A Cone-Penetration-Test Inversion Model Developed via Machine Learning of Physical and Virtual Calibration-Chamber Experiments

dc.contributor.author	Rateria, Gunjan
dc.contributor.author	Maurer, M.ASCE, Brett W.
dc.date.accessioned	2026-08-20T10:58:47Z
dc.date.available	2026-08-20T10:58:47Z
dc.date.copyright	2026/05/22
dc.date.issued	2026
dc.identifier.other	JGGEFK.GTENG-14428.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311524
dc.description.abstract	AbstractVolume-averaging effects significantly influence the accuracy of cone-penetration-test (CPT) measurements in layered soil profiles. This study presents a machine learning (ML)–based inversion model designed to correct these effects, leveraging a ...
dc.publisher	American Society of Civil Engineers
dc.title	A Cone-Penetration-Test Inversion Model Developed via Machine Learning of Physical and Virtual Calibration-Chamber Experiments
dc.type	Journal Article
dc.journal.volume	152
dc.journal.issue	8
dc.journal.title	Journal of Geotechnical and Geoenvironmental Engineering
dc.identifier.doi	10.1061/JGGEFK.GTENG-14428
dc.journal.fristpage	04026050-1
dc.journal.lastpage	04026050-15
dc.page	15
dc.tree	Journal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 008
dc.contenttype	Fulltext