Data-driven analysis of concrete-filled steel-tube CFRP-confined NSC columns

The main objective of the present investigation is to propose the finite element model (FEM), empirical model, and artificial neural networks (ANN) model for predicting the axial strength of steel-tube concrete-filled CFRP-confined NSC (STC) columns. The FEM was proposed using improved concrete damaged plasticity that was employed for an extensive parametric investigation to examine the effect of various parameters of STC columns on their performance. The empirical and ANN models were proposed using a database of 700 and 216 specimens, respectively. The FEM, empirical, and ANN models portrayed the accuracy of 94%, 87%, and 92% for the axial strength, respectively.

Automated summary

The study proposes finite element models, empirical models, and artificial neural network models to predict the axial strength of steel-tube concrete-filled CFRP-confined NSC columns. The accuracy of the FEM, empirical, and ANN models for axial strength prediction was found to be 94%, 87%, and 92% respectively.