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Neural network modelling of RC deep beam shear strength

By Keun-Hyeok Yang, Ashraf F. Ashour, J-K. Song and E-T. Lee

Abstract

YesA 9 x 18 x 1 feed-forward neural network (NN) model\ud trained using a resilient back-propagation algorithm and\ud early stopping technique is constructed to predict the\ud shear strength of deep reinforced concrete beams. The\ud input layer covering geometrical and material properties\ud 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\ud achieved using a comprehensive database compiled from\ud 362 simple and 71 continuous deep beam specimens.\ud The shear strength predictions of deep beams obtained\ud from the developed NN are in better agreement with\ud test results than those determined from strut-and-tie\ud 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\ud trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations

Topics: Neural Network Modelling, Reinforce Concrete, Deep Beam, Shear Strength
Year: 2008
DOI identifier: 10.1680/stbu.2008.161.1.29
OAI identifier: oai:bradscholars.brad.ac.uk:10454/865
Provided by: Bradford Scholars

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