A novel evaluation method for pavement distress based on impact of ride comfort

ABSTRACT

Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance of the distress, which makes it difficult to assess its depth or impact on passengers’ experience. This paper proposes a data fusion-based method for pavement distress evaluation, which comprehensively considers the joint effect of distress physical appearance and the corresponding impact on riding comfort. A deep convolutional neural network was employed to automatically detect and locate the pavement distress using image data. A wavelet transform was applied to extract the acceleration effectuated by the defects in the frequency domain using vibration data. Finally, a comfort evaluation index was constructed based on the results of image and vibration data fusion. Furthermore, a mobile vehicle-mounted collective system was designed for rapid evaluation of the pavement distress, which integrated multiple distributed accelerometers, an industrial camera, and a graphics processing unit. The results demonstrated the stability and efficiency of the proposed approach, making it a potential tool to comprehensively evaluate the condition of pavement distress.

KEYWORDS:

• Pavement distress evaluation
• ride comfort
• data fusion
• convolutional neural network
• wavelet transform

Acknowledgments

The first author would like to acknowledge the support provided by Yifan Zhu and Wengzi Hang for collecting the data and my girlfriend, Jiefan Li, for revising this article. The authors are responsible for all the views and opinions expressed in this paper.

Disclosure statement

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

Additional information

Funding

This study was partly supported by the National Natural Science Foundation of China (Grant No. 51978519), and partly supported by Joint Science Foundation of Ministry of Education of China & CMCC (No. 2018202004).