Application of Unmanned Aerial Vehicle (UAV) for Reservoir Embankment Inspections
Publication: Geo-Congress 2023
ABSTRACT
Reservoir embankments are built to retain water for various needs including water supply, flood damage reduction, flow channelization, and safeguarding lives and property. Due to the critical nature of these infrastructure assets, they are frequently monitored and maintained to ensure continued functionality and compliance with the safety guidelines. The routine traditional inspection practices are subjective to the inspector’s experience. The advancements in unmanned aerial vehicle (UAV) platforms and lightweight sensors have provided a solution to conduct proactive monitoring of these assets. A reservoir, located in restricted airspace, was inspected using a UAV equipped with an optical camera to conduct qualitative and quantitative condition assessments. Multiple flight missions were conducted at different flight altitudes to obtain high-resolution data of the embankment and the coverage of surrounding areas. Multiple 3D mapping products of the reservoir were built by processing the same set of aerial images collected using photogrammetric techniques. The stabilized soil embankment was monitored quantitatively to assess the distress conditions. Further, the visualization of the 3D models provided multiple views of the inlet pump, aeration systems, and overflow/emergency spillway and an immersive experience to the inspector, which could have been either prohibitively costly or physically impossible, or a combination of the two, to achieve using traditional inspection practices. The 3D model developed in this study will also be used as a comprehensive base map for timeline monitoring of these assets. This study will provide an idea about best practices for planning, collection, and processing of aerial imagery, which will be of interest to the agencies managing dam and levee assets.
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Published online: Mar 23, 2023
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- Zachary H. Nick, Joe G. Tom, LinBin Zhang, Considerations for Augmented Flood Control Infrastructure Inspection Using Convolutional Neural Networks, Geo-Congress 2024, 10.1061/9780784485347.035, (345-353), (2024).