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

Abstract

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.

Keywords:

• Finite element analysis
• steel-tube
• confinement
• damaged plastic model
• strength model

Acknowledgments

The authors acknowledge the Deanship of Scientific Research for providing administrative and financial support. Funding for this work has been provided by the Deanship of Scientific Research, King Khalid University, Ministry of Education, Kingdom of Saudi Arabia, under research grant award number R.G.P. 1/175/41

Conflicts of interest

The author declares no conflict of interest.

Additional information

Funding

The authors acknowledge the Deanship of Scientific Research for providing administrative and financial support. Funding for this work has been provided by the Deanship of Scientific Research, King Khalid University, Ministry of Education, Kingdom of Saudi Arabia, under research grant award number R.G.P. 1/175/41