Predicting fresh and hardened properties of self-compacting concrete containing fly ash by artificial neural network model

Abstract

Self-compacting concrete (SCC) is a highly efficient concrete that can be compacted and formed under its own weight without external vibration. However, the constituents of SCC are many and they have diverse material properties. Hence, it is difficult to predict the working performance of SCC with a single factor regression relationship. Therefore, the artificial neural network (ANN) approach is chosen in the present work to simulate the relationship between proportions of constituents and properties of SCC. This paper aims at predicting properties of SCC containing fly ash based on the experimental data available from the literature. The eight input parameters in the proposed models include amounts of cement, water, water to powder ratio, binder, fly ash, coarse aggregate, fine aggregate, and superplasticizers. The four output parameters are V-funnel flow time, slump flow final spread diameter, compressive strength at 28 and 90 days. A procedure to select the number of hidden layer neurons is discussed. Moreover, the parametric analysis of the developed ANN model is conducted to evaluate the effect of input parameters on SCC properties. By comparing the estimated and experimental results, the proposed ANN model shows great potential in predicting the properties of SCC with different percentage volume fractions of fly ash.