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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.accessioned2008-11-04T16:56:59Z
dc.date.available2008-11-04T16:56:59Z
dc.date.issued2008
dc.identifier.citationYang, K.H., Ashour, A.F., Song, J.K. and Lee, E.T. (2008). Neural network modelling of RC deep beam shear strength. Proceedings of the Institution of Civil Engineers: Structures and Buildings. Vol. 161, No. 1, pp. 29-39.en
dc.identifier.urihttp://hdl.handle.net/10454/865
dc.descriptionYesen
dc.description.abstractA 9 x 18 x 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
dc.language.isoenen
dc.relation.isreferencedbyhttp://www.extenza-eps.com/doi/pdf/10.1680/stbu.2008.161.1.29en
dc.rights© 2008 Thomas Telford Ltd. Reproduced in accordance with the publisher's self-archiving policy.en
dc.subjectNeural Network Modellingen
dc.subjectReinforce Concreteen
dc.subjectDeep Beamen
dc.subjectShear Strengthen
dc.titleNeural network modelling of RC deep beam shear strengthen
dc.status.refereedYesen
dc.typeArticleen
refterms.dateFOA2018-07-17T21:56:26Z


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