Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/32962
DC FieldValueLanguage
dc.contributor.authorSpijkerman, Zelda-
dc.contributor.authorBakas, Nikolaos P.-
dc.contributor.authorMarkou, George-
dc.contributor.authorPapadrakakis, Manolis-
dc.date.accessioned2024-10-02T05:58:00Z-
dc.date.available2024-10-02T05:58:00Z-
dc.date.issued2021-01-01-
dc.identifier.citation8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineeringen_US
dc.identifier.issn26233347-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/32962-
dc.description.abstractThis paper focusses on the ongoing discussion of developing a single relationship that can accurately predict the shear capacity of slender, reinforced concrete (RC) beams without stirrups. To date, the main approach used to predict the shear capacity of RC beams, has been based on the derivation of a formula from experimental data. In this study, the approach uses the development of RC FEM models without stirrups, where the beam width is larger or equal to the section height and tested under three-point bending. The models were created and analysed by using Reconan FEA software, where the obtained results from the nonlinear analyses were used to construct a large database of 10,000 beams with varying material and geometric properties. Artificial Intelligence (AI) training was performed by using machine learning algorithms on the numerically generated database to develop predictive models and to develop an improved formula for predicting the shear capacity of RC beams without stirrups. The proposed predictive formula was validated against an available ACI database of RC beams that were assembled by using experimentally tested, physical beams without stirrups. The predictive formula was also compared with the design code formulae proposed by ACI 318-19 and Eurocode 2. According to the numerical findings of this research work, the proposed formula outperformed both design formulae demonstrating significant potential in replacing the current design approach.en_US
dc.language.isoenen_US
dc.subjectShear Strength Predictionen_US
dc.subjectArtificial Intelligence Algorithmsen_US
dc.subjectDesign Formulaeen_US
dc.subjectFinite Element Methoden_US
dc.subjectReinforced Concreteen_US
dc.titlePredicting the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithmsen_US
dc.typeConference Papersen_US
dc.collaborationUniversity of Pretoriaen_US
dc.collaborationThe Cyprus Instituteen_US
dc.collaborationNational Technical University Of Athensen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryGreeceen_US
dc.countrySouth Africaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceInternational Conference on Computational Methods in Structural Dynamics and Earthquake Engineeringen_US
dc.identifier.scopus2-s2.0-85120824530-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85120824530-
cut.common.academicyear2021-2022en_US
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.fulltextWith Fulltext-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.orcid0000-0002-1890-8792-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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