Composite structures
In this study, a surrogate Machine Learning (ML)-based model was developed, to predict the load-bearing capacity (LBC) of concrete-filled steel square hollow section (CFSS) members, considering loading eccentricity. The proposed Artificial Neural Network (ANN) model was trained and validated against experimental data using the following error measurement criteria: coefficient of determination (R2), slope of regression, root mean square error (RMSE) and mean absolute error (MAE). A parametric study was conducted to calibrate the parameters of the ANN model, including the number of neurons, activation function, cost function and training algorithm, respectively. The results showed that the ANN model can provide reliable and effective prediction of LBC (R2 = 0.975, Slope = 0.975, RMSE = 294.424 kN and MAE = 191.878 kN). Sensitivity analysis showed that the geometric parameters of the steel tube (width and thickness) and the compressive strength of concrete were the most important variables. Finally, the effect of eccentric loading on the LBC of CFSS members is presented and discussed, showing that the ANN model can assist in the creation of continuous LBC maps, within the ranges of input variables adopted in this study.
Figure 1: Diagram of a concrete-filled steel square hollow section (CFSS) member: (a) under eccentric loading; (b) square cross section (et and eb are defined based on the Oexey coordinate system, in this example, et is positive and eb is negative, respectively); and (c) load-axial shortening curve
Figure 2: Effect of eccentric loading on the LBC of compressive CFSS members in different scenarios. The same limits are applied on the x- and y-axes