The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column.

Since laboratory tests are usually costly, simulating methods using computers are always under the spotlight. This study performed a finite element analysis (FEA) using iterative solutions for simulating circular and square concrete-filled steel tube (CFST) columns infilled with high-strength concrete and reinforced with a cross-shaped plate (comprising two plates along the columns that divide the hollow columns into four equal sections) with and without opening. For this reason and for validation purposes, the columns had length of 900 mm, width/diameter of 150 mm and wall thickness of 3 mm. In this study, unlike in some other studies, the cross-shaped plate was assumed to be fixed at the top and the bottom of a column, and the columns were subjected to axial compression pointed in the center. The outcomes revealed that the cross-shaped plate could improve the axial strength of both circular and square CFST columns; however, the structural performance of the square CFST columns changed: local outward buckling was observed after inserting the cross-shaped plate. By inserting an opening on the cross-shaped plate, the bearing capacity of the circular CFST columns was further improved, while the square CFST columns experienced a decline in their ultimate bearing capacity compared with the corresponding models without the opening. The lateral deflection also improved for the circular CFST columns by adding the reinforcement. However, for the square CFST columns, while it initially improved, increasing the thickness of the cross-shaped plate inversely influenced the lateral deflection of the square CFST columns. The results were also compared with some available codes, and a good agreement was achieved with those outcomes.

During design and construction of buildings, the employed materials can substantially impact the structures’ performance. In composite columns, the properties and performance of concrete and steel have a significant influence on the behavior of structure under various loading conditions. In this study, two metaheuristic algorithms, particle swarm optimization (PSO) and competitive imperialism algorithm (ICA), were combined with the artificial neural network (ANN) model to predict the bearing capacity of the square concrete-filled steel tube (SCFST) columns. To achieve this objective and investigate the performance of optimization algorithms on the ANN, one of the most extensive datasets of pure SCFST columns (with 149 data samples) was used in the modeling process. In-depth and detailed predictive modeling of metaheuristic-based models was conducted through several parametric investigations, and the optimum factors were designed. Furthermore, the capability of these hybrid models was assessed using robust statistical matrices. The results indicated that PSO is stronger than ICA in finding optimum weights and biases of ANN in predicting the bearing capacity of the SCFST columns. Therefore, each column and its bearing capacity can be well-predicted using the developed metaheuristic-based ANN model.