Artificial intelligence model for predicting the load-bearing capacity of eccentrically compressed short concrete filled steel tubular columns

Abstract

The purpose of this work is to develop the artificial neural network (ANN) model to determine the load-bearing capacity of concrete filled steel tubular (CFST) columns of circular cross-section in a wide range of input parameters. Short columns are considered for which deflections do not lead to a significant increase in the eccentricity of the axial force. The input parameters of the artificial neural network are the outer diameter of the pipe, the wall thickness, the yield strength of steel, the compressive strength of concrete, and the relative eccentricity of the axial force. The artificial neural network is trained on the synthetic data. For training, the dataset of 179,025 numerical experiments with different values of input parameters was generated. Numerical experiments were carried out using the finite element method in a simplified formulation, which makes it possible to reduce the three-dimensional problem of determining the stress-strain state of a CFST column to a two-dimensional problem. The results of testing the developed model on the data from full-scale experiments are presented.