This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
Brno University of Technology , Brno , Czechia
,
University of Coimbra , Coimbra , Portugal
University of Coimbra , Coimbra , Portugal
The component method enables efficient design of steel joints by decomposing them into individual components characterized by their resistance and stiffness. This paper presents a machine learning-based approach for resistance prediction of the column web panel in transverse compression, based on a numerical dataset generated using IDEA StatiCa Connection. Three machine learning models are investigated and compared: a deep neural network, Random Forest, and XGBoost. Particular attention is given to the influence of model tuning on predictive performance, where the deep neural network is tuned using a manual sensitivity-based procedure, while Random Forest and XGBoost are tuned using grid search. The results highlight differences in model behavior, sensitivity to tuning, and overall prediction accuracy. The study emphasizes the importance of appropriate model selection and tuning when applying machine learning models to resistance prediction problems in steel joint design.
machine learning, numerical design calculations, component method, column web in transverse compression
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
The financial support of project FAST-S-26-9017 is gratefully acknowledged.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
