Hybrid Optimization of CO₂ Emissions and Energyin High-Performance Concrete Using KNN, Elastic Net, and Artificial Rabbits Optimization Models
Abstract
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.
Full Text:
PDFReferences
O. Büyüköztürk and D. Lau, “High performance concrete: fundamentals and application,” Massachusetts Institute of Technology, Cambridge, Massachusetts, US, pp. 1–20, 2002. https://eskisakarya.imo.org.tr/resimler/ekutuphane/pdf/3146.pdf.
A. N. Arasu, N. Muthusamy, B. Natarajan, and R. Parthasaarathi, “Optimization of high performance concrete composites by using nano materials,” Research on Engineering Structures and Materials, vol. 9, no. 3, pp. 843–859, 2023.
S. Chithra, S. R. R. S. Kumar, K. Chinnaraju, and F. A. Ashmita, “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks,” Constr Build Mater, Elsevier, vol. 114, pp. 528–535, 2016. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
B. Li, R. Basu Roy, D. Wang, S. Samsi, V. Gadepally, and D. Tiwari, “Toward sustainable hpc: Carbon footprint estimation and environmental implications of hpc systems,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, ACM Digital Library, 2023, pp. 1–15. https://doi.org/10.1145/3581784.3607035.
H. H. M. Ali, “Advances in High-Performance Concrete: A Comprehensive Review of Materials, Design, and Applications,” KHWARIZMIA, vol. 2023, pp. 131–137, Aug. 2023.
A.-T. Nguyen, S. Reiter, and P. Rigo, “A review on simulation-based optimization methods applied to building performance analysis,” Appl Energy, Elsevier, vol. 113, pp. 1043–1058, 2014. https://doi.org/10.1016/j.apenergy.2013.08.061.
A. Zahoor, F. Mehr, G. Mao, Y. Yu, and A. Sápi, “The carbon neutrality feasibility of worldwide and in China’s transportation sector by E-car and renewable energy sources before 2060,” J Energy Storage, Elsevier, vol. 61, p. 106696, 2023. https://doi.org/10.1016/j.est.2023.106696.
Y. Zhang and Y. Zhang, “Artificial Intelligence-Driven Models for Predicting Chloride Diffusion in Concrete: A Comparative Regression Analysis,” Journal of Artificial Intelligence and System Modelling, JAISM Bilijipub, vol. 03, no. 01, pp. 33–48, 2025. https://doi.org/10.22034/jaism.2025.495993.1092
A. M. Omer, “Energy use and environmental impacts: A general review,” Journal of renewable and Sustainable Energy, AIP Publishing, vol. 1, no. 5, 2009. https://doi.org/10.1063/1.3220701.
E. I. Aghimien, L. M. Aghimien, O. O. Petinrin, and D. O. Aghimien, “High-performance computing for computational modelling in built environment-related studies–a scientometric review,” Journal of Engineering, Design and Technology, Emerald, vol. 19, no. 5, pp. 1138–1157, 2021. https://doi.org/10.1108/JEDT-07-2020-0294.
A. Kanduri et al., “Approximation knob: Power capping meets energy efficiency,” in 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE, 2016, pp. 1–8.
J. K. W. Wong and J. Zhou, “Enhancing environmental sustainability over building life cycles through green BIM: A review,” Autom Constr, Elsevier, vol. 57, pp. 156–165, 2015. https://doi.org/10.1016/j.autcon.2015.06.003.
A. Korpa, T. Kowald, and R. Trettin, “Phase development in normal and ultra high performance cementitious systems by quantitative X-ray analysis and thermoanalytical methods,” Cem Concr Res, Elsevier, vol. 39, no. 2, pp. 69–76, 2009. https://doi.org/10.1016/j.cemconres.2008.11.003.
H. Yazıcı, H. Yiğiter, A. Ş. Karabulut, and B. Baradan, “Utilization of fly ash and ground granulated blast furnace slag as an alternative silica source in reactive powder concrete,” Fuel, Elsevier, vol. 87, no. 12, pp. 2401–2407, 2008. https://doi.org/10.1016/j.fuel.2008.03.005.
N. Van Tuan, G. Ye, K. Van Breugel, and O. Copuroglu, “Hydration and microstructure of ultra high performance concrete incorporating rice husk ash,” Cem Concr Res, Elsevier, vol. 41, no. 11, pp. 1104–1111, 2011. https://doi.org/10.1016/j.cemconres.2011.06.009.
J. Dils, V. Boel, and G. De Schutter, “Influence of cement type and mixing pressure on air content, rheology and mechanical properties of UHPC,” Constr Build Mater, Elsevier, vol. 41, pp. 455–463, 2013. https://doi.org/10.1016/j.conbuildmat.2012.12.050.
S. Abbas, A. M. Soliman, and M. L. Nehdi, “Exploring mechanical and durability properties of ultra-high performance concrete incorporating various steel fiber lengths and dosages,” Constr Build Mater, Elsevier, vol. 75, pp. 429–441, 2015. https://doi.org/10.1016/j.conbuildmat.2014.11.017.
Ç. Yalçınkaya and H. Yazıcı, “Effects of ambient temperature and relative humidity on early-age shrinkage of UHPC with high-volume mineral admixtures,” Constr Build Mater, Elsevier, vol. 144, pp. 252–259, 2017. https://doi.org/10.1016/j.conbuildmat.2017.03.198.
Z. Wu, C. Shi, K. H. Khayat, and L. Xie, “Effect of SCM and nano-particles on static and dynamic mechanical properties of UHPC,” Constr Build Mater, Elsevier, vol. 182, pp. 118–125, 2018. https://doi.org/10.1016/j.conbuildmat.2018.06.126.
Y. Akbulut, A. Sengur, Y. Guo, and F. Smarandache, “NS-k-NN: Neutrosophic set-based k-nearest neighbors classifier,” Symmetry (Basel), MDPI, vol. 9, no. 9, p. 179, 2017. https://doi.org/10.3390/sym9090179.
Y. Qian, W. Zhou, J. Yan, W. Li, and L. Han, “Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery,” Remote Sens (Basel), MDPI, vol. 7, no. 1, pp. 153–168, 2014. https://doi.org/10.3390/rs70100153.
H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” J R Stat Soc Series B Stat Methodol, vol. 67, no. 2, pp. 301–320, 2005.
C. Hans, “Elastic net regression modeling with the orthant normal prior,” J Am Stat Assoc, vol. 106, no. 496, pp. 1383–1393, 2011.
A. J. Riad, H. M. Hasanien, R. A. Turky, and A. H. Yakout, “Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm,” Sustainability, MDPI, vol. 15, no. 5, p. 4625, 2023. https://doi.org/10.3390/su15054625.
DOI: https://doi.org/10.31449/inf.v49i15.10054
This work is licensed under a Creative Commons Attribution 3.0 License.