A novel explainable AI-based design optimization framework to estimate sustainability and economic impacts of reinforced concrete structures

Commonly, structures are designed with a focus on safety and serviceability, while structural sustainability is often overlooked at the preliminary design stage. Optimizing a design that considers environmental, economic, and structural factors early in the process requires substantial time and resources. This paper introduces an innovative Explainable Artificial Intelligence (XAI) approach to optimize the environmental and economic impacts of reinforced concrete building designs at the early stage. First, machine learning (ML) models are developed to predict carbon emissions, embodied energy, and life cycle costs based on materials and basic construction information. Then, XAI techniques such as SHAP, PDP, ICE, and LIME are used to identify key input features that influence Life Cycle Assessment (LCA) and Life Cycle Cost Assessment (LCCA). Finally, the counterfactual (CF) technique optimizes design by modifying these key features. The results show that XGBoost is the best-performing model (R 2 = 0.99) for the dataset. XAI analysis identifies material quantity as the most influential variable, with other significant factors including concrete strength, distance to construction and disposal sites, vehicle capacity, and the daily volume of concrete poured. Using these insights, CF optimization reduces both LCA and LCCA by 10–20%, as predefined desired target outcomes. This study demonstrates the potential of XAI and ML to optimize the design process at the preliminary stage, balancing sustainability and economic efficiency.