Modeling and simulation of shear resistance of R/C beams using artificial neural network

Artificial neural network (ANN) has been used in several engineering application areas including civil engineering. The use of ANN to predict the behavior of reinforced concrete (R/C) members, using the vast amount of experimental data as a test-bed for learning and verification of results, proved to be a viable method for carrying out parametric studies. This paper presents application of ANN for predicting the shear resistance of rectangular R/C beams. Six parameters that influence the shear resistance of beams, mainly shear- span-to-depth ratio, concrete strength, longitudinal reinforcement, shear reinforcement, beam depth and beam width, are used as input for the ANN. A back propagation neural network (BPNN) with different activation functions is used and their results are compared. The sigmoid function with variable threshold is adopted due to its accuracy of prediction. The ANN prediction and the measured experimental values are compared with the shear strength predictions of ACI318-02 and BS8110 codes. A sensitivity study of the parameters that affect shear strength of R/C beams is carried out and the underlying complex nonlinear relationships among these parameters were investigated. Shear response curves and surfaces based on these parameters were generated. It is concluded that ANN can predict, to a great degree of accuracy, the shear resistance of rectangular R/C beams and it is a viable tool for carrying out parametric study of shear behavior of R/C beams. (c) 2006 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.