Rocznik Ochrona Środowiska 2026, vol. 28, pp. 450-467
Abdullah Naser M. Asiri 
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King Khalid University, Saudi Arabia |
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https://doi.org/10.54740/ros.2026.030 |
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The cement industry is a major contributor to global carbon dioxide (CO₂) emissions, necessitating the development of sustainable construction materials with reduced environmental impact. This study proposes a novel integrated framework that combines industrial waste-based low-carbon concrete development with machine-learning-driven strength prediction and mix optimization. Low-carbon concrete mixes were produced by partially replacing ordinary Portland cement with fly ash and ground granulated blast furnace slag (GGBS) at various replacement levels. Experimental investigations were conducted to evaluate compressive strength development at different curing ages. A dataset comprising 300 experimental observations was subsequently employed to develop and compare three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), for compressive strength prediction. The results demonstrate that appropriately designed low-carbon concrete mixtures can achieve comparable or superior long-term strength while substantially reducing cement consumption and associated CO₂ emissions. Among the evaluated models, XGBoost exhibited the highest predictive accuracy, indicating its suitability for sustainable concrete mix optimization. The novelty of this study lies in integrating experimental low-carbon concrete design, environmental assessment, and advanced machine learning techniques within a unified framework to enhance structural performance and sustainability simultaneously. The proposed approach provides an efficient pathway to reduce trial-and-error experimentation and accelerate the adoption of eco-friendly concrete in modern construction practices.
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low-carbon concrete, machine learning, compressive strength, industrial waste, XGBoost
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AMA Style
Asiri A.. Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste. Rocznik Ochrona Środowiska. 2026; 28. https://doi.org/10.54740/ros.2026.030
ACM Style
Asiri A.. 2026. Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste. Rocznik Ochrona Środowiska. 28. DOI:https://doi.org/10.54740/ros.2026.030
ACS Style
Asiri A., Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste Rocznik Ochrona Środowiska 2026, 28, 450-467. https://doi.org/10.54740/ros.2026.030
APA Style
Asiri A. (2026). Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste. Rocznik Ochrona Środowiska, 28, 450-467. https://doi.org/10.54740/ros.2026.030
ABNT Style
ASIRI A.. Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste. Rocznik Ochrona Środowiska, v. 28, p. 450-467, 2026. https://doi.org/10.54740/ros.2026.030
Chicago Style
Abdullah Naser M. Asiri. 2026. "Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste". Rocznik Ochrona Środowiska 28, 450-467. https://doi.org/10.54740/ros.2026.030
Harvard Style
Asiri A. (2026) "Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste", Rocznik Ochrona Środowiska, 28, pp. 450-467. doi:https://doi.org/10.54740/ros.2026.030
IEEE Style
Asiri A., "Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste", RoczOchrSrod, vol 28, pp. 450-467. https://doi.org/10.54740/ros.2026.030