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

In this paper, the artificial neural network (ann) and the adaptive neuro-fuzzy inferencesystem (anfis) are used to predict the shear strength of reinforced concrete (rc) beams, and the modelsare 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 usedto predict the shear strength of rc beams. six important parameters are selected as input parametersincluding: 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 appliedto a database and results are compared with the ann model and empirical codes. the first-order sugenofuzzy is used because the consequent part of the fuzzy inference system (fis) is linear and the parameterscan be estimated by a simple least squares error method. comparison between the models and theempirical formulas shows that the ann model with the mlp/bp algorithm provides better prediction forshear strength. in adition, ann and anfis models are more accurate than ici and aci empirical codes inprediction of rc beams shear strength.