A data-driven machine learning approach for predicting axial load capacity in steel storage rack columns

In this study, we present a machine learning (ML) framework to predict the axial load-bearing capacity, (kN), of cold-formed steel structural members. The methodology emphasizes robust model selection and interpretability, addressing the limitations of traditional analytical approaches in capturing the nonlinearities and geometrical complexities inherent to buckling behavior. The dataset, comprising key geometric and mechanical parameters of steel columns, was curated with appropriate preprocessing steps including removal of non-informative identifiers and imputation of missing values. A comprehensive suite of regression algorithms—ranging from linear models to kernel-based regressors and ensemble tree methods—was evaluated. Among these, Gradient Boosting Regression exhibited superior predictive performance across multiple metrics, including the coefficient of determination (R 2 ), root mean squared error (RMSE), and mean absolute error (MAE), and was consequently selected as the final model. Model interpretability was addressed using SHapley Additive exPlanations (SHAP), enabling insight into the relative importance and interaction of input features influencing the predicted axial capacity. To facilitate practical deployment, the model was integrated into an interactive, Python-based web interface via Streamlit. This tool allows end-users—such as structural engineers and designers—to input design parameters manually or through CSV upload, and to obtain real-time predictions of axial load capacity without the need for programming expertise. Applied to the context of steel storage rack columns, the framework demonstrates how data-driven tools can enhance design safety, streamline validation workflows, and inform decision-making in structural applications where buckling is a critical failure mode.