Optimization of the rheological properties of self compacting concrete using neural network and genetic algorithm

Self compacting concrete (SSC) is one of the most useful innovations in concrete technology that has the ability to flow efficiently and maintain material homogeneity. However, additives particularly admixtures introduced in the production of SSC to enhance some specific properties of fresh and hardened concrete may contribute undesirable effects on the workability performance. In this study, superplasticizers blended with fly ash was used in the mix and were tested for Slump Flow, L-Box, and Screen Stability tests to determine its influence on the rheological properties of SCC. Several mixtures were tested in order to derive a mix proportion having the optimum rheological properties. Artificial neural network and genetic algorithm were used to determine the concrete mix proportion that will provide the best workability. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance.