Interpretable machine learning for predicting the bearing capacity of double shear-bolted connections: a data-driven evaluation

Abstract

Introduction: Accurate prediction of the bearing capacity of double shear-bolted connections in structural steel is essential for ensuring safety and efficiency in structural design. This study explores the application of ten machine learning algorithms to enhance prediction accuracy while addressing the interpretability challenges often associated with such models. Methods: Models were tuned with 10-fold crossvalidation and assessed using RMSE, R2 and a20 accuracy index. A comprehensive sensitivity analysis evaluates the influence of input parameters, while advanced interpretability techniques, such as partial dependence plots, accumulated local effects, and Shapley additive explanations, are employed alongside parametric studies to elucidate the decision-making processes of the models. Results: These methods facilitate the identification of critical variables that influence bearing capacity predictions at both local and global scales. Discussion: The study demonstrates that machine learning can be a trustworthy and data-driven complement to conventional mechanics-based approaches, when coupled with rigorous interpretability, advancing both safety and efficiency in steelconnection design. The findings highlight the potential of interpretable machine learning approaches to not only improve predictive precision but also provide actionable insights into complex model behaviours, ultimately advancing structural engineering practices and promoting data-driven design methodologies.