Modeling Slump of Ready Mix Concrete using Artificial Neural Network

Abstract

With rapid growth in the construction industry, Ready Mix Concrete (RMC) is playing a key role in offering high-quality customized concrete to contractors and builders. The workability of concrete involves early stage operations of concrete: placing, compaction, and finishing. Since RMC is manufactured at a plant and transported to the construction site, loss of workability is of prime concern due to the considerable time interval between mixing and placing of concrete. Workability of concrete is measured using a slump test to evaluate the life of the concrete during its transportation phase and the uniformity of the concrete from batch to batch. The proportions of cement, fly ash, coarse aggregates, fine aggregates, water, and admixtures in the concrete govern its workability or slump value. In this study, an Artificial Neural Networks (ANNs) learning from past examples gathered from a RMC plant were used to model the functional relationship between the input parameters and the slump value of concrete. The ANN model provided promising results compared to first-order and second-order regression techniques for modeling the unknown and complex relationships exhibited by the design mix proportions and the slump of concrete. The neural network synaptic weights that control the learning mechanism of ANN were further used to compute the percentage of relative importance of each constituent of RMC on the slump value, providing insight into the composite nature of concrete. The technique presented in the study will enable technical staff to quickly estimate the slump of RMC based on its design mix constituents without having to perform multiple design mix trials in order to achieve a customized slump value.