DESIGN OF OPTIMUM MIX OF CONCRETE CONTAINING VARIOUS MINERAL ADMIXTURES

Abstract

In the recent developments in civil engineering, such as high-rise. buildings and long-span bridges, the higher compressive strength concretes are needed. Concrete needs to be designed for certain properties in the plastic stage, such as workability, cohesiveness, initial set retardation and in the hardened stage, such as strength, imperviousness, durability. Among various properties of concrete, compressive strength is the most important mechanical property, which is usually measured after a standard curing of 28 days and it gains strength over a long period of time after pouring. Concrete mix design is the process of deciding the optimum proportions for improved performance of concrete. It involves satisfying a balance between economics and the mix design specifications. The required characteristics, such as workability and strength, are governed by the expected use of concrete and by conditions expected to be encountered at the time of placement. In this study an attempt is made to illustrate parameter optimization for compressive strength of different grades of concrete by using Taguchi method. The experiments were designed using an L16 orthogonal array (OA) technique with five parameters and four levels. They were water/binder ratio, cement content, super-plasticizer content, four different type of mineral admixtures (fly ash up to 50%, GGBS up to 60%, silica fume up to 15% and binary combination of fly ash and GGBS in ratio of 2/3 up to 50% overall; each replacement constitutes a different case) and fine to total aggregate ratio. Signal to noise ratio transformation and ANOVA have been applied to the results of experiments in Taguchi analysis. According to the ANOVA table, mineral admixture and water/binder ratio play significant roles for compressive strength of concrete mix. The confirmation tests corroborated the theoretical optimum test conditions. Regression analysis (linear and non-linear) and Artificial neural network technique have been used to predict the optimized compressive strength by models. Keywords: Parameter Optimization, Taguchi Method, Compressive Strength, Regression Analysis, Artificial Neural Network.