This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical application, an explicit equation and an Excel-based Graphical User Interface were derived based on the ANN model. The graphical user interface is provided freely for all interested users, to support the design, teaching, and interpretation of the axial behavior of CFST columns.

这项工作旨在开发一种新颖实用的方程，用于基于软计算技术预测矩形钢管混凝土 (CFST) 柱的轴向载荷。更准确地说，首先从现有文献中收集了一个包含 880 个实验测试的数据集，用于开发人工神经网络 (ANN) 模型。进行优化策略以获得最终的 ANN 架构及其权重和偏差参数。然后将开发的 ANN 的性能与当前代码（AS、EN、AIJ、ACI、AISC、LRFD 和 DBJ）和现有的经验方程进行比较。在预测矩形 CFST 柱的轴向荷载时，发现本模型的准确性优于其他人获得的结果。对于实际应用，基于 ANN 模型推导出显式方程和基于 Excel 的图形用户界面。图形用户界面免费提供给所有感兴趣的用户，以支持 CFST 柱的轴向行为的设计、教学和解释。