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Πεδίο DCΤιμήΓλώσσα
dc.contributor.advisorMarkou, George-
dc.contributor.advisorDorbani, Saida-
dc.contributor.authorYahiaoui, Asma-
dc.date.accessioned2024-11-06T06:51:36Z-
dc.date.available2024-11-06T06:51:36Z-
dc.date.issued2026-12-01-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33149-
dc.description.abstractDevelop a large dataset through the use of Reconan FEA and HPC LUMI that will be used to train and test AI algorithms for the development of predictive models. The main objective is to replace the need of performing pushover analyses on RC structures with AL generated predictive models.en_US
dc.language.isoenen_US
dc.rightsAttribution-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectReinforced Concrete Structuresen_US
dc.subjectSeismic Assessmenten_US
dc.subjectMachine Learningen_US
dc.subjectHigh Performance Computingen_US
dc.subjectFinite Element Methoden_US
dc.subjectLarge Datasetsen_US
dc.titleDevelopment of Predictive Models through ML Algorithms for the Computation of the Mechanical Behaviour of RC Structures under Pushover Loading Conditionsen_US
dc.typePhD Thesisen_US
dc.affiliationUniversity of Science and Technology Houari Boumedieneen_US
dc.relation.deptDepartment of Structures and Materials, Built Environmental Research Laboratoryen_US
dc.description.statusCurrenten_US
cut.common.academicyearemptyen_US
dc.relation.facultyCivil Engineering Facultyen_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_db06-
item.cerifentitytypePublications-
item.openairetypedoctoralThesis-
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-
Εμφανίζεται στις συλλογές:Διδακτορικές Διατριβές/ PhD Theses
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