Capacity prediction of cold-formed stainless steel tubular columns using machine learning methods

Abstract Current methods of designing cold-formed stainless-steel tubular columns provide a series of design formulae for columns to adapt to various grades of materials and failure modes. In this study, machine learning algorithms were used to establish a unified capacity prediction method. Test data on cold-formed stainless-steel tubular columns were collected from the literature, and Pearson correlation analysis was performed to establish the relations among the design parameters. Seven classical machine learning algorithms were introduced and developed to predict column capacity in a variety of cases used for the analysis. The results show that the random forest performs the best, followed by another gradient boosted tree-based ensemble algorithm, XGBoost. The non-dimensional slenderness of the member and of the cross-section (denoted as comprehensive parameters) are the two key parameters for capacity prediction in the current design methods, and are used as the feature input to acquire the base machine learning model. More models are evaluated by adding one or more material and geometric variables into the feature input. The analysis results show that the performance of the random forest algorithm can be significantly improved by considering the comprehensive parameters together with the member slenderness, ratio of tensile strength to yield strength, and strain hardening exponent. The predictions of the machine learning-based method were compared with those of the current design method in Eurocode. It is concluded that the proposed unified machine learning model provides more accurate results owing to the consideration of the diversities of both material properties and member failure modes.