<![CDATA[Geopolymer Concrete (GPC) is a new class of concrete that presents a vital improvement in sustainability and the environment, particularly in recycling and alternative construction methods. Geopolymers offer a sustainable, low energy consumption, low carbon footprint, and a 100% substitute for the Portland cement binder for civil infrastructure applications. Furthermore, many aluminosilicate materials can be obtained as by-products of other processes, such as coal combustion or the thermal pulping of wood. In addition, slag and fly ash are necessary to source materials for geopolymer. Therefore, geopolymer is considered a solution for waste management that can minimize greenhouse gas emissions. In this statistical study, the present experimental work and found experimental data were collected from local and international literature and were used to build and validate the statistical models to predict the strength development of Geopolymer concrete with binary and ternary systems of source materials. The main independent variable was R, representing the ratio of SiO2/Al2O3by weight in the source material. The investigated range of R was 1.42–3.6. Nine concrete geopolymer mixes with R in the above range represent the experimental part carried out. The targeted properties were compressive, splitting, and flexural strengths. The experimental results showed that the R ratio significantly influences the mechanical performance of the final product. The compressive strength improved by 82, 86, 93, and 95%, when metakaolin content was partially replaced by fly ash and GGBS by percentages of 30, 70, 72, 90, and 95% for mixes 2, 3, 5, 7, and 8 respectively. Also, when GGBS partially replaced fly ash content by 36% and 100% for mixes 6 and 9, compressive strength improved by 10.6% and 41.8%, respectively, compared to mix4. Furthermore, the statistical study revealed that the R ratio might be utilized to determine geopolymer strength with reasonable accuracy. The built models were developed by linear and non-linear regression analysis using SPSS software, version 25. Doi: 10.28991/CEJ-2022-08-03-04 Full Text: PDF]]>
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