Sustainable Mix Design of Sugarcane Bagasse Ash Concrete via AutoML-Assisted Multi-Objective Optimization
The environmental impact of cement production has become a growing global concern due to its substantial contribution to CO2 emissions. Sugarcane bagasse ash (SCBA), as a supplementary cementitious material, offers a sustainable alternative by partially replacing cement and reducing the carbon footprint of concrete. However, determining optimal mix proportions that balance mechanical strength, cost efficiency, and environmental benefits remains a complex challenge. In this study, a multi-objective optimization framework was developed by integrating automated machine learning (Auto-ML) with the NSGA-III algorithm. A surrogate model for predicting compressive strength was constructed using the TPOT-based Auto-ML tool, achieving high predictive accuracy with R2 values of 0.993 and 0.908 for the training and test datasets, respectively. NSGA-III was then employed to derive Pareto-optimal mix designs, enabling simultaneous optimization of strength, cost, and CO2 emissions. To validate the proposed multi-objective optimization framework, SCBA concrete specimens were prepared using the optimized mix proportions and tested under uniaxial compression. The experimental results exhibited good agreement with the predicted values, with deviations within an acceptable margin, thereby confirming the accuracy and reliability of the framework. This study provides a practical approach for the intelligent design of low-carbon SCBA concrete, contributing to the advancement of sustainable construction practices.