Predicting the Unfrozen Water Content of Freezing Soils Using an Artificial Neural Network Model

dc.contributor.author	Bi, Jun
dc.contributor.author	Pan, Yuxuan
dc.contributor.author	Mu, Wenxuan
dc.contributor.author	Yang, Sheng
dc.contributor.author	Wang, Guoxu
dc.contributor.author	Mao, Mengyao
dc.contributor.author	Wang, Shengnian
dc.contributor.author	Wei, Tingting
dc.date.accessioned	2026-08-20T10:47:44Z
dc.date.available	2026-08-20T10:47:44Z
dc.date.copyright	2025/11/12
dc.date.issued	2026
dc.identifier.other	JCRGEI.CRENG-991.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311267
dc.description.abstract	Abstract Unfrozen water content is one of the most prominent hydrothermal properties because it largely affects the hydraulic, physical, and mechanical behavior of frozen soils. However, the accurate estimation of unfrozen water contents under different ...
dc.publisher	American Society of Civil Engineers
dc.title	Predicting the Unfrozen Water Content of Freezing Soils Using an Artificial Neural Network Model
dc.type	Journal Article
dc.journal.volume	40
dc.journal.issue	1
dc.journal.title	Journal of Cold Regions Engineering
dc.identifier.doi	10.1061/JCRGEI.CRENG-991
dc.journal.fristpage	04025050-1
dc.journal.lastpage	04025050-15
dc.page	15
dc.tree	Journal of Cold Regions Engineering:;2026:;Volume ( 040 ):;issue: 001
dc.contenttype	Fulltext