| description abstract | Predicting the axial capacity and behavior of concentrically, eccentrically, and slender loaded fiber-reinforced polymer (FRP)-RC columns is not completely established, and the current design codes lack design provisions for FRP-RC columns. Rather, it requires ignoring the contribution of FRP bars in compression conservatively. To bridge this knowledge gap, this study proposes an artificial neural network (ANN)-based model capable of predicting the axial capacity and slenderness limit and constructing an interaction diagram for FRP-reinforced columns. The aforementioned model was trained with Bayesian regularization utilizing a comprehensive database of 241 tested FRP-RC columns. Parameters included in the model are column cross-sectional area, compressive strength, FRP elastic modulus, reinforcement ratio, eccentricity ratio, and slenderness ratio. The predictions of the ANN-based model match well with the experimental results of the compiled database; the model predictions have a COV of 15% and root-mean square error of 130 kN. In addition, a parametric study was conducted to investigate the effect of parameters and ensure the generalizability of the proposed model. | |
