Experimental analysis of durability in self-compacting concrete: carbonation penetration perspectives

Abstract

Carbonation, a chemical reaction between atmospheric CO2 and the hydration products of cement, leads to a reduction in the pH of concrete, thereby increasing the risk of reinforcement corrosion. This study examines the durability of conventional concrete (CC) and self-compacting concrete (SCC) through accelerated carbonation tests, with a focus on the impact of mineral admixtures, specifically Ground Granulated Blast Furnace Slag (GGBS) and fly ash, as partial replacements for cement. The study investigates the depth of carbonation over time under controlled accelerated conditions, using concrete mixes with varying proportions of GGBS and fly ash. The results indicate that SCC mixes with higher GGBS content exhibit superior durability, as evidenced by significantly lower carbonation depths compared to conventional concrete mixes. Specifically, for SCC, carbonation depths ranged from 8.77 mm (SCC1 with 30% GGBS) to 11.9 mm (SCC7 with higher fly ash content), whereas for CC, carbonation depths ranged from 11.43 mm (CC2 with 30% GGBS) to 16.1 mm (CC7 with higher fly ash content). The inclusion of mineral admixtures, particularly GGBS, was found to reduce porosity, thereby hindering the penetration of CO2. However, it was observed that excessive replacement of cement with mineral admixtures beyond an optimal threshold resulted in decreased carbonation resistance due to the reduced availability of calcium hydroxide for carbonation. Additionally, the study highlights the significance of the water/binder ratio in influencing the concrete’s strength and porosity, both of which are critical factors in carbonation resistance. The findings suggest that SCC, particularly with an optimal GGBS content, offers enhanced durability compared to conventional concrete. A Multiple Linear Regression (MLR) model was also developed, providing accurate predictions for key durability parameters and demonstrating the potential of statistical modeling in optimizing concrete mix designs for improved performance and sustainability.