Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

Abstract

The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio. The methodology utilizes the universal function approximation ability of ANN for imbibing the subtle relationships between the input and output variables and the stochastic search ability of GA for evolving the initial optimal weights and biases of the ANN to minimize the probability of neural network getting trapped at local minima and slowly converging to global optimum. The performance of hybrid model (ANN-GA) was compared with commonly used back-propagation neural network (BPNN) using six different statistical parameters. The study showed that by hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved. The trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions.