The Binder Index – A Parameter That Influences the Strength of Geopolymer Concrete

Geopolymer concrete (GPC) is an environmentally friendly material in the sense that it uses industrial by-products such as ground granulated blast furnace slag (GGBS) and fly ash (FA), which are activated by an alkaline solution. This paper presents an experimental investigation concerning the strength of the GPC and its relation to a new parameter called the ‘Binder Index (BI)’. The parameters considered in the investigation include GGBS to fly ash ratios (0.25 0.43, 0.67, 1.0, 1.5, and 2.3) and the molarity of the alkaline activator (6, 8, 10, and 12). The binder index combines the effect of the GGBS to the fly ash ratio and the molarity of the alkaline activator. The results have shown that the strength of the GPC is significantly influenced by varying the binder index. The results indicate that a nonlinear relation exists between the binder index and the compressive strength of the GPC and the binder index and the modulus of rupture

Automatic Feature Subset Selection using Genetic Algorithm for Clustering

Abstract: Feature subset selection is a process of selecting a subset of minimal, relevant features and is a pre processing technique for a wide variety of applications. High dimensional data clustering is a challenging task in data mining. Reduced set of features helps to make the patterns easier to understand. Reduced set of features are more significant if they are application specific. Almost all existing feature subset selection algorithms are not automatic and are not application specific. This paper made an attempt to find the feature subset for optimal clusters while clustering. The proposed Automatic Feature Subset Selection using Genetic Algorithm (AFSGA) identifies the required features automatically and reduces the computational cost in determining good clusters. The performance of AFSGA is tested using public and synthetic datasets with varying dimensionality. Experimental results have shown the improved efficacy of the algorithm with optimal clusters and computational cost. Key words: feature subset selection, Genetic Algorithm and clustering