Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network

Abstract

AbstractThe use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical mechanical parameters used in many design codes can be obtained either through costly and time‐consuming experiments or non‐destructive methods such as predictive models. In this study, a hybrid model of the radial basis function neural network (RBFNN) and firefly optimization algorithm (FOA) (RBFNN + FOA) was proposed for the prediction of the compressive strength of self‐compacting concrete (SCC) containing CFFA. For this purpose, a multi‐laboratory dataset containing 327 SCC samples made with CFFA was employed. The input parameters in the proposed models included the age of the specimen and the amounts of cement, water, CFFA, coarse aggregate, fine aggregate, and superplasticizer, and the only output parameter was the compressive strength of SCC. For comparison purpose, the artificial neural network (ANN) was also used to model the compressive strength of SCC containing CFFA. The results show that the compressive strength estimated by the proposed and ANN models have good accuracies compared to the experimental results. Moreover, several performance metrics showed that the accuracy of the proposed model is higher than that of the model developed based on ANN algorithm.