Application of ANN to the design of CFST columns

Abstract

In this paper, artificial neural network (ANN) is used to predict the ultimate strength of rectangular and circular concrete-filled steel tubular (CFST) columns subjected to concentric and eccentric loading. Four comprehensive datasets are compiled and used for developing ANN-based predictive models. Empirical equations are also derived from the weights and biases of the ANNs to predict the ultimate strength of CFST columns. The proposed empirical equations can be used for both normal strength and high strength CFST columns with different section slenderness ratios (compact, non-compact and slender sections), and with different length-to-depth ratios (stub and slender columns). The test resu..