Characterization of self-curing concrete's strength and microstructural parameters using pumice stone, polyethylene glycol, light expanded clay aggregate, and super absorbent polymers

Abstract

Abstract This study focuses on enhancing concrete's sustainable development by improving microstructural properties, strength, and crack resistance compared to standard concrete. Traditional curing methods require substantial water resources, posing challenges in water-scarce regions and tall construction. The research aims to develop a water-independent curing solution. Shrinkage Reduction Admixtures (SRA), specifically PEG 400 and 600, are added in varying proportions (1% to 5% of binder) to counteract shrinkage and allow internal curing. Additionally, porous aggregates like Pumice stone (PU), light expanded clay aggregate (LECA), and super absorbent polymer (SAP) are employed to act as internal reservoirs for cement hydration. Different ratios of PU and LECA (1% to 5% of binder) and levels of SAP (0.05% to 0.25% of binder) are used. This research involves 26 unique blend formulations. Experimental results show significant enhancements in concrete strength, reduced shrinkage, and lowered water demand for self-curing concrete. Among the formulations, LECA concrete performs the best, followed by PU, SAPs, PEG 600, and PEG 400. Optimal self-curing agent levels are identified as PEG 400 (4%), PEG 600 (3%), SAP (0.15%), LECA (3%), and PU stone (4%). The outcomes reveal positive contributions of self-curing agents to microstructural characteristics, enhancing hydration properties such as prolonged hydration, improved water retention, workability, and consistent curing. As a result, the resulting concrete demonstrates increased strength, durability, and overall performance, emphasizing the importance of incorporating pumice stone, polyethylene glycol, light expanded clay aggregate, and super absorbent polymers in concrete formulations. Principal Component Analysis is employed to validate experimental variables, and mathematical modeling using multilinear regression (MLR) predicts self-curing concrete characteristics, aligning well with experimental data.