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Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

  • Dimitrios Bolkas EMAIL logo , Matthew O’Banion , Jordan Laughlin and Jakeb Prickett
Published/Copyright: June 14, 2024
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Abstract

Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.


Corresponding author: Dimitrios Bolkas, Department of Surveying Engineering, The Pennsylvania State University, Wilkes-Barre Campus, Dallas, PA, USA, E-mail: 

Acknowledgments

The authors would like to acknowledge David Williams and Brett Anderton, U.S. Army Corps of Engineers, for their assistance in this project. This project has received funding from Chancellor Endowment Funds, Penn State University, Wilkes-Barre Campus and the National Geospatial Intelligence Agency. In addition, undergraduate students Gregory Ellsworth, Hannah Corson, Gerald Rusek, Nick Lawler, Tanner Smith, and Tyler Pokrinchak are thanked for their contribution in the data collection. We also want to thank the anonymous reviewers for their excellent comments that have notably improved the original version of this manuscript.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-05-26
Accepted: 2024-05-22
Published Online: 2024-06-14
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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