Predicting CO2 emissions and strength of FA-SF geopolymer mortar: An ANN approach for sustainable construction

Abstract

The construction industry is facing increasing pressure to reduce the CO 2 emissions from conventional cement manufacturing. Geopolymer concrete, utilizing fly ash (FA) and silica fume (SF) as precursors, presents a promising sustainable alternative. This study investigates the mechanical properties and environmental performance of FA- and SF-based GPC through a comprehensive experimental campaign and predictive modeling. This study conducted destructive (DT) and non-destructive testing (ultrasonic pulse velocity [UPV] measurement) of the fresh and hardened properties of GPC, focusing on the effect of steel fiber reinforcement on flexural strength and resilience. Based on life cycle assessment, artificial neural networks (ANNs) were used to predict both mechanical properties and CO 2 emissions of GPC mixes. The ANN models demonstrated accurate predictions (R² = 0.96–0.99) and exhibited low errors. A SHAP-based sensitivity analysis identified key input parameters influencing ANN predictions. From the results, it can be concluded that optimized GPC mixes containing FA and SF can achieve gains of 20–25% in early-age compressive strength and a 15–20% reduction in CO 2 emissions compared to OPC. The findings of this research are significant for designing long-life, high-performance GPC mixes. The resulting ANN-based predictive tool provides a practical approach for engineers and materials scientists to design GPC mixes that meet specific performance requirements and sustainability objectives, thereby promoting the development of low-carbon construction materials.