Integrated Machine Learning Using Autoencoder and Multilayer Perceptron Approaches to Predict Stress–Strain Curves of Frozen Soil–Rock Mixtures

dc.contributor.author	Sun, Kai
dc.contributor.author	Li, Zhiqing
dc.contributor.author	Wang, Haosen
dc.contributor.author	Kong, Youxing
dc.contributor.author	Wang, Shuangjiao
dc.contributor.author	Zhou, Yingxin
dc.contributor.author	Hu, Ruilin
dc.date.accessioned	2026-08-20T11:08:58Z
dc.date.available	2026-08-20T11:08:58Z
dc.date.copyright	2025/08/13
dc.date.issued	2025
dc.identifier.other	IJGNAI.GMENG-10984.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311759
dc.description.abstract	Abstract Machine learning is extensively used in constitutive modeling of geomaterials and is critical for enhancing computational analyses in geotechnical engineering. In the area of geomechanical modeling, the primary application of machine learning ...
dc.publisher	American Society of Civil Engineers
dc.title	Integrated Machine Learning Using Autoencoder and Multilayer Perceptron Approaches to Predict Stress–Strain Curves of Frozen Soil–Rock Mixtures
dc.type	Journal Article
dc.journal.volume	25
dc.journal.issue	10
dc.journal.title	International Journal of Geomechanics
dc.identifier.doi	10.1061/IJGNAI.GMENG-10984
dc.journal.fristpage	04025229-1
dc.journal.lastpage	04025229-15
dc.page	15
dc.tree	International Journal of Geomechanics:;2025:;Volume ( 025 ):;issue: 010
dc.contenttype	Fulltext