Application of ANN to the design of CFST columns

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 results are then compared with those predicted from the proposed empirical equations, American code, European code and Australian code. The comparison study shows that the ultimate strengths predicted from the proposed equations have the best agreement with the experimental results with the least mean square error (MSE). In addition, strength reduction factors for the proposed equations are derived using Monte Carlo simulation (MCS). The use of the proposed strength reduction factors will ensure that CFST columns designed by the developed ANN-based equations are safe because their reliability indices meet the target value of 3.0 required by American code or 3.8 required by European and Australian codes.