Prediction and optimization of strength and CO2 emission for geopolymer concrete mix design using machine learning
Year: 2025
Type: article
Abstract: The growing demand for sustainable construction materials has driven interest in geopolymer concrete (GPC) as an eco-friendly alternative to conventional Ordinary Portland Cement (OPC) concrete. GPC offers considerable potential to reduce carbon emissions. However, the lack of standardized mix design procedures and the complex interdependence of its components pose significant challenges in achieving optimal performance. This study presents a machine learning (ML)-based approach for optimizing GPC mix design with the dual objective of minimizing CO2 emissions and maximizing compressive strength. A comprehensive dataset comprising 1,507 data points was compiled from 23 published studies. The Extreme Gradient Boosting (XGBoost) model was employed and achieved strong predictive performance with an R2 of 0.9113, MAE of 3.9232, and RMSE of 6.2736. Further, Bayesian Optimization was applied to identify optimal mix proportions that meet strength requirements while minimizing environmental impact. The optimization results a 35.4% increase in compressive strength and a 77.7% reduction in carbon emissions. Additionally, a Graphical User Interface (GUI) was developed to allow users to input their mix design parameters, receive instant predictions and optimization recommendations. This study highlights the potential of integrating machine learning with sustainable construction practices and offers a scalable, data-driven solution for environmentally responsible concrete design.
Source: Results in Materials
Authors: Mohammad Mohsin, Tanima Ghosh, Nusrat Hoque
Institution: Chittagong University of Engineering & Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,530
OpenAlex ID: W4415424626