Non-destructive prediction techniques for asphalt mixture based on back propagation neural networks

ABSTRACT

The density of the asphalt mixture surface is an important indicator to evaluate the quality of construction and is of great importance to ensure the quality of construction and maintain the long-term serviceability of the pavement. The 3D ground-penetrating radar (3D GPR) and density prediction model can achieve rapid and continuous prediction of the density of asphalt pavement. In this study, a back propagation neural network (BPNN) density prediction model was established and combined with 3D GPR to conduct field experiments on a new asphalt concrete mid-surface layer to compare the density values obtained by the coring method in order to verify the model performance. The results show that the BPNN prediction model established in this study has good application to the overall density diagnosis of the road in the field. The average prediction error of the BPNN density prediction model is 0.184%, which is highly reproducible in practical applications.

KEYWORDS:

• Asphalt pavement
• 3D ground-penetrating radar
• non-destructive testing
• backpropagation neural network
• density prediction model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 51968006].