Evaluation of Compressive Strength of Sustainable Concrete Using Genetic Algorithm Assisted Artificial Neural Networks

Sustainable concrete which contains fly ash and slag is increasingly used in modern construction practices. This study presents a genetic algorithm (GA) assisted artificial neural network (ANN) model for evaluating the compressive strength of sustainable concrete. 425 mixtures are used for making the prediction system. Genetic algorithm (GA) is used to generate the initial values of the weight matrix and bias of ANN. The input parameter of GA assisted ANN is water-to-binder ratio, fly ash or slag replacement ratio, sand ratio, and water contents. The output result is compressive strength. The correlation coefficients for single ANN and GA assisted ANN model are 0.88 and 0.911, respectively. GA assisted ANN model has a strong prediction ability for the strength of sustainable concrete.