Temperature-dependent compressive strength modeling of geopolymer blocks utilizing glass powder and steel slag

This article investigates the development of geopolymers as a modern, environmentally sustainable binder with ceramic-like properties, offering exceptional thermal and fire-resistant characteristics. The study primarily utilized fly ash (FA) in combination with glass powder (GP) and steel slag (SS). The SS content varied between 30 % and 40 %, while the molarity of NaOH was set at 10 M, 12 M, and 14 M. Based on these variables, a total of eighteen mixes incorporating GP and SS were formulated. The samples were subjected to elevated temperatures of 200 °C, 400 °C, 600 °C, and 800 °C, after which their compressive strengthswere measured. To better understand the material formation, analyses were conducted by using scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and thermogravimetry differential thermal analysis. The investigation examined the influence of oxide ratios (Na/Si, Si/Al, H₂O/Na₂O, and Na/Al) on the compressive strength at elevated temperatures. Additionally, the research sought to develop a predictive model, elucidating the relationship between these oxide ratios and the compressive strength of geopolymers. To achieve this, ten machine learning techniques were applied, revealing the complex connection between oxide ratios and the strength properties of geopolymers. The support vector regressor (SVR) model outperformed other regression and boosting models, obtaining a high coefficient of determination (R²) value of 0.95, indicating superior predictive accuracy. The reduced error levels and high R² values highlighted the enhanced performance of the SVR model. A sensitivity analysis was done to understand the contributions of each parameter to the outcome predictions further. Employing machine learning techniques to predict the compressive strength of geopolymer blocks under various elevated temperature conditions improves predictive accuracy and optimizes resource utilization, leading to significant time savings.