Machine Learning–Based Settlement Models for Shallow-Founded Structures on Interbedded Liquefiable Sites Considering Dense Granular Columns

dc.contributor.author	Bessette, Caroline
dc.contributor.author	Dashti, Shideh
dc.contributor.author	Liel, Abbie B.
dc.contributor.author	Hwang, Yu-Wei
dc.date.accessioned	2026-08-20T10:56:38Z
dc.date.available	2026-08-20T10:56:38Z
dc.date.copyright	2025/09/15
dc.date.issued	2025
dc.identifier.other	JGGEFK.GTENG-13775.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311476
dc.description.abstract	AbstractIn this paper, we introduce physics-informed, machine learning (ML)–based predictive models for the permanent settlement of foundations on stratigraphically variable liquefiable deposits. In this paper, we define stratigraphic variability as two-...
dc.publisher	American Society of Civil Engineers
dc.title	Machine Learning–Based Settlement Models for Shallow-Founded Structures on Interbedded Liquefiable Sites Considering Dense Granular Columns
dc.type	Journal Article
dc.journal.volume	151
dc.journal.issue	11
dc.journal.title	Journal of Geotechnical and Geoenvironmental Engineering
dc.identifier.doi	10.1061/JGGEFK.GTENG-13775
dc.journal.fristpage	04025143-1
dc.journal.lastpage	04025143-22
dc.page	22
dc.tree	Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 011
dc.contenttype	Fulltext