Exploring Concrete Slump Model Using Artificial Neural Networks

Abstract

Fly ash and slag concrete (FSC) is a highly complex material whose behavior is difficult to model. This paper describes a method of modeling slump of FSC using artificial neural networks. The slump is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. The model built was examined with response trace plots to explore the slump behavior of FSC. This study led to the conclusion that response trace plots can be used to explore the complex nonlinear relationship between concrete components and concrete slump.