Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

• Main Authors: Hassan Amer Algaifi, Abdeliazim Mustafa Mohamed, Eyad Alsuhaibani, Shahiron Shahidan, Fahed Alrshoudi, Ghasan Fahim Huseien, Suhaimi Abu Bakar
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Polymers
• Subjects: waste glass materials; alkali-activated mortars; optimisation; nano silica; fly ash; granulated blast-furnace slag
• Online Access: https://www.mdpi.com/2073-4360/13/16/2750

Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (<i>p</i> values < 0.0001 and high F-values) as well as several statistical validation methods (<i>R</i> > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted <i>R</i>² and adj. <i>R</i>² was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.