Frontiers in Materials (Aug 2023)

Pavement dynamic monitoring data processing based on wavelet decomposition and reconfiguration methods

  • Shujie Shang,
  • Ming Liang,
  • Hao Wang,
  • Yuepeng Jiao,
  • Zhaoxin Liu,
  • Congwei Bi,
  • Fei Xu,
  • Runzhi Zhang,
  • Hongjie Li,
  • Yongfeng Zhao,
  • Zhanyong Yao

DOI
https://doi.org/10.3389/fmats.2023.1221385
Journal volume & issue
Vol. 10

Abstract

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Early damage to asphalt pavements generally occurs due to the increasing traffic flow and the loads of vehicles, coupled with alternating high- and low-temperature cycles, freeze–thaw cycles, ultraviolet radiation, and other harsh environments. Several types of distress, such as rutting, cracking, and other damage, deteriorate the serviceability of asphalt pavements and shorten the road service life. Thus, the long-term structural mechanical response of asphalt pavements under the influence of loads and the environment is crucial data for the road sector, which provides guidance about road maintenance. Effectively processing the pavement dynamic monitoring data is a prerequisite to obtain the dynamic response of asphalt pavement structures. However, the dynamic monitoring data of pavements are often characterized by transient weak signals with strong noises, making it challenging to extract their essential characteristics. In this study, wavelet decomposition and reconstruction methods were applied to reduce the noise of pavement dynamic response data. The parameters of the signal-to-noise ratio (SNR) and root mean square error (RMSE) were introduced to compare and analyze the effect of the decomposition of two different wavelet functions: the symlet (sym) wavelet function and the Daubechies (db) wavelet function. The results showed that both the sym and db wavelet functions can effectively obtain the average similarity information and the detailed information of the dynamic response signals of the pavement, the SNR after the sym wavelet fixed-threshold denoising process is relatively higher, and the RMSE is smaller than that of the db wavelet. Thus, wavelet transformation exhibits good localization properties in both the time and frequency domains for processing pavement dynamic monitoring data, making it a suitable approach for handling massive pavement dynamic monitoring data.

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