Change of Strength of Recycled Concrete with Age

Abstract

In this paper, a mathematical model of the relationship between the amount of various components of recycled concrete and its compressive strength is established by using a large number of experimental data on the strength performance of recycled concrete and BP artificial neural network technology. In this paper, the influence trend of water binder ratio and aggregate substitution rate on the strength of recycled concrete is studied by this model. Taking recycled concrete with different recycled aggregate as the research object, a nonlinear structural model reflecting the mapping relationship between mix proportion and strength of recycled concrete is established in this paper. In this paper, the predicted values of the model are compared with the experimental data. The research shows that the strength model of recycled concrete based on BP neural network established in this paper can predict the compressive strength of recycled concrete at the corresponding age according to the mix proportion of recycled concrete, and the prediction accuracy is high. The application of BP neural network effectively reduces the times of a large number of repeated mix proportion test when preparing recycled concrete in practical engineering, reduces the waste of human and material resources, and has good economic benefits.