Concrete-filled steel tubular (CFST) columns are unique structural members that capitalize on the synergy between steel and concrete materials. Due to complexities arising from the interaction between steel tube and concrete filling, the analysis and design of CFST columns are both intricate and tedious. A closer examination to the provisions of American, European and Australian/New Zealand design guidelines shows how these building codes seem to diverge on a proper methodology to design CFST columns. This leverages naturally inspired machine learning (NIML) algorithms (namely genetic algorithms and gene expression programing) to derive compact and one-stepped predictive expressions that can accurately predict the structural response of CFST columns. These expressions were developed and validated using the results of 3,103 available tests carried out on CFST columns over the past few years. The outcome of this work shows that the NIML-derived expressions have superior prediction capabilities than those in currently used design codes.

钢管混凝土（CFST）柱是利用钢和混凝土材料之间协同作用的独特结构构件。由于钢管与混凝土之间相互作用的复杂性，CFST柱的分析和设计既复杂又乏味。仔细研究美国，欧洲和澳大利亚/新西兰的设计准则，可以发现这些建筑规范似乎在设计CFST色谱柱的正确方法上有所不同。这利用了自然启发式机器学习（NIML）算法（即遗传算法和基因表达编程）来生成紧凑，一步式的预测表达式，可以准确地预测CFST色谱柱的结构响应。这些表达式是根据3的结果进行开发和验证的，过去几年，在CFST色谱柱上进行了103种可用的测试。这项工作的结果表明，NIML派生的表达式比当前使用的设计代码具有更好的预测能力。