Artificial neural networks approach for prediction of axial loading capacity of circular normal strength concrete columns confined by both transverse steel reinforcement and carbon fiber reinforced polymer wraps

Abstract

A few empirical models for the axial loading capacity (ALC) of circular normal strength concrete (NSC) columns wrapped by carbon fiber reinforced polymer (CFRP) sheets and interior transverse steel reinforcement (TSR) (CSC columns) are available in the literature. The deficiency of those models is that they were proposed based on a small number of tests by considering limited parameters of CSC columns. Therefore, the main aim of the current investigation is to propose the improved empirical models for the ALC of CSC columns by including the interaction mechanism between TSR and FRP confining behavior. To secure this aim, a general regression analysis technique and artificial neural networks (NNs) on the experimental outcomes of 76 CSC columns collected from the previous investigations were employed. The proposed NN model was adjusted for the different number of hidden layers and neurons to achieve an optimized model. The suggested NN and empirical models portrayed a close agreement with the testing database with R2 = 0.998 and R2 = 0.892, respectively. The NN model reported a higher accuracy than the theoretical model. The comparative investigation solidly authenticated the superiority and accuracy of the anticipated strength models for CSC columns.