Prediction of the Slump of Fly Ash Blended Concrete Based on Various Numerical Models

Abstract

Slump is a fundamental engineering property of fly ash blended concrete. This study proposes various numerical models, such as multiple linear regression (MLR), artificial neural network (ANN), and genetic algorithm assisted artificial neural network (GA-ANN) for estimating the slump. First, the concrete slump is regressed as a multiple linear equation of water-to-binder ratio, water content, sand ratio, fly ash replacement ratio, air-entraining agent content, and superplasticizer contents. The correlation coefficient of the MLR method is 0.63. Second, the ANN model is set up, which consists of an input layer, a hidden layer, and output layer. Based on the backpropagation (BP) training method, the optimized network is found. The correlation coefficient between analysis results and experimental results of ANN model is 0.78. Third, GA is used to assist in the optimization process of ANN. The initial value of the hidden layer of ANN is generated by using a genetic algorithm (GA). The correlation coefficient of GAANN integrated model is 0.85. GA-ANN integrated model can make more accurate prediction results than MLR model and ANN model.