Recognition of hidden distress in asphalt pavement based on convolutional neural network

ABSTRACT

Ground penetrating radar (GPR) is widely used in nondestructive tests of asphalt pavements, but the characteristics of its detection data need to be extracted manually, and it is very difficult to determine the hidden distress efficiently and accurately inside the asphalt pavement. Therefore, to recognize hidden distress inside asphalt pavement intelligently and improve the efficiency and accuracy of GPR for asphalt pavement detection, this research used the convolutional neural network (CNN) algorithm combined with GPR detection technology. Significantly, the image of the dielectric properties distribution of the road was obtained by processing the GPR detection data, and the GPR detection image data set was established. Moreover, this research dealt with the imbalance of the data set by weighting the loss value. Next, this research designed a CNN model with a simple structure, called visual GPR (VGPR), and used the GPR image data set to train, validate, and test the VGPR. Finally, the model was fine-tuned according to the results, and the optimal hyperparameters were selected. The results show that VGPR converges fast in the training process, and no obvious overfitting phenomenon exists. During the test, the weighted average F1 score in the comprehensive metrics reached 99.626%, indicating that VGPR has excellent generalization performance. When VGPR uses a graphics processing unit for computing, the image recognition efficiency can meet the engineering requirements. A comparison of VGPR with other CNN models reveals that when using the same GPR detection image data set, VGPR has the best recognition effect. In summary, the method detailed herein can quickly and accurately identify the hidden distress of the asphalt pavement and provide a guarantee for the maintenance and operation of the expressway.

KEYWORDS:

• road detection
• distress recognition
• convolutional neural network
• hidden road distress
• ground penetrating radar

Acknowledgements

The authors acknowledge the financial support of the Key Research and Development Project of Science and Technology Department of Hubei Province of China (Project No. 2020BCA085) and the Science and Technology Project of Hubei Provincial Department of Transportation (Project No. 2020-2-1-2). Special thanks to the 1,000-Youth Elite Program of China for the start-up funds used for purchasing the laboratory equipment that was crucial to this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the Key Research and Development Project of Hubei Province of China under Grant [Project No. 2020BCA085] and Science and Technology Project of Hubei Provincial Department of Transportation under Grant [Project No. 2020-2-1-2].

Notes on contributors

Wenchao Liu

Wenchao Liu is currently a graduate research assistant with the School of Transportation and Logistics Engineering, Wuhan University of Technology, Hubei Province 430063, China. His research interests include ground penetrating radar and nondestructive detection.

Rong Luo

Rong Luo is currently a professor with the School of Transportation and Logistics Engineering, Wuhan University of Technology, Hubei Province 430063, China. Her research interests include asphalt mixtures.

Yu Chen

Yu Chen is currently a lecturer with the School of Transportation and Logistics Engineering, Wuhan University of Technology, Hubei Province 430063, China. Her research interests include asphalt mixtures and data analysis.

Xiaohe Yu

Xiaohe Yu is currently a graduate research assistant with the School of Transportation and Logistics Engineering, Wuhan University of Technology, Hubei Province 430063, China. His research interests include ground penetrating radar and nondestructive detection.