Using artificial neural networks to predict the compressive strength of sustainable self-consolidating concrete

Abstract

Self-consolidating concrete (SCC) in which significant amount of Portland cement is replaced with fly ash and silica fume is gaining popularity. The process of manufacturing cement is known to contribute significantly to the emission of carbon dioxide into the atmosphere. Therefore, a concrete mix in which significant amount of cement is replaced with a sustainable alternative is known as sustainable concrete. The purpose of this paper is to present an artificial neural network (ANN) to predict the compressive strength attainable by concrete mixes. The fundamental ANN parameters considered to affect the compressive strength include the water-to-binder ratio, the amounts of high range water reducer, silica fume, fly ash, course aggregate, and fine aggregate. A set of data from the literature is used to train the ANN and the results are validated using data produced in the laboratory by the investigators. © Civil-Comp Press, 2015.

Keywords: Artificial Neural Networks, Fly Ash, Self-Consolidating-Concrete, Silica Fume, Sustainable Concrete, Water-Cement-Ratio