TY  - EJOU
AU  - Nawaz, Ahmad 
AU  - Khan, Hilal 
AU  - Ullah, Salamat 
AU  - Almujibah, Hamad 
AU  - Elshekh, Ali E. A. 
AU  - Bashir, Maaz Osman 

TI  - Data-Driven Design and Optimization of Sustainable and Low-Carbon Calcium Sulfoaluminate Cement Blends Incorporating Blast Furnace Slag
T2  - Computer Modeling in Engineering \& Sciences

PY  - 
VL  - 
IS  - 
SN  - 1526-1506

AB  - Calcium sulfoaluminate (CSA) cement is considered a promising low-carbon alternative to ordinary Portland cement owing to its lower clinkerization temperature and reduced CO<sub>2</sub> emissions. The incorporation of blast furnace slag can further enhance the sustainability of CSA-based binders by lowering clinker content, reducing cost and embodied carbon emissions, while maintaining satisfactory mechanical performance. However, optimizing CSA-slag systems remains challenging due to the complex interactions among binder composition, clinker mineralogy, and slag replacement levels. This study therefore aims to predict the compressive strength of CSA-slag binders, identify the mixture parameters governing it, and optimize mixture proportions for balanced mechanical, environmental, and economic performance, using a machine learning (ML)-based framework trained on 232 experimental samples compiled from the literature. Three ML models, Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB), were optimized using a Genetic Algorithm and integrated through stacked ensemble learning with a multilayer perceptron meta-learner. Among the developed models, the DT-XGB ensemble achieved the highest predictive accuracy for compressive strength (R<sup>2</sup> = 0.965, RMSE = 3.168 MPa). Shapley additive explanations (SHAP) analysis identified the water-to-cement ratio as the most influential parameter governing compressive-strength prediction, followed by CSA clinker content, curing age, ye’elimite content, M (sulfate-to-ye’elimite ratio), slag content, and belite content. Furthermore, a multi-objective optimization framework based on NSGA-II was employed to simultaneously optimize compressive strength, embodied CO<sub>2</sub> emissions, and cost. The optimum mixture contained 30% CSA clinker and 70% slag at a water-to-cement ratio of 0.4, achieving 57.9 MPa compressive strength, 379 kg/m<sup>3</sup> embodied CO<sub>2</sub> emissions, and a cost of 91.8 USD/m<sup>3</sup>. These findings provide an effective strategy for designing sustainable, high-performance, and low-carbon CSA-based cementitious materials.
KW  - Machine learning; calcium sulfoaluminate cement; blast furnace slag; multi-objective optimization; sustainable cement

DO  - 10.32604/cmes.2026.086781