Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 1
Abstract
Ground penetrating radar (GPR) is a very useful nondestructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction. This paper presents a novel robotic system to automate the GPR data-collection process, localize underground utilities, and interpret and reconstruct the underground objects for better visualization, allowing regular nonprofessional users to understand the survey results. This system is composed of three modules: (1) an omnidirectional robotic data-collection platform that carries a RGB-D camera with inertial measurement unit (IMU) and a GPR antenna to perform automatic GPR data collection and tag each GPR measurement with visual positioning information at every sampling step, (2) a learning-based migration module to interpret the raw GPR B-scan image into a two-dimensional (2D) cross-section model of objects, and (3) a three-dimensional (3D) reconstruction module, i.e., 30.0% GPRNet, to generate underground utility model represented as fine 3D point cloud. Comparative studies were performed on synthetic data and field GPR raw data with various incompleteness and noise. Experimental results demonstrated that our proposed method achieves a higher GPR imaging accuracy in mean intersection over union (IoU) than the conventional back-projection (BP) migration approach, and less loss in Chamfer distance (CD) than point cloud model reconstruction baseline methods. The GPR-based robotic inspection provides an effective tool for civil engineers to detect and survey underground utilities before construction.
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Data Availability Statement
All GPR data and models are available for noncommercial use, and all the code that support the findings of this study are available from the corresponding author upon request.
Acknowledgments
Financial support for this study was provided by US National Science Foundation (NSF) Grant No. IIP-1915721, and by the US Department of Transportation under Grant No. 69A3551747126 through INSPIRE University Transportation Center 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. J Xiao has significant financial interest in InnovBot LLC, a company involved in R&D and commercialization of the technology.
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Information & Authors
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Published In
Journal of Computing in Civil Engineering
Volume 37 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 29, 2022
Accepted: Aug 10, 2022
Published online: Oct 12, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 12, 2023
ASCE Technical Topics:
- Automation and robotics
- Communication systems
- Construction engineering
- Data collection
- Engineering fundamentals
- Field tests
- Infrastructure
- Lifeline systems
- Methodology (by type)
- Models (by type)
- Research methods (by type)
- Special condition construction
- Subsurface utilities
- Systems engineering
- Tests (by type)
- Three-dimensional models
- Two-dimensional models
- Underground construction
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Cited by
- Jinglun Feng, Liang Yang, Jizhong Xiao, Subsurface Object 3D Modeling Based on Ground Penetration Radar Using Deep Neural Network, Journal of Computing in Civil Engineering, 10.1061/JCCEE5.CPENG-5359, 37, 6, (2023).
- Li Zeng, Xiaobing Zhang, Xiongyao Xie, Biao Zhou, Chen Xu, Sébastien Lambot, Measuring annular thickness of backfill grouting behind shield tunnel lining based on GPR monitoring and data mining, Automation in Construction, 10.1016/j.autcon.2023.104811, 150, (104811), (2023).