Accurate structural displacement monitoring by data fusion of a consumer-grade camera and accelerometers
The displacement responses of bridge structures are critical both for structural health monitoring and structural safety evaluation. Using vision-based sensors to measure structural displacement responses is an effective method and has attracted considerable attention owing to its advantages of achieving non-contact, time-saving, and full-field measurements. However, there are several obstacles, including the limited frame rate, insufficient accuracy in large-scale measurements, and camera instability, which prevent the application of vision-based sensors. To overcome the shortcomings and improve the accuracies of the vision-based sensors, a displacement monitoring system combined with a consumer-grade camera and accelerometers was developed. Accelerometers attached to the target structures were used to reconstruct the dynamic displacement for data fusion with the vision-based displacement, while an accelerometer attached to the camera was used for camera vibration cancellation. Dynamic loading tests on a self–anchored suspension model bridge were conducted and results of the proposed system, the linear variable differential transformers, and a conventional vision-based system were compared. Field tests on a steel–concrete composite continuous beam bridge and a cable-stayed railway bridge were conducted, and the potential of the proposed system for use in real structures was validated.
» Author: Tong Wu, Liang Tang, Shuai Shao, Xiangyu Zhang, Yijun Liu, Zhixiang Zhou, Xiaolei Qi
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737