1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33043
DC FieldValueLanguage
dc.contributor.authorAlHamaydeh, Mohammad-
dc.contributor.authorMarkou, George-
dc.contributor.authorBakas, Nikolas-
dc.contributor.authorPapadrakakis, Manolis-
dc.date.accessioned2024-10-09T05:56:23Z-
dc.date.available2024-10-09T05:56:23Z-
dc.date.issued2022-08-01-
dc.identifier.citationEngineering Structures, 2022, vol.264en_US
dc.identifier.issn01410296-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33043-
dc.description.abstractThe presented work utilizes Artificial Intelligence (AI) algorithms, to model and interpret the behavior of the fiber reinforced polymer (FRP)-reinforced concrete deep beams without stirrups. This is done by first running an extensive nonlinear finite element analysis (NLFEA) investigation, spanning across the practical ranges of the different input parameters. The FEA modeling is meticulously validated against published experimental results. A total of 93 different models representing a multitude of possible FRP-reinforced deep beam designs are rigorously analyzed. The results are then utilized in building an AI-model that describes the shear capacity for FRP-reinforced deep beams. The study investigates the effect of several factors on the shear capacity and identifies the vital parameters to be used for further model development. Additionally, the developed AI-model is benchmarked against several design standards for blind predictions on new unseen data and design codes, namely: the EC, ACI 440.1R-15, and the modified ACI 440.1R-15 (for size effect). The AI-model demonstrated superior generalization on the blind prediction dataset in comparison to the design codes.en_US
dc.language.isoenen_US
dc.relation.ispartofEngineering Structuresen_US
dc.subjectNonlinear FEAen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectFRPen_US
dc.subjectDeep Beams without Stirrupsen_US
dc.titleAI-based shear capacity of FRP-reinforced concrete deep beams without stirrupsen_US
dc.typeArticleen_US
dc.collaborationUniversity of Pretoriaen_US
dc.collaborationAmerican University of Sharjahen_US
dc.collaborationThe Cyprus Instituteen_US
dc.collaborationNational Technical University Of Athensen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.subject.categoryENGINEERING AND TECHNOLOGYen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryGreeceen_US
dc.countrySouth Africaen_US
dc.countryUnited Arab Emiratesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.engstruct.2022.114441en_US
dc.identifier.scopus2-s2.0-85131041957-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85131041957-
dc.relation.volume264en_US
cut.common.academicyearemptyen_US
item.grantfulltextnone-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-5004-0778-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.orcid0000-0002-1890-8792-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.journal.journalissn0141-0296-
crisitem.journal.publisherElsevier-
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