An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks

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dc.contributor.authorTanarslan H.M.
dc.contributor.authorSecer M.
dc.contributor.authorKumanlioglu A.
dc.date.accessioned2024-07-22T08:19:29Z
dc.date.available2024-07-22T08:19:29Z
dc.date.issued2012
dc.description.abstractAn artificial neural network model is developed to predict the shear capacity of reinforced concrete (RC) beams, retrofitted in shear by means of externally bonded wrapped and U-jacketed fiber-reinforced polymer (FRP) in this study. However, unlike the existing design codes the model considers the effect of strengthening configurations dissimilarity. In addition model also considers the effect of shear span-to-depth ratio (a/d) ratio at the ultimate state. It is also aimed to develop an efficient and practical artificial neural network (ANN) model. Therefore, mechanical properties of strengthening material and mechanical and dimensional properties of beams are selected as inputs. ANN model is trained, validated and tested using the literature of 84 RC beams. Then neural network results are compared with those 'theoretical' predictions calculated directly from International Federation for Structural Concrete (fib14), the American guideline (ACI 440.2R), the Australian guideline (CIDAR), the Italian National Research Council (CNR-DT 200) and Canadian guideline (CHBDC) for verification. Performed analysis showed that the neural network model is more accurate than the guideline equations with respect to the experimental results and can be applied satisfactorily within the range of parameters covered in this study. © 2011 Elsevier Ltd. All rights reserved.
dc.identifier.DOI-ID10.1016/j.conbuildmat.2011.12.008
dc.identifier.issn09500618
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/17682
dc.language.isoEnglish
dc.subjectBeams and girders
dc.subjectMechanical properties
dc.subjectReinforced concrete
dc.subjectStrengthening (metal)
dc.subjectArtificial neural network models
dc.subjectDesign codes
dc.subjectDimensional properties
dc.subjectExternally bonded
dc.subjectFiber reinforced polymers
dc.subjectFRP
dc.subjectFRP reinforcement
dc.subjectInternational federation
dc.subjectNational Research Council
dc.subjectNeural network model
dc.subjectRC beams
dc.subjectReinforced concrete beams
dc.subjectShear capacity
dc.subjectShear strengthening
dc.subjectSpan-to-depth ratio
dc.subjectStrengthening materials
dc.subjectStructural concretes
dc.subjectNeural networks
dc.titleAn approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks
dc.typeArticle

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