Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33019
DC FieldValueLanguage
dc.contributor.authorBraun, Kevin T.-
dc.contributor.authorMarkou, George-
dc.contributor.authorJacobsz, S. W.-
dc.contributor.authorCalitz, Duan-
dc.date.accessioned2024-10-02T11:03:59Z-
dc.date.available2024-10-02T11:03:59Z-
dc.date.issued2024-06-01-
dc.identifier.citationStructures, 2024, vol.64en_US
dc.identifier.issn23520124-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33019-
dc.description.abstractThe design of pile foundations that are expected to develop significant lateral loading is a complex procedure that requires the development of objective and accurate design formulae that will not be based on semi-empirical know-how. For this reason, the main objective of this research work is to develop predictive models that will be able to compute the overall mechanical response of reinforced concrete (RC) piles embedded in unsaturated clay. To achieve this goal, experimental data, and advanced nonlinear 3D detailed finite element (FE) modelling were used to construct datasets comprising multiple results related to the ultimate capacity and corresponding horizontal deformation of RC piles that are loaded horizontally until failure. In total, three datasets were developed and then used to train and test predictive models through the use of various machine learning (ML) algorithms. After successfully developing various predictive models, an out-of-sample dataset was developed and used to further validate the accuracy and extendibility of the predictive models. Finally, the most accurate ML-generated predictive model was used to predict the mechanical response of a RC pile embedded in unsaturated clay that was experimentally tested. The ability of the proposed predictive model is demonstrated through this pilot research work.en_US
dc.language.isoenen_US
dc.relation.ispartofStructuresen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSoil-structure interactionen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectPredictive modelsen_US
dc.subjectReinforced concrete pileen_US
dc.subjectHorizontal load-displacement responseen_US
dc.titleDeveloping predictive models for the load-displacement response of laterally loaded reinforced concrete piles in stiff unsaturated clay using machine learning algorithmsen_US
dc.typeArticleen_US
dc.collaborationUniversity of Pretoriaen_US
dc.collaborationNeapolis University Pafosen_US
dc.collaborationSRK Consultingen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.subject.categoryENGINEERING AND TECHNOLOGYen_US
dc.subject.categoryCivil Engineeringen_US
dc.subject.categoryOther Engineering and Technologiesen_US
dc.journalsOpen Accessen_US
dc.countrySouth Africaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.istruc.2024.106532en_US
dc.identifier.scopus2-s2.0-85193046284-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85193046284-
dc.relation.volume64en_US
cut.common.academicyearemptyen_US
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextWith Fulltext-
crisitem.journal.journalissn2352-0124-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.parentorgFaculty of Engineering and Technology-
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This item is licensed under a Creative Commons License Creative Commons