Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network

A back propagation (BP) neural network (NN) model was used to analyze the relationship between the cube compressive strength and various strength indicators of concrete with large-sized recycled aggregates (LSRA) (80 mm maximum size). Factors such as strength and replacement rate of recycled aggregates were used as input parameters to establish the neural network model. The BP-NN model was optimized by analyzing the influence and sensitivity of each parameter in the model. Then the mechanical properties of concrete with LSRA were predicted. Results showed that the strength of new concrete had a more significant impact on the strength of recycled concrete with LSRA, followed by the strength of old concrete. While considering all the factors, including the mechanical strength and the replacement ratio regarding the maximum utilization of RA, the 30% incorporation rate was suggested as an ideal incorporation rate.

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