Workability, compressive strength, and optimal temperature scrutiny of geopolymer concrete containing bespoke activator and superplasticizer using different prediction models

Abstract

Geopolymers (GP) are novel substances made of aluminosilicate and alkaline activator that are sustainable and environmental-friendly. Geopolymers possess exceptional mechanical properties, and other admirable properties including fire and corrosion resistance. Thus, the efficiency of ash-based geopolymer was assessed. The binders include a combination of rice-husk ash (RHA), kaolin clay powder (KCP) and flyash (FA). RHA and KCP were introduced to mitigate respectively the problems of poor workability and efflorescence associated with flyash (FA) based Geopolymer concrete (GPC). A bespoke sodium silicate produced using RHA was found a more suitable alkaline activator than factory ready-made brand and was therefore utilized together with sodium hydroxide for binder activation. The activator was added at 0, 2.5, 5, 7.5, and 10% of the combined weight of the binder content. A bespoke superplasticizer produced from rice husk in the laboratory was introduced to improve workability. The GPC were cured at various temperatures. The result showed an increase in slump with the addition of the bespoke plasticizer while the compressive strength decreases at sodium hydroxide content above 2.5% of total binder weight. Fourier Transform Infrared Spectroscopy (FTIR) results show key absorbance band at the area between 949 and 3251 cm⁻¹ indicating that addition of the bespoke superplasticizer to the geopolymer concrete reduced the viscosity and improved the flow characteristics. Brunauer-Emmett-Teller (BET) shows RHA, FA and KCP each has higher surface area than cement, thus they can serve as appropriate pozzolanic material and cement proxy. At temperatures above 700C, both compressive strength and weight decrease, for both the bespoke and ready-made sodium silicate. The optimal geopolymer product showed substantial strength and durability enhancements at 70℃, while strength and durability values decline above 70℃, indicating material deterioration. Among models used for prediction, Feret model performed best with R2 of 0.967, indicating its excellent predictive performance.