Multi-objective optimization of one-part alkali-activated mortar mixes using Taguchi-Grey relational analysis

Abstract

In the context of the contemporary emphasis on sustainability within the realm of construction, there is a notable surge in attention towards one-part alkali-activated (OP-AA) materials. This is primarily attributed to their enhanced performance and reduced carbon emissions as compared to conventional OPC-based concrete. In the present investigation, Taguchi and Taguchi- Grey relational analysis (GRA) methodologies were employed to execute the experimental design, involving three input parameters, each considered at three levels, to generate an L<inf>9</inf> orthogonal array. An attempt was made to assess the impact of different parameters, such as ground granulated blast furnace slag (GGBFS) to fly ash (FA) ratio - (S/F), water-to-binder ratio - (W/B), and percentage of Na<inf>2</inf>O - (N), on the slump flow, setting time, and compressive strength characteristics and hence to optimize the proportions of the OP-AA mortar blends. The results revealed that optimum parameter levels for multi-objective optimization corresponded to S/F = 1, W/B = 0.45, and N = 5%. For these parameter levels specified, the corresponding values of slump flow, initial setting time, final setting time, and 28 days compressive strength were 208 mm, 285.4 min, 990.4 min, and 36.52 MPa, respectively. In addition, to gain insights into their mineral composition, morphology, and chemical bond characteristics, microstructural characterization such as X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), and Fourier transform infrared spectroscopy (FTIR) were also conducted on selected OP-AA mortar mixes. The microstructural examination unveiled the predominant formation of hydration products, such as C/N -A-S-H gels, in OP-AA mortar blends, resembling those found in conventional alkali-activated materials (AAMs). During the validation phase, an assessment was conducted by comparing the actual experimental results with the predicted values obtained through regression equations. The outcome of this comparison revealed that the proposed optimum mix parameter levels demonstrated the effectiveness of both the Taguchi and Taguchi-GRA approaches. © 2024 Elsevier Ltd