Optimizing the mechanical properties of sustainable self-compacting concrete contains industrial by-products by using Taguchi and Grey-Taguchi methods

This study focused on investigating and optimizing the mechanical properties of self-compacting concrete (SCC) through the application of the Taguchi and Grey-Taguchi methods. Sixteen SCC mixtures were initially formulated using a standard L16 (4 5 ) orthogonal array comprising five factors, each with four levels. The selected factors included metakaolin and ground granulated blast furnace slag as components of the cementitious materials , waste granite powder as a substitute for sand, waste marble powder as a limestone powder filler, and beet molasses as part of the superplasticizer . The Taguchi method was employed to optimize the responses of compressive strength , splitting tensile strength , flexural strength , Ultrasonic Pulse Velocity , and Dynamic Elastic Modulus after curing periods of 28 and 90 days. Additionally, the Grey-Taguchi method, as a multi-response optimization approach, converted the problem into a mono-response framework, leading to the determination of a single optimal SCC mixture to maximize the multiple responses. Analysis of variance (ANOVA) was conducted to evaluate the contribution of each factor to both mono- and multi-response optimizations. Moreover, based on grey relational grades, the microstructures of the best-performing and worst-performing mixtures among the sixteen mixtures were analyzed using Scanning Electron Microscopy (SEM) imaging and Energy-Dispersive X-ray Spectroscopy (EDX). It was found that the final optimal mixture for all of the experiments is defined as A 0 B 15 C 2.5 D 100 E 0.25 (comprising 0 % metakaolin, 15 % ground granulated blast furnace slag, 2.5 % waste granite powder, 100 % waste marble powder, and 0.25 % beet molasses). This mixture achieved a predicted grade of 0.9257, notably higher than the grades of the 16 batched mixtures. Furthermore, the interactions between factors and their roles in mono- and multi-response optimization, representing the most complex aspect of the study, offer a profound and comprehensive analysis of the by-products utilized.