Mr. David Wamai Profile

David Wamai

at Univ. of South Carolina

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Publications (2)

Proceedings Article | 10 June 2024 Presentation + Paper

Highway slope monitoring using 3D laser scanning at different seasons

AQM Zohuruzzaman, David Wamai, Weicong Feng, Sadik Khan, Austin R. Downey, Jie Wei, Erik Blasch, Paul Schrader

Proceedings Volume 13037, 1303706 (2024) https://doi.org/10.1117/12.3016172

KEYWORDS: LIDAR, Point clouds, Soil moisture, Roads, Soil science, Principal component analysis, Deformation, 3D scanning, Rain, Image segmentation

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Proceedings Article | 7 June 2024 Presentation + Paper

Edge processing for frequency identification on drone-deployed structural health monitoring sensor nodes

Ryan Yount, Joud Satme, David Wamai, Austin R. Downey

Proceedings Volume 13055, 130550L (2024) https://doi.org/10.1117/12.3013712

KEYWORDS: Sensors, Structural health monitoring, Vibration, Data processing, Detection and tracking algorithms, Computer hardware, Unmanned aerial vehicles, Bridges, Structural monitoring, Profiling

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For rapid civil infrastructure assessment following natural and man-made emergencies, the utilization of minimally invasive and cost-effective drone deployable sensor packages has the potential to become a valuable tool. Although compact sensors with wireless data transfer capabilities have proven effective in monitoring the structural dynamics of infrastructure, these systems require data processing to occur externally, frequently off-site. These extra steps impede the high-speed assessment of a structure’s state. Difficulties can arise when the transmission is unfeasible due to degraded communication links during natural or man-made emergencies. Additionally, off-site data processing can add unneeded interruptions to actions that can be taken by emergency personnel after infrastructure damage. To enhance the effectiveness of sensor packages in expediting infrastructure assessment, incorporating real-time data analysis through embedded edge computing techniques emerges as a promising solution. The objective of this work is to demonstrate on-device data processing for frequency-based structural health monitoring techniques using drone-deployable sensors. This approach advances the effectiveness of drone-deployable sensors in rapid infrastructure assessment by mitigating their susceptibility to errors or delays in data communications. The proposed approach computes the frequency components of vibration measurements taken from a structure of interest, for example, the monitoring of a bridge immediately following a damaging event such as a flood. This work presents contributions in terms of outlining a methodology that emphasizes the hardware-based implementation of edge computing algorithms and examines the required on-device performance and resource utilization for structural health monitoring at the edge. The execution time for the sensor’s edge computing functions was profiled, resulting in an additional 9.77 seconds per test, an advancement over traditional transmit and analyze methods.

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