Rocznik Ochrona Środowiska 2026, vol. 28, pp. 349-373
Muhannad Riyadh Alasiri 
 |
King Khalid University, Saudi Arabia |
 |
|
 |
https://doi.org/10.54740/ros.2026.024 |
   |
|
 |
The construction sector is a huge contributor to carbon emissions in the world, mostly because of the heavy applications of Ordinary Portland Cement (OPC). Geopolymer concrete with low carbon content has become a viable alternative to concrete; nevertheless, accurate prediction and evaluation of its CO2 emissions have not been fully achieved, especially through combined experimental and data analysis. The literature on this study focuses mainly on mechanical performance, and there is limited understanding of environmental emission modeling. To measure and forecast CO2 emissions from low-carbon geopolymer concrete, this paper develops an experimental and machine-learning system. An array of geopolymer blends comprising fly ash and ground granulated blast furnace slag (GGBS) was developed at different binder proportions, alkaline activator ratios, and curing conditions. The amount of CO2 was determined using a cradle-to-gate evaluation method (kg CO2/m3). Data were subsequently trained on and tested on machine learning models, such as Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) using experimental data. The R², RMSE, and MAE measures were used to assess model performance. Findings show that the XGBoost model has the best prediction accuracy (R² > 0.95), indicating good generalization ability. The highest CO2 emission reduction was 45–65 percent for geopolymer mixtures compared to traditional OPC concrete. The construct can offer a trustworthy decision support system in geopolymer concrete design that is environmentally friendly and further sustainability in construction methods with predictive modeling of emissions.
geopolymer concrete, CO2 emission, low-carbon materials, Life Cycle Assessment (LCA), machine learning, environmental impact
 |
 |
AMA Style
Alasiri M.. Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques. Rocznik Ochrona Środowiska. 2026; 28. https://doi.org/10.54740/ros.2026.024
ACM Style
Alasiri M.. 2026. Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques. Rocznik Ochrona Środowiska. 28. DOI:https://doi.org/10.54740/ros.2026.024
ACS Style
Alasiri M., Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques Rocznik Ochrona Środowiska 2026, 28, 349-373. https://doi.org/10.54740/ros.2026.024
APA Style
Alasiri M. (2026). Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques. Rocznik Ochrona Środowiska, 28, 349-373. https://doi.org/10.54740/ros.2026.024
ABNT Style
ALASIRI M.. Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques. Rocznik Ochrona Środowiska, v. 28, p. 349-373, 2026. https://doi.org/10.54740/ros.2026.024
Chicago Style
Muhannad Riyadh Alasiri. 2026. "Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques". Rocznik Ochrona Środowiska 28, 349-373. https://doi.org/10.54740/ros.2026.024
Harvard Style
Alasiri M. (2026) "Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques", Rocznik Ochrona Środowiska, 28, pp. 349-373. doi:https://doi.org/10.54740/ros.2026.024
IEEE Style
Alasiri M., "Data-Driven Prediction of CO2 Emissions in Low-Carbon Geopolymer Concrete Using Integrated Experimental Analysis and Machine Learning Techniques", RoczOchrSrod, vol 28, pp. 349-373. https://doi.org/10.54740/ros.2026.024