Force-displacement relation for lumped plasticity model of compact square concrete-filled steel tube columns

Abstract

Integration of steel and concrete benefits in square concrete-filled tube columns (SCFTs) makes them be utilized extensively worldwide. To evaluate the seismic behavior of special moment frames having SCFT columns, an accurate macro-modeling tool with little complexity is necessary. Although the fiber section can consider both linear and nonlinear behavior of SCFT, it fails to allow for deterioration, which is vital in assessing a seismic resisting system. This study proposes a new deterioration tool based on the Ibarra-Krawinkler model for compact SCFT columns using artificial neural networks (ANNs) through numerical simulation. Initially, 96 specimens with variations in geometry, material, and axial loading conditions were simulated in ABAQUS and then were analyzed using displacement control loading protocol to obtain hysteretic curves, which were used to prepare the data sets for training, testing, and validating the neural networks. Finally, the performance of ANN models was evaluated by statistical relations such as regression and mean square error. Through FEMA 356 and Ibarra-Krawinkler approaches, parameters defining linear and nonlinear phases of SCFTs behavior were derived. By employing efficiently trained neural networks, equations predicting the parameters of the deterioration model were established, and the obtained results show the accuracy and efficiency of the proposed model.

Keywords

• Deterioration model
• Square concrete-filled tube column (SCFT)
• Artificial neural network (ANN)
• Seismic behavior
• Finite element method (FEM)