Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network

This study presents the application of combined artificial neural networks (CANNs) for the flexural capacity estimation of quadrilateral fiber-reinforced polymer (FRP) confined reinforced concrete (RC) columns. A database on quadrilateral FRP confined RC columns subjected to axial load and moment was obtained from experimental studies in the literature; CANN models were built, trained and tested. Then the flexural capacities of quadrilateral FRP confined RC columns were determined using the developed CANN model. Single and combined ANN was used for the first time in the literature for the estimation of flexural capacities of non-circular fiber-reinforced polymer (FRP) confined reinforced concrete (RC) columns. The accuracies of the proposed ANN and CANN models were more satisfactory as compared to the existing conventional approaches in the literature. Moreover, the proposed CANN models’ results had lower prediction error than those of the single ANN model.

► A CANN model was developed for non-circular FRP confined RC columns.
► The results of sixty-one column tests are taken into account during testing and training phases of CANN modeling.
► 99.52% accuracy rate was obtained.
► The proposed CANN model is successful for both FRP confined and unconfined columns.