Modelling of Modulus of Elasticity of Low-Calcium-Based Geopolymer Concrete Using Regression Analysis

Abstract

Despite the unremitting efforts to model the modulus of elasticity of low-calcium-based geopolymer concrete, the state-of-the-art models need much improvement to reduce the error signals and increase the reliability. This study represents a comprehensive regression analysis to model the modulus of elasticity of low-calcium-based geopolymer concrete in terms of its compressive strength. The proposed model’s assumptions are based on taking into account the chemical composition and compressive strength class and considering the normal density of concrete. The modelling is based on 67 different mix-design samples collected from peer-reviewed literature, which are divided into two groups. The first group consists of 59 samples that are used to construct the proposed model, while the second group consists of 8 samples that are used to test the validity of the proposed model. The analysis showed that the proposed model gives the root mean squared error value (RMSE) of 3.122 GPa and the mean absolute percentage error value (MAPE) of 15.0%. Therefore, the proposed model gives 41% and 52.2% reductions in RMSE and MAPE, respectively, from the state-of-the-art model in the literature. Furthermore, other statistical parameters to evaluate the goodness of fitness of the proposed model have been considered, such as the relative root mean squared error (RRMSE) (15.5%), the coefficient of determination (R-squared) (0.773), and the coefficient of correlation (R) (0.88); all of which indicated that the goodness of fitness is good and the proposed model has a high correlation to the actual values. Applying the proposed model in future applications will help reduce the time and cost of geopolymer production, as the proposed model has significantly reduced the error signals.