文章基于计算机视觉技术拍摄的一系列结构振动图像, 运用数字图像相关( DIC) 技术荻取监则节点的亚像素振动位移数据, 在此基础上, 采用快速贝叶斯 FFT 方法对被则试结构的动力模态参数进行辨识｡ 为了验证计算机视觉结构振动则试和快速贝叶斯 FFT 方法相结合在结构模态参数识别中的有效性和准确性, 以某实验室5 6 m 跨钢析架模型的振动视频为算例, 十对结构有无构件损伤等6 种不同工况条件下的振动视频数据,进行振动位移的提取, 进而分析析架不同位置处监则节点识别位移与实际位移之间产生的误差大小及其原因,并借助快速贝叶斯 FFT 方法, 对从不同部位采集的振动视频数据进行结构动力模态参数识别结果的精确度和不确定性对比分析｡ 实验结果表明, 文章将 DIC 位移则量技术和贝叶斯 FFT 方法相结合, 能够有效实现对结构动力模态参数的精准识别｡

This paper discusses the utilization of computer vision technology to capture a series of structural vibration images. The resulting sub-pixel vibration displacement of the monitoring nodes are then obtained by Digital Image Correlation ( DIC) technology, the fast Bayesian FFT method is then adopted to identify the dynamic modal parameters of the tested structure. To assess the precision and reliability of the dynamic modal parameter identified for a structure, a combination of computer vision based vibration test and fast Bayesian FFT methodology is conducted in this paper. A 5 6 m steel truss is adopted as an example to extract its vibration displacement data from captured videos for the undamaged and other 5 damaged conditions of the test structure. The size of the errors and reasons in the identified displacement of monitoring nodes at various locations in the truss are analyzed in this study. Additionally, the fast Bayesian FFT method is adopted to evaluate the accuracy and uncertainty of the identified dynamic modal parameters by investigating the vibration video data extracted from different monitoring points in the tested structure. The results from the proposed method show that the utilization of DIC technology and fast Bayesian FFT can accurately identify dynamic modal parameters of the test structure.