A Three-Stage Neural Model for Attribute Based Classification and Indexing of Fly Ashes

Abstract

The primary objective of this work is to categorize the available fly ashes in different parts of the world into distinct groups based on its compositional attributes. Kohonen's self-organizing feature map and radial basis function networks are utilized for the classification of fly ashes in terms of its chemical parameters. The basic procedure of the methodology consists of three stages: (1) apply self-organizing neural net and delineate distinct groups of fly ashes and identify the group sensitive attributes; (2) find mean values of sensitive attributes of the elicited groups and augment them as start-up prototypes for k-means algorithm and find the refined centroids of these groups; (3) incorporate the centroids in a two layer radial basis function network and refine the delineation of the groups and develop an indexing equation using the weights of the stabilized network. Further, to demonstrate the utility of this classification scheme, the so formed groups were correlated with their performance in High Volume Fly Ash Concrete System [HVFAC]. The categorization was found to be excellent and compares well with Canadian Standard Association's [CSA A 3000] classification scheme.