Optimization of self-compacting composite composition using Taguchi-Grey relational analysis

Abstract

This paper presents the results from the multi-performance optimization self-compacting concrete (SCC) mixtures containing fly ash and manufactured sand using Taguchi-Grey relational analysis which translates the performances of all the parametric variables into a comparability sequence. An orthogonal array of trial runs of experiments was developed using the Taguchi method and the results analyzed using grey relational analysis. For the design of experiments, five parameters at different levels were identified and used in the array development. The parameters considered are ordinary Portland cement content (OPC), the content of fly ash used as the replacement of OPC, the content of fine aggregate, the content of manufactured sand used as the replacement of fine aggregate and the content of superplasticizer. Based on the analysis from the trial runs, the optimum mixture was identified, and additional experiments were carried out to validate the composition of the optimized mixture. The findings from this study showed that the content of fly ash used as the replacement of OPC has the highest effect on the properties of the SCC mixtures. A good correlation between the predicted properties of the optimized mixtures and experimental results used as validation indicated that Taguchi-grey relational analysis can be used successfully to optimize the composition of SCC mixtures. The properties of the optimized mixtures also conform with the recommended limits by EFNARC. Thus, SCC mixtures exhibiting acceptable properties and made with fly ash as replacement of OPC and manufactured sand as replacement of the natural fine aggregate can be produced for various construction applications.