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A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

dc.contributor.authorTao, Rui
dc.contributor.authorPan, Yutao
dc.contributor.authorLiu, Zhongqiang
dc.contributor.authorLiu, Yong
dc.contributor.authorRitter, Stefan
dc.date.accessioned2023-12-21T14:28:45Z
dc.date.available2023-12-21T14:28:45Z
dc.date.created2023-11-29T08:40:05Z
dc.date.issued2023
dc.identifier.citationActa Geotechnica. 2023, 18 5959-5982.
dc.identifier.issn1861-1125
dc.identifier.urihttps://hdl.handle.net/11250/3108679
dc.language.isoeng
dc.subjectArtificial Intelligence
dc.subjectArtificial Intelligence
dc.titleA physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors
dc.title.alternativeA physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber5959-5982
dc.source.volume18
dc.source.journalActa Geotechnica
dc.identifier.doi10.1007/s11440-023-02030-z
dc.identifier.cristin2204633
dc.relation.projectNorges forskningsråd: 328767
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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