estimating Pavement Skid Numbers from LWST Measurements for Improved Asphalt Pavement Safety

dc.contributor.authorKhasawneh, Mohammad Ali
dc.contributor.authorMewada, Hiren
dc.contributor.authorRababah, Samer
dc.contributor.authorNayeemuddin, Mohammed
dc.contributor.authorKhasawneh, Ahmad Ali
dc.contributor.authorAlsheyab, Mohammad Ahmad
dc.date.accessioned2026-08-20T12:01:40Z
dc.date.available2026-08-20T12:01:40Z
dc.date.copyright2026/04/19
dc.date.issued2026
dc.identifier.otherJPEODX.PVENG-1822.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312994
dc.description.abstractAbstract This study investigated the feasibility of predicting skid numbers (SNs) measured by a locked-wheel skid trailer (LWST) using more-accessible surface friction and texture data from a dynamic friction tester (DFT), British pendulum tester (BPT), ... Practical Applications Accurately measuring pavement friction is crucial for maintaining roadway safety, but traditional methods, such as locked-wheel skid trailer (LWST) testing, are time-consuming, expensive, and require traffic disruptions. This study ...
dc.publisherAmerican Society of Civil Engineers
dc.titleField Data–Driven Machine-Learning Approach for Estimating Pavement Skid Numbers from LWST Measurements for Improved Asphalt Pavement Safety
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue3
dc.journal.titleJournal of Transportation Engineering, Part B: Pavements
dc.identifier.doi10.1061/JPEODX.PVENG-1822
dc.journal.fristpage04026020-1
dc.journal.lastpage04026020-16
dc.page16
dc.treeJournal of Transportation Engineering, Part B: Pavements:;2026:;Volume ( 152 ):;issue: 003
dc.contenttypeFulltext


Files in this item

Thumbnail
Name:
JPEODX.PVENG-1822.pdf
Size:
6.636Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record