Finite element, analytical, artificial neural network models for carbon fibre reinforced polymer confined concrete filled steel columns with elliptical cross sections

In the present era of architecture, different cross-sectional shapes structural concrete elements have been utilized. However, this change in shape has a significant effect on load-carrying capacity. To restore this, use column confinements with elliptical sections gained attention. This paper aim to investigate confined reinforced Carbon Fiber Reinforced Polymer (CFRP) and steel tube axial study is achieved using following tools Finite Element (FE) Abaqus Artificial Neural Networks (ANN) modeling. The involved 500-mm-high three sets aspect ratios: 1.0, 1.5, 2.0. each ratio, layers CFRP were used, i.e., .167, .334, .501-mm. Analytical results showed that increase ratio from 1 2, there decrease ultimate load about 23.2% average. addition, combined confining pressure increases dilation angle as number increases. failure mode for large local buckling at its mid-height along minor axis. result good correlation between FE experimental stress strains, mean squared error 2.27 .001, respectively. Moreover, ANN analytical models delightful R 2 .97 .88 strain models, section tubes can be adopted new architectural type construction; however, more than ratios, wrapping jackets highly recommended.