School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China.
Sensors (Basel). 2020 Feb 21;20(4):1187. doi: 10.3390/s20041187.
To further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer-vision-based full-field non-contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full-field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification.
为了进一步提高结构健康监测技术和大型结构理论的精度和效率，全场非接触结构几何形态监测有望成为结构安全状态监测和数字孪生的突破性技术，因为它具有经济、可靠、高频和全像的优势。本研究在基于计算机视觉的全场非接触式位移和振动测量中验证了一种所提出的全像视觉传感器和算法。利用自动相机巡逻实验装置，采集了模型桥在各种损伤/活动下的原始分段动态和静态视频监测数据。根据序列数据的时空特征，引入了全像几何形态跟踪算法。此外，建立了结构全像几何和全像特征轮廓的特征点集。实验结果表明，全像视觉传感器和所提出的算法可以提取准确的全像全场位移信号，并真实、敏感地完成振动测量，同时准确反映各种损伤/作用条件下结构特性的实际变化。该方法可以为大型结构的数字孪生、结构状态评估和智能损伤识别的进一步研究奠定基础。
