Artificial neural network assisted bearing capacity and confining pressure prediction for rectangular concrete-filled steel tube (CFT)

Abstract

The evaluation of the confining pressure between steel and concrete is a complex yet important issue for concrete-filled steel tube (CFT) columns. The previous study is mainly based on the finite element model (FEM) because the confining pressure cannot be measured directly in the tests. Besides, the proposed approaches to calculating the bearing capacity of CFT columns and confining pressure varied significantly as a result of the different simplification. The neural network can simulate the complex problems quickly and accurately based on the proper training and validation. This study investigates the bearing capacity of CFT columns and the confining pressure of concrete based on a newly established experimental database and the artificial neural network (ANN) models. The results can be obtained from five primary parameters of rectangular CFT columns using the ANN models. The obtained bearing capacity and confining pressure from ANN models is compared and validated with that presented in the previous literatures. The parametric studies are performed to evaluate the bearing capacity and confining pressure with the variations of the dimensions and the material strength. The result could contribute to evaluate the interaction between the steel tube and infill concrete and its effect on the bearing capacity of rectangular CFT column.