Neural network-based models versus empirical models for the prediction of axial load-carrying capacities of FRP-reinforced circular concrete columns
Publication Name
Structural Concrete
Abstract
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 are more accurate than the empirical models with 52% and 42% reduction in mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively involved in the empirical models. Moreover, within NN-based prediction models, the prediction accuracy of DNN model is comparatively higher than the CNN model due to the integration of neurons in each layer (9-64-64-64-64-1) and embedded rectified linear unit (ReLu) activation function. Overall, the proposed DNN and CNN models can be utilized as paramount in the future studies.
Open Access Status
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