Mix design of equal strength high volume fly ash concrete with artificial neural network

Abstract

High volume fly ash concrete (HVFAC) has been widely used, and the mix proportion of HVFAC is usually designed based on the required compressive strength. Therefore, the research of HVFAC under the condition of equal strength has more engineering application value. However, in the current mix proportion design standard, there is no special discussion on the mix proportion design method of HVFAC. In this study, the compressive strength of HVFAC affected by four factors (water-binder ratio, FA content, curing method and curing age) was systematically studied. Based on 594 sets of strength data obtained from experiments, the method to determine the key mix proportion parameters (water-binder ratio and FA content) of equal strength HVFAC was proposed through machine learning algorithms (ML). Two ML methods (i.e., a multiple linear regression (MLR) and an artificial neural networks (ANN)) were developed to predict the HVFAC compressive strength and then their performances were compared. Through comparison, it was found that the mean absolute error and root mean squared error of the ANN model were both lower than the MLR model, and the ANN model has lower error and higher accuracy. In addition, the reliability of the proposed ANN model was verified with data in other literatures, the results showed the errors between the predicted values and the measured values are lower than 25%. The weight contribution rate of each factor to the strength was calculated. Among the four factors, the curing age had the greatest impact on the compressive strength, which contributes up to 60.3%. The influence degree of FA content is greater than that of water-binder ratio. With the development of curing age, the influence degree of FA content and water-binder ratio on the strength gradually decreases while the curing method increases. Finally, according to the ANN prediction results, the key mix parameters design method for equal strength HVFAC under multi-factor conditions was proposed, which provide guidance for concrete preparation in actual engineering.