Yongchao Yang
Papers
2
Total Citations
68
H-Index
2
About
Yongchao Yang is a leading researcher in structural health monitoring and computer vision, whose work bridges the gap between traditional structural dynamics and modern sensing technologies. His primary research areas include compressed sensing for structural diagnosis, full-field vibration analysis from video, and the use of cost-effective vision sensors for infrastructure assessment. Yang's most influential work, "Robust data transmission and recovery of images by compressed sensing for structural health diagnosis" (2016), has garnered 55 citations and demonstrates how digital cameras—including those on unmanned aerial vehicles and robotic agents—can efficiently capture and transmit structural condition data. He further advanced the field with his 2018 framework for identifying full-field structural dynamics from image sequences under non-ideal conditions, a method that automatically extracts natural frequencies, damping ratios, and mode shapes from video. This work has the potential to dramatically reduce the time and resources needed for experimental modal analysis. Yang's innovative integration of computer vision with structural engineering is paving the way for more accessible, automated, and cost-effective infrastructure monitoring solutions.
Research Focus
Key Achievements
Top Papers
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