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 (45) orthogonal array comprising five factors, each with...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525004905 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 (45) 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 A0B15C2.5D100E0.25 (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. |
|---|---|
| ISSN: | 2214-5095 |