Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Hemn Unis Ahmed; Aso A Abdalla; Ahmed S Mohammed; Azad A Mohammed; Amir Mosavi

doi:10.3390/ma15051868

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Materials (Basel). 2022 Mar 2;15(5):1868. doi: 10.3390/ma15051868.

Authors

Hemn Unis Ahmed^{1

2}, Aso A Abdalla¹, Ahmed S Mohammed¹, Azad A Mohammed¹, Amir Mosavi^{3

4

5}

Affiliations

¹ Civil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan Region, Sulaimaniyah 46001, Iraq.
² Department of Civil Engineering, Komar University of Science and Technology, Kurdistan Region, Sulaimaniyah 46001, Iraq.
³ Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81107 Bratislava, Slovakia.
⁴ Institute of Information Society, University of Public Service, 1083 Budapest, Hungary.
⁵ John von Neumann Faculty, Obuda University, 1034 Budapest, Hungary.

Abstract

In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R²), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R², RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models' SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO₂ percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.

Keywords: alkaline activator; compressive strength; construction materials; fly ash; geopolymer; geopolymer concrete; machine learning; mortar; prediction; regression.