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UAV-based smart rock localization for bridge scour monitoring

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Abstract

Since 2015, smart rocks with embedded magnets have proven promising to measure the maximum scour depth around a bridge pier. However, the field tests from a magnetometer installed on a truck-mounted crane moving across a bridge deck were time-consuming and caused a safety concern associated with traffic congestion. In this study, an unmanned aerial vehicle (UAV) is proposed as a mobile station for the measurement of magnetic fields of the Earth, a bridge, and a smart rock. The UAV was equipped with a three-axis, high-resolution magnetometer and a global positioning system (GPS). The magnetometer and the GPS were integrated to provide a synchronized measurement of magnetic fields and their corresponding coordinates. The effects of UAV speed, motor current and GPS accuracy on the accuracy of smart rock positioning were investigated in an open field. As an important parameter of the rock localization algorithm, the ambient magnetic field around a bridge site was numerically simulated under the Earth’s magnetic field, taking into account the distribution of steel reinforcing bars in bridge piers. The computational results are in good agreement with experimental data with less than 3.75% in error. Six field tests at the Roubidoux Creek Bridge since 24 January 2018 has demonstrated an approximately 0.3 m accuracy of the smart rock positioning. During the February 2019 flood event, the existing scour hole was deepened by 0.4 m. The proposed UAV-based smart rock positioning method was compared favorably with the previous crane-based positioning method.

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Acknowledgements

Financial support for this study is provided by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) under Grant No. 69A3551747126 through INSPIRE University Transportation Center (http://inspire-utc.mst.edu) at Missouri University of Science and Technology. The views, opinions, findings and conclusions reflected in this publication are solely those of the authors and do not represent the official policy or position of the USDOT/OST-R, or any State or other entity. The authors would like to thank additional project team members for their input during field tests, including Yanping Zhu, Zepeng Chen, Cole Distler, and Dr. Gang Wang. The authors also thank Chao Liu for his assistance in three-dimensional electromagnetic analysis.

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Authors and Affiliations

  1. Missouri University of Science and Technology, Rolla, USA

    Haibin Zhang, Zhaochao Li, Genda Chen, Alec Reven & Buddy Scharfenberg

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Haibin Zhang & Jinping Ou

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  1. Haibin Zhang
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  2. Zhaochao Li
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  3. Genda Chen
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  4. Alec Reven
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  5. Buddy Scharfenberg
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  6. Jinping Ou
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Correspondence to Genda Chen.

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Zhang, H., Li, Z., Chen, G. et al. UAV-based smart rock localization for bridge scour monitoring. J Civil Struct Health Monit 11, 301–313 (2021). https://doi.org/10.1007/s13349-020-00453-w

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  • DOI: https://doi.org/10.1007/s13349-020-00453-w

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