Using a hybrid artificial intelligence method for estimating the compressive strength of recycled aggregate self-compacting concrete

Abstract

Nowadays, because of human activities, the earth's environment is in danger. So, all industries including construction and building industries should be concerned about the environment, and their activities have to be in this direction. Using waste materials in construction is one of the solutions for decreasing environmental damage, for example; using recycled aggregate in various types of concrete including self-compacting concrete (SCC). Moreover, the usage of a non-destructive method for determining the mechanical properties of concrete can improve the environmental situation. To do so, in this study the radial basis function neural network (RBFNN) assisted by firefly algorithm (FA) is proposed for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). In these regards, the information of 310 samples of RASCC has been collected from previous studies. In this model, the water to binder ratio, age, the ratio of recycled coarse aggregate, coarse aggregate, fine aggregate, and superplasticizer have been considered as input variables, and compressive strength as the output variable. Also, an ANN model has been utilized to conduct comparisons. The performance of the models has been evaluated based on statistical parameters, and by comparing the results of the model with experimental results. The values of statistical parameters of RBFNN model (for all data: R-values:0.97 & RMSE:3.3) show that the correlation between results of the model and experimental results are high and the error of the model’s results are acceptable. Moreover, results of the models indicate that both models have good ability and acceptable accuracy for predicting the compressive strength of RASCC.