Multioptimization of FA-Based Geopolymer Concrete Mixes: A Synergistic Approach Using Gray Relational Analysis and Principal Component Analysis

Geopolymer concrete (GPC) is a promising substitute for ordinary portland cement concrete (OPCC) due to its environmental benefits and better mechanical performance. However, the lack of standard mix design limits its applicability in concrete construction. Several mix design parameters significantly influence the characteristics of fly ash (FA)-based GPC. This study aimed to examine the impacts of mix design parameters on various FA-GPC properties and optimize them for maximum response. The mix design parameters were synergistically analyzed and optimized by integrating the Taguchi approach, gray relational analysis (GRA), and principal component analysis (PCA). The effects of three levels of curing temperature (60°C, 75°C, 90°C), alkaline liquid-to-binder ratio (AL/B) (0.4, 0.45, 0.5), molarity of NaOH (10M, 12M, 14M), and Na2SiO3 to NaOH ratio (SS/SH) (1.5, 2.0, 2.5) on the workability, compressive strength (CS), and splitting tensile strength (STS) were studied. Nine mixes were designed by employing Taguchi L9 orthogonal array. Cubical (150 mm side) and cylindrical (150×300 mm) specimens were casted to evaluate the 28-day CS and STS, respectively. Further, a microstructure study using scanning electron microscopy was also conducted. The analyses determine the most influential factors and their optimum values for improvement in synergistic response. The results indicated that curing temperature was the most influential parameter affecting the CS and STS of FA-GPC, while the AL/B ratio and NaOH molarity primarily impacted workability. The optimal input parameter settings for maximum responses were identified as a curing temperature of 75°C, AL/B ratio of 0.4, NaOH molarity of 12M, and SS/SH ratio of 1.5. Finally, the experiments for validation were performed on the optimized GPC mix, demonstrating that the combination of Taguchi-GRA with PCA can be synergistically employed to optimize the parameters of the FA-GPC mixes efficiently. Furthermore, a comprehensive cost analysis was conducted, revealing that the optimized FA-GPC mix incurred a 2.36% higher cost than the most economical FA-GPC mix, demonstrating a slight increase in cost for enhanced performance.