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Identifying coastal highway pavement anomalies using multiscale wavelet analysis in radar signal interpretation

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Abstract

Highways are the main components of modern transportation hubs, where lots of pavement anomalies have been still occurring with the increasing of the traffic volume and overloading, especially for the coastal developed areas. Rapid nondestructive detection is a necessary means to ensure uninterrupted highway traffic in the road operation period. In this paper, MALA Ground-penetrating Radar is used for detecting the coastal highway pavement anomalies, while the radar signal interpretation is vulnerable to environmental influences and relies too much on experience judgments from engineers. In order to improve the interpretation accuracy, the continuous wavelet transforms are introduced for analyzing multiscale characteristics of the radar signals. Parametric and non-parametric test methods are combined to validate the availability of the processed signals with Reflexw and denoised signals with continuous wavelet transforms. The results show that major pavement anomalies are basically consistent with the radar detection and field survey. These qualitative and quantitative methods for revealing the central location of pavement anomalies, especially for depth, is of importance to assist engineers with reasonable interpretation. Moreover, the accuracy of pavement anomalies determining can be improved from decimeter to millimeter magnitude. Meanwhile, a comprehensive and intuitive interpretation method is provided for engineers combining with one-dimensional and two-dimensional analysis.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (42176224), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070504) and the Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology (2021B1212040003). All the sources of support are gratefully acknowledged. We would like to thank the handling editors and anonymous reviewers for their constructive suggestions that improve the paper quality.

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Authors and Affiliations

  1. State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Jiehui Xie, Fujun Niu, Wenji Su & Yunhui Huang

  2. TransportationSouth China Institute of Geotechnical Engineering, School of Civil Engineering and Transportation, South China University of Technology, 510640, Guangzhou, People’s Republic of China

    Jiehui Xie, Fujun Niu, Wenji Su & Yunhui Huang

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  1. Jiehui Xie
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Correspondence to Fujun Niu.

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Xie, J., Niu, F., Su, W. et al. Identifying coastal highway pavement anomalies using multiscale wavelet analysis in radar signal interpretation. J Civil Struct Health Monit 13, 49–65 (2023). https://doi.org/10.1007/s13349-022-00595-z

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  • DOI: https://doi.org/10.1007/s13349-022-00595-z

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