Compressive Strength Prediction by ANN Formulation Approach for FRP Confined Rectangular Concrete Columns

Enhancement of strength and ductility is the main reason for the extensive use of FRP (fiber reinforced polymer) jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of FRP confined rectangular concrete columns by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. The results of training, validation and testing sets of the model are compared with experimental results. All of the results show that ANN model is fairly promising approach for the prediction of compressive strength of FRP confined rectangular concrete columns. The performance of the ANN model is also compared with different proposed formulas available in the literature. It was found that the ANN model provides the most accurate results in calculating the compressive strength of FRP confined rectangular concrete columns among existing compressive strength formulas. Finally, a sensitivity analysis using Garson’s algorithm has been also developed to determine the importance of each input parameters.