Modelling Slump of Concrete Containing Natural Coarse Aggregate from Bida Environs Using Artificial Neural Network

Abstract

Consumption of crushed granite as coarse aggregate in concrete has led to devastating environmental and ecological consequences. In order to preserve local and urban ecology therefore, substitute aggregate such as naturally occurring stone with the propensity of reducing this problem was studied. Furthermore, artificial Neural Network (ANN) models have become the preferred modeling approach due to their accuracy. Thus, in this paper, MATLAB software was used to develop ANN models for predicting slump of concrete made using Bida Natural Gravel (BNG). Four model architectures (5:5:1; 5:10:1; 5:15:1 and 5:20:1) were tried using a back-propagation algorithm with a tansig activation function. The performance of the developed models was examined using Mean Square Error (MSE), Correlation Coefficient (R) and Nash-Sutcliffe Efficiency (NSE). Results showed that 5:20:1 model architecture with MSE of 8.33e-27, R value of 98% and NSE of 0.96 was the best model. The chosen 5:20:1 ANN model also out performed Multiple Linear Regression (MLR) model which recorded MSE of 0.83, R value of 88.68% and NSE of 0.87. The study concluded that the higher the neuron in hidden layer of ANN slump model for concrete containing BNG, the better the model.