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dc.contributor.authorLiu, Mingpeng
dc.contributor.authorLu, Dechun
dc.contributor.authorShiau, Jim
dc.contributor.authorLai, Fengwen
dc.contributor.authorHuang, Ming
dc.contributor.authorZhou, Xin
dc.date.accessioned2026-08-20T21:11:21Z
dc.date.available2026-08-20T21:11:21Z
dc.date.copyright2026/06/05
dc.date.issued2026
dc.identifier.otherIJGNAI.GMENG-13743.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314085
dc.description.abstractAbstract Numerical modeling is an effective approach to investigating deformation responses caused by deep excavations; however, it often shows discrepancies when compared with field measurements. To this end, this study develops a multifidelity neural ...
dc.publisherAmerican Society of Civil Engineers
dc.titleA Multifidelity Neural Network Model for Predicting Deformation in Deep Excavations and Nearby Existing Tunnels
dc.typeJournal Article
dc.journal.volume26
dc.journal.issue8
dc.journal.titleInternational Journal of Geomechanics
dc.identifier.doi10.1061/IJGNAI.GMENG-13743
dc.journal.fristpage04026153-1
dc.journal.lastpage04026153-16
dc.page16
dc.treeInternational Journal of Geomechanics:;2026:;Volume ( 026 ):;issue: 008
dc.contenttypeFulltext


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