Experimental and modelling analysis of waste material-based geopolymer concrete incorporated with crumb rubber particles

Abstract

Rubberized geopolymer concrete (RuGPC) emerges as an eco-friendly alternative to conventional concrete, significantly reducing greenhouse gas emissions. This study investigates the use of steel slag (SS) in varying proportions (30 %, 35 %, and 40 %) as a replacement for ground granulated blast furnace slag (GGBS) in geopolymer concrete, with crumb rubber (CR) replacing crusher dust (CD) as fine aggregate due to its increasing demand. The research focuses on understanding the impact of aluminosilicate materials on the mechanical, thermal, and microstructural properties of geopolymer concrete cured at 60°C. Advanced characterization techniques, including Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDX), X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR), were employed. SEM and EDX analyses revealed that the microstructural properties of GGBS and SS materials, mainly Na/Si, Si/Al, H₂O/Na₂O, and Na/Al ratios, significantly influence RuGPC performance through gel formation. FTIR analysis indicates a shift in the stretching vibrations of GGBS and SS to lower wavenumbers due to geopolymerization changes. XRD results show the formation of C-S-H gel at around 27–30° 2theta, attributed to increased GGBS and SS content. Despite efforts to incorporate CR into geopolymer matrices, challenges in mitigating strength degradation persist. To address this, a predictive model was developed to understand the key factors affecting RuGPC performance. Six machine learning techniques—M5P (pruned and unpruned), random forest, random tree, linear regression, and support vector machine with various kernels (PUK, RBF, PK, and NPK), and artificial neural network (ANN)—were employed to predict the physical and thermal behavior of RuGPC. The analysis identified ANN-based models as the most effective. Sensitivity analysis highlighted the grade of rubber and the CR replacement percentage by volume of CD as the most influential parameters determining RuGPC compressive strength, density, and thermal conductivity. Moreover, The economic analysis revealed that RuGPC mixtures were 1.2–11.61 % more cost-effective than OPC concrete. These findings underscore the importance of predictive model development in optimizing RuGPC properties for practical applications, offering valuable insights for decision-making processes.