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Estimation of ground cavity configurations using ground penetrating radar and time domain reflectometry

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  • Won-Taek Hong

    (Korea University)

  • Jong-Sub Lee

    (Korea University)

Abstract

Ground cavity configurations, including depth, roof shape, and length, are the main factors affecting the risk of ground sinkholes. In this study, ground penetrating radar and time domain reflectometry are applied to estimate the ground cavity configurations. To accurately estimate ground relative permittivity with depth, a penetrometer incorporated with a time domain reflectometry (PTDR) system is developed. In addition, a new method is established to calculate the coordinates of the reflection points. Experimental studies are conducted on ground models prepared in different soil types with buried objects of various shapes to simulate ground cavities by using circular rubber tubes and rectangular and trapezoidal polystyrene objects. The experimental studies show that the estimated depths of the buried objects are identical to the experimental setup. The estimated roof shapes clearly represent the roof shapes of the buried objects. In addition, the estimated diameters of the rubber tubes and the estimated lengths of the polystyrene objects show good agreement with those of the buried objects. This study shows that the ground penetrating radar survey, PTDR test, and the new method for estimating ground cavity configurations may be effectively used to assess the risk of ground sinkholes.

Suggested Citation

  • Won-Taek Hong & Jong-Sub Lee, 2018. "Estimation of ground cavity configurations using ground penetrating radar and time domain reflectometry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1789-1807, July.
    • Handle: RePEc:spr:nathaz:v:92:y:2018:i:3:d:10.1007_s11069-018-3278-z
    DOI: 10.1007/s11069-018-3278-z
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    Cited by:

    1. Feifei Hou & Xu Liu & Xinyu Fan & Ying Guo, 2022. "DL-Aided Underground Cavity Morphology Recognition Based on 3D GPR Data," Mathematics, MDPI, vol. 10(15), pages 1-18, August.

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