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contributor authorRateria, Gunjan
contributor authorMaurer, M.ASCE, Brett W.
date accessioned2026-08-20T10:58:47Z
date available2026-08-20T10:58:47Z
date copyright2026/05/22
date issued2026
identifier otherJGGEFK.GTENG-14428.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311524
description abstractAbstractVolume-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 ...
publisherAmerican Society of Civil Engineers
titleA Cone-Penetration-Test Inversion Model Developed via Machine Learning of Physical and Virtual Calibration-Chamber Experiments
typeJournal Article
journal volume152
journal issue8
journal titleJournal of Geotechnical and Geoenvironmental Engineering
identifier doi10.1061/JGGEFK.GTENG-14428
journal fristpage04026050-1
journal lastpage04026050-15
page15
treeJournal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 008
contenttypeFulltext


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