Hybrid Optimization of CO₂ Emissions and Energyin High-Performance Concrete Using KNN, Elastic Net, and Artificial Rabbits Optimization Models - Gao, Xiong; Li, Yan; Wu, Yansheng (Informatica, 2025)
In this study, an integrated machine learning framework is proposed to accurately predict and minimize CO₂ emissions and energy consumption in the manufacturing of High-Performance Concrete (HPC). The methodology combines K-Nearest Neighbor (KNN) and Elastic Net Regression (ENR) models with the Artificial Rabbits Optimization (ARO) algorithm for hyperparameter tuning, and employs Recursive Feature Elimination (RFE) to isolate the most influential input variables. A dataset comprising key HPC mix components was curated from experimental sources and subjected to rigorous preprocessing. Among the tested models, the hybrid ENR + ARO (ENAR) model achieved the best performance for energy prediction with an R² of 0.986 and RMSE of 52.63 MJ/m³, while the KNN + ARO (KNAR) model yielded the highest accuracy for CO₂ emission prediction with an R² of 0.992 and RMSE of 7.57 kg/m³. The application of RFE improved model performance by 12.4% in RMSE reduction for energy prediction and 9.6% for CO₂ estimation, by eliminating redundant features. Cement and superplasticizer content were identified as the most influential predictors. These results provide a reliable and interpretable framework for enhancing the sustainability of concrete production through data-driven mix optimization.