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Beam Deflection Monitoring Based on a Genetic Algorithm Using Lidar Data.
Michael Bekele Maru1, Donghwan Lee2, Gichun Cha2
1School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea.
Sensors (Basel, Switzerland)
|April 16, 2020
Summary
This study uses Terrestrial Laser Scanning (TLS) to measure structural element deflection. A genetic algorithm optimizes accuracy for precise damage detection and shape change analysis in structural health monitoring.
Area of Science:
- Engineering
- Geospatial Technology
Background:
- Light Detection and Ranging (LiDAR) is crucial for structural health monitoring.
- Terrestrial Laser Scanning (TLS) offers 3D data acquisition for assessing structural integrity.
Purpose of the Study:
- To enhance TLS application for damage detection and shape change analysis.
- To introduce a method for estimating structural element deflection using LiDAR.
- To improve the accuracy of deflection measurements.
Main Methods:
- Utilized Terrestrial Laser Scanning (TLS) for 3D data acquisition.
- Employed a genetic algorithm for robust deflection measurement.
- Optimized genetic algorithm parameters using Taguchi experiment design.
Main Results:
- Successfully estimated deflection of a simply supported beam under artificial load.
- Demonstrated enhanced accuracy in deflection measurement through genetic algorithm optimization.
- Identified the optimal plane with minimum error for data analysis.
Conclusions:
- TLS combined with a genetic algorithm provides an accurate method for structural deflection estimation.
- The optimized approach enhances damage detection and shape change analysis in structural health monitoring.
- This research validates the effectiveness of LiDAR-based techniques for structural assessment.
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