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dc.contributor.authorYang, Keun-Hyeok*
dc.contributor.authorAshour, Ashraf F.*
dc.contributor.authorSong, J-K.*
dc.contributor.authorLee, E-T.*
dc.date.accessioned2015-12-16T12:44:58Z
dc.date.available2015-12-16T12:44:58Z
dc.date.issued2008-02
dc.identifier.citationYang KH, Ashour AF, Song JK and Lee ET (2008) Neural Network Modelling for Shear Strength of Reinforced Concrete Deep Beams. Structures & Buildings,161(1): 29-39.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7543
dc.descriptionyesen_US
dc.description.abstractA 9 × 18 × 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1·028 and 0·154, respectively, for simple deep beams, and 1·0 and 0·122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1680/stbu.2008.161.1.29en_US
dc.rights© 2008 ICE. Reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectNeural network modelling; Reinforced Concrete; Deep beams; Shear strength predictionen_US
dc.titleNeural Network Modelling for Shear Strength of Reinforced Concrete Deep Beamsen_US
dc.status.refereedyesen_US
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-25T12:09:55Z


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