Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network

Reinforced concrete beam; Shear strength; Artificial neural network; Adaptive neuro-fuzzy inference system; Iranian concrete institute code; American concrete institute code. Abstract In this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict the shear strength of Reinforced Concrete (RC) beams, and the models are compared with American Concrete Institute (ACI) and Iranian Concrete Institute (ICI) empirical codes. The ANN model, with Multi-Layer Perceptron (MLP), using a Back-Propagation (BP) algorithm, is used to predict the shear strength of RC beams. Six important parameters are selected as input parameters including: concrete compressive strength, longitudinal reinforcement volume, shear span-to-depth ratio, transverse reinforcement, effective depth of the beam and beam width. The ANFIS model is also applied to a database and results are compared with the ANN model and empirical codes. The first-order Sugeno fuzzy is used because the consequent part of the Fuzzy Inference System (FIS) is linear and the parameters can be estimated by a simple least squares error method. Comparison between the models and the empirical formulas shows that the ANN model with the MLP/BP algorithm provides better prediction for shear strength. In adition, ANN and ANFIS models are more accurate than ICI and ACI empirical codes in prediction of RC beams shear strength. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.