Artificial neural network for predicting the flexura bond strength of FRP bars in concrete

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dc.contributor.authorKoroglu, Mehmet Alpaslan
dc.date.accessioned2024-02-23T14:31:54Z
dc.date.available2024-02-23T14:31:54Z
dc.date.issued2019
dc.departmentNEÜen_US
dc.description.abstractThe bond strength between fibre-reinforced polymer (FRP) rebars and concrete is one of the most significant aspects of composite behaviour for rebars and concrete. In this study, a database of 408 beam type specimens consisting of beam end specimens, beam anchorage specimens and splice beam specimens was compiled from the current literature and used to develop a simple prediction using the artificial neural network (ANN). The data used for modelling were organised in a format of eight input parameters that include FRP type, cover bar surface, confinement, bar diameter (d(b)), concrete compressive strength (root f(c)), minimum cover-to-bar-diameter ratio (c/d(b)), bar-development-length-to-bar-diameter ratio (l/d(b)), and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameters (A(tr)/snd(b)). Additionally, a simple prediction formula by regression analysis was developed. The root mean square error and R-2 values of the testing data were found in order to compare the results of both ANN and the proposed model with existing regulations. The new ANN model predicts the bond strength of FRP bars in reinforced concrete with 0.8989 R-2, thus yielding better results when compared with existing regulations.en_US
dc.identifier.doi10.1515/secm-2017-0155
dc.identifier.endpage29en_US
dc.identifier.issn0792-1233
dc.identifier.issn2191-0359
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85061830124en_US
dc.identifier.startpage12en_US
dc.identifier.urihttps://doi.org/10.1515/secm-2017-0155
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15401
dc.identifier.volume26en_US
dc.identifier.wosWOS:000458802000002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter De Gruyter Gmbhen_US
dc.relation.ispartofScience And Engineering Of Composite Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnnen_US
dc.subjectBond Strengthen_US
dc.subjectFrp Barsen_US
dc.titleArtificial neural network for predicting the flexura bond strength of FRP bars in concreteen_US
dc.typeArticleen_US

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