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Enhancing Empirical Predictions from CPTU Data Using a Hierarchical Bayesian Framework with Adaptive Model Weighting

dc.contributor.authorXiong, Haibin
dc.contributor.authorDai, Shaoheng
dc.contributor.authorShan, Feng
dc.contributor.authorHe, Xuzhen
dc.contributor.authorTong, Chenxi
dc.contributor.authorZhang, Sheng
dc.contributor.authorSheng, Daichao
dc.date.accessioned2026-08-20T10:59:06Z
dc.date.available2026-08-20T10:59:06Z
dc.date.copyright2026/06/01
dc.date.issued2026
dc.identifier.otherJGGEFK.GTENG-14713.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311533
dc.description.abstractAbstractEmpirical correlations are extensively employed in geotechnical engineering for their practical simplicity, yet their deterministic nature and neglect of inherent uncertainties often constrain their predictive reliability. This study proposes a ...
dc.publisherAmerican Society of Civil Engineers
dc.titleEnhancing Empirical Predictions from CPTU Data Using a Hierarchical Bayesian Framework with Adaptive Model Weighting
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue8
dc.journal.titleJournal of Geotechnical and Geoenvironmental Engineering
dc.identifier.doi10.1061/JGGEFK.GTENG-14713
dc.journal.fristpage04026058-1
dc.journal.lastpage04026058-19
dc.page19
dc.treeJournal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 008
dc.contenttypeFulltext


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