Predictive Temperature Modeling in Granular Base and Subgrade Layers Using Machine Learning: A Smart Road Study in Edmonton, Alberta, Canada

dc.contributor.author	Awan, Malik Noor-Ul-Amin
dc.contributor.author	Barbosa de Carvalho, Silas Henrique
dc.contributor.author	Hashemian, Leila
dc.contributor.author	Shafiee, Mohammad
dc.contributor.author	Bayat, Alireza
dc.date.accessioned	2026-08-20T10:47:34Z
dc.date.available	2026-08-20T10:47:34Z
dc.date.copyright	2026/05/19
dc.date.issued	2026
dc.identifier.other	JCRGEI.CRENG-966.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311263
dc.description.abstract	Abstract Temperatures in the pavement layers are critical for preserving pavement structural integrity especially during freeze and thaw cycles. Better prediction of these temperatures, based on factors such as air temperature, incoming solar radiation, ...
dc.publisher	American Society of Civil Engineers
dc.title	Predictive Temperature Modeling in Granular Base and Subgrade Layers Using Machine Learning: A Smart Road Study in Edmonton, Alberta, Canada
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	3
dc.journal.title	Journal of Cold Regions Engineering
dc.identifier.doi	10.1061/JCRGEI.CRENG-966
dc.journal.fristpage	04026032-1
dc.journal.lastpage	04026032-11
dc.page	11
dc.tree	Journal of Cold Regions Engineering:;2026:;Volume ( 040 ):;issue: 003
dc.contenttype	Fulltext