| contributor author | Bi, Jun | |
| contributor author | Pan, Yuxuan | |
| contributor author | Mu, Wenxuan | |
| contributor author | Yang, Sheng | |
| contributor author | Wang, Guoxu | |
| contributor author | Mao, Mengyao | |
| contributor author | Wang, Shengnian | |
| contributor author | Wei, Tingting | |
| date accessioned | 2026-08-20T10:47:44Z | |
| date available | 2026-08-20T10:47:44Z | |
| date copyright | 2025/11/12 | |
| date issued | 2026 | |
| identifier other | JCRGEI.CRENG-991.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311267 | |
| 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Predicting the Unfrozen Water Content of Freezing Soils Using an Artificial Neural Network Model | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 1 | |
| journal title | Journal of Cold Regions Engineering | |
| identifier doi | 10.1061/JCRGEI.CRENG-991 | |
| journal fristpage | 04025050-1 | |
| journal lastpage | 04025050-15 | |
| page | 15 | |
| tree | Journal of Cold Regions Engineering:;2026:;Volume ( 040 ):;issue: 001 | |
| contenttype | Fulltext | |
