Optimization of ML and AI systems

Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P_u) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P_u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN–IWO model and improve its prediction performance. The results showed that the FNN–IWO algorithm is an excellent predictor of P_u, with a value of R² of up to 0.979. The advantage of FNN–IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R², respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P_u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.