Machine learning-guided optimization of coarse aggregate mix proportion based on CO2 intensity index

Abstract

Aggregate accounts for 60‐80% volume fraction of concrete, which has a great influence on the CO₂ emission and performance of concrete. Apart from natural coarse aggregate (NCA), recycled coarse aggregate (RCA) and carbonation recycled coarse aggregate (CRCA) are becoming an important component. This study established a database containing 925 experimental samples of compressive strength (CS) and CO₂ emission, which including NCA, RCA, and CRCA concrete respectively. Additionally, the CO₂ intensity index was introduced to evaluate the CS and CO₂ emission. Machine learning (ML) methods were utilized to establish prediction models for CS, CO₂ emissions, and CO₂ intensity. The significance of features was analyzed through SHAP and PDP. For the optimization of coarse aggregate mix proportion, the GA and MOPSO algorithms were employed for single and bi-objective optimization designs, respectively. The results indicated that the optimization of coarse aggregate mix proportion can effectively reduce CO₂ emission and CO₂ intensity of concrete. A CRCA content of 30% is optimal for achieving both enhanced CS and reduced CO₂ emissions. The carbonation treatment of RCA presents a viable approach for mitigating CO₂ footprint and enhancing the mechanical properties of RCA concrete. The proposed optimization frame can facilitate appropriate decision making for low-carbon concrete design.