Concrete-filled steel tube (CFT) are widely used as critical members for various types of structures such as bridges, high-rise buildings etc. However, there is a lack of proper models in standards to calculate the capacity of CFT members especially for high strength steel and concrete. This leads to various experiments and simulations conducted and provided in literature and a data-driven is a potential candidate with such plenty of data. The developed model used Artificial Neural Network, ANN, and this model well performed on the test set with R² is up to 0.9899. Consequently, the ANN model is incorporated with a novel optimization algorithm, the Balancing Composite Motion Optimization - BCMO, recently proposed by Le-Duc et al. This new algorithm is compared with other existing algorithms including: Differential Evolution, Dual Annealing and Second-harmonic generation, to observe the differences among these algorithms. The parameter study of the number of individuals and the maximum generations of the BCMO also conducted for further investigations. Finally, taking the advantage of computationally cost saving of the BCMO, the ANN is again conducted with the inputs is the length and the load applied on the short columns and the output is the objective functions. This ANN is a high accuracy model with R² is 0.9984, which aimed to provide the designer a rough prediction of the Objective function, which especially useful when the monetary unit cost of materials used is available.

钢管混凝土（CFT）被广泛用作桥梁，高层建筑等各种结构的关键构件。但是，在标准中缺乏适当的模型来计算CFT构件的能力，特别是对于高强度混凝土而言。强度钢和混凝土。这导致进行了各种实验和模拟，并在文献中提供了这些实验和模拟，而数据驱动的海量数据潜力巨大。开发的模型使用了人工神经网络（ANN），并且该模型在带有R ²的测试集上表现良好高达0.9899。因此，ANN模型结合了一种新的优化算法，即Le-Duc等人最近提出的平衡复合运动优化-BCMO。将该新算法与其他现有算法（包括差分进化，对偶退火和二次谐波生成）进行比较，以观察这些算法之间的差异。还对BCMO的个体数量和最大世代进行了参数研究，以供进一步研究。最后，利用BCMO的计算成本节省优势，再次进行ANN，输入为长度，短列上的负载为输出，输出为目标函数。该ANN是具有R ^2的高精度模型 为0.9984，旨在为设计人员提供对目标函数的粗略预测，当可以使用所用材料的货币单位成本时，该功能尤其有用。