ANN Model for Predicting the Compressive Strength of Circular Steel-Confined Concrete

Concrete filled steel tube is constructed using various tube shapes to obtain most efficient properties of concrete core and steel tube. The compressive strength of concrete is considerably increased by the lateral confined steel tube in circular concrete filled steel tube (CCFT). The aim of this study is to present an integrated approach for predicting the steel-confined compressive strength of concrete in CCFT columns under axial loading based on large number of experimental data using artificial neural networks. Neural networks process information in a similar way that the human brain does. Neural networks learn by example. The main parameters investigated in this study include the compressive strength of unconfined concrete (f(c)(')), outer diameter (D) and length (L) of column, wall thickness (t) and tensile yield stress (F-y) of steel tube. Subsequently, using the idealized network, empirical equations are developed for the confinement effect. The results of proposed model are compared with those of existing models on the basis of the experimental results. The findings indicate the precision and efficiency of ANN model for predicting the capacity of CCFT columns.