Machine learning-driven capacity design and embodied carbon reduction optimization in composite reduced web section (RWS) connections

• An ensemble machine learning framework with NSGA-II optimization was developed to predict mechanical, ductility, and sustainability of RWS connections. • XGBoost demonstrated the highest predictive accuracy across most evaluated outputs within the ensemble machine learning models used in the study. • SHAP analysis showed cross-sectional geometry and material stiffness as key features influencing capacity and ductility, enhancing interpretability. • An online interface was deployed on Hugging Face to explore Pareto-optimal designs balancing seismic performance and embodied carbon reduction. A gap in current predictive modelling approaches limits the ability to accurately assess the mechanical, durability performance and sustainability metrics of Reduced Web Section (RWS) connections. This paper addresses this gap by developing an ensemble machine learning (ML) framework combined with multi-objective optimisation, enabling the efficient prediction of seven key mechanical and ductility properties alongside total embodied carbon (EC) reduction. Three ensemble ML models—Extra Trees Regressor (ETR), Gradient Tree Boosting (GTBR), and Extreme Gradient Boosting (XGBoost)—were evaluated, with XGBoost demonstrating superior generalization across most outputs. Additionally, Shapley Additive Explanations (SHAP) analysis was conducted to identify the most influential design parameters, improving model interpretability. The multi-objective optimisation performed using NSGA-II, generated Pareto-optimal solutions, highlighting trade-offs between structural performance and sustainability considerations. The findings reveal that cross-sectional properties, material stiffness, and connection type significantly impact RWS performance, and optimising these parameters can lead to improved ductility, moment capacity, and reduced environmental impact. To enhance practical applicability, a user-friendly interface was developed and deployed via Hugging Face, allowing users to test the results, make predictions and retrieve optimal design parameters based on the nearest Pareto-optimal solutions. The results of this paper demonstrate that ensemble ML methods, coupled with optimisation and explainability tools, provide a robust framework for advancing RWS connection design, ensuring both seismic resilience and sustainability in structural engineering.