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

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

JOURNAL=Frontiers in Materials

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1221385

DOI=10.3389/fmats.2023.1221385

ISSN=2296-8016

ABSTRACT=The early damage of asphalt pavements generally occurs, due to the increasing traffic flow and the loads of vehicles, coupled with alternating high and low temperatures cycles, freeze-thaw cycles, ultraviolet radiation, and other harsh environments. The distress such as rutting, cracking and other damage, deteriorates the serviceability of asphalt pavements and shortens road service life. Thus, the long-term structural mechanical response of asphalt pavements under the influence of loads and the environment is the crucial data for the road sector, which provides the guidance for 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 noise, making it challenging to extract their essential characteristics. In this study, the 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 the root mean square error (RMSE) were introduced to compare and analyze the effect of the decomposition of two different wavelet functions, i.e. the sym wavelet function and the db wavelet function. The results showed that both the sym and db wavelet functions can effectively obtain the average similarity information and 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 compared with that of 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.