Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load

Abstract

Due to high strength and ductility, concrete filled steel tube columns have been highly regarded in recent decades and many experimental studies have been carried out in predicting the strength of these columns. Increase in compressive strength of concrete core by the lateral confinement provided by steel tube and delay of the steel local buckling by the contact with the hardened concrete are effective parameters in behavior of concrete filled steel tubes. This study presents a new approach to predict the capacity of circular concrete filled steel tube columns under axial loading condition, using a large number of experimental data by applying artificial neural networks. The effects of yield stress and wall thickness of steel tube, compressive strength of concrete and dimensions of column are examined. Proposed equation is compared with other existing models and indicates that the new model can predict the ultimate strength of axially loaded columns by a high level of precision.