Adaptive neuro-fuzzy inference modelling and sensitivity analysis for capacity estimation of fiber reinforced polymer-strengthened circular reinforced concrete columns

In this study, a fuzzy logic-based model for predicting the ultimate strength of FRP-confined circular reinforced concrete (RC) columns is presented. The adaptive neuro-fuzzy inference system (ANFIS) model was generated using valid experimental data with seven input variables. Input parameters were considered in such a way that all the parameters affecting the compressive strength of the column were simultaneously involved. Different models for compressive strength of fiber reinforced polymer (FRP)-confined concrete including the model in American Concrete Institute (ACI), to calculate the maximum stress endured by the column under axial load, were presented and compared with the results of the ANFIS model. Also, for similarity to other models, the ACI equation for calculating the maximum compressive strength tolerated by a column was considered without reducing coefficients as ACI-N and was compared with other models. The results obtained from the ANFIS model were compared with results from other models. ANFIS model showed the highest accuracy among all models in predicting the experimental results.