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"Neural network-based models versus empirical models for the prediction" by Shehroze Ali, Junaid Ahmad et al.

This study presents new neural-network (NN)-based models to predict the axial load-carrying capacities of fiber-reinforced polymer (FRP) bar reinforced-concrete (RC) circular columns. A database of FRP-reinforced concrete (RC) circular columns having outside diameter and height ranged between 160–305 and 640–2500 mm, respectively was established from the literature. The axial load-carrying capacities of FRP-RC columns were first predicted using the empirical models developed in the literature and then predicted using deep neural-network (DNN) and convolutional neural-network (CNN)-based models. The developed DNN and CNN models were calibrated using various neurons integrated in the hidden layers for the accurate predictions. Based on the results, the proposed DNN and CNN models accurately predicted the axial load-carrying capacities of FRP-RC circular columns with R2 = 0.943 and R2 = 0.936, respectively. Further, a comparative analysis showed that the proposed DNN and CNN models ar ....

Axial Load Carrying Capacity , Circular Concrete Columns , Convolutional Neural Network Cnn , Deep Neural Network Dnn , Empirical Models , Iber Reinforced Polymer Frp Reinforcement ,