UAV-Enabled Subsurface Characterization Using Multichannel Analysis of Surface Waves
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 147, Issue 11
Abstract
The implementation of unmanned aerial vehicles (UAVs) in civil engineering has primarily focused on remote sensing using optical cameras, light detection and ranging (LiDAR), and communicating with other sensing systems such as wireless sensor networks. However, UAV-enabled methods for subsurface characterization are still at their naissance. In this study, a UAV was used to lift and drop a mass onto the ground surface. The mass’s impact on the ground was used as the impulsive source for multichannel analysis of surface waves (MASW) testing. For this implementation, a hexarotor UAV was modified to lift and drop 4–7 kg masses to generate Rayleigh surface waves. The frequency domain characteristics of the seismic waves generated by UAV-dropped masses were compared to conventional MASW testing using a sledgehammer. The MASW method using a UAV-deployed source developed herein was applied to a soil site and a solid waste site. It was demonstrated that the UAV-dropped mass can generate Rayleigh surface waves below 10 Hz more effectively than the sledgehammer and could therefore support shear wave velocity profiling to greater depths in the subsurface, possibly without the need for signal stacking. Recommendations for drop mass selection and a discussion of observations made by the authors are also provided. Contributions made in this study are intended to contribute toward remote, fully autonomous subsurface mapping coupled with image-based surface mapping that will be critical for remote site characterization in postearthquake geotechnical reconnaissance and will allow the acquisition of more extensive subsurface data than presently feasible.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Science Foundation (NSF) under Grant No. CMMI-1362975. Any opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF. The authors would like to thank graduate students Hao Zhou, Cassandra Champagne, Michelle Basham, Goldie Gunawan, William Medwedeff, Gabriel Draughon, and Chenghang Li for assistance with field work.
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Journal of Geotechnical and Geoenvironmental Engineering
Volume 147 • Issue 11 • November 2021
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© 2021 American Society of Civil Engineers.
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Received: Apr 14, 2020
Accepted: May 14, 2021
Published online: Aug 27, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 27, 2022
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