Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions

Abstract

Summary A camera can theoretically capture visual information of all target surfaces within its field of view. This capability allows a camera to act as a sensor for determining motion or deformation of targets at appropriate distances. Considering that there are so many cameras in public society (including cameras in smartphones) that can record the vibration of structures in an earthquake, this capability will be very helpful for assessing structural seismic damage post‐earthquake. To this end, this paper proposes a two‐step combination of the SCF (support correlation filters) algorithm with the KLT (Kanade‐Lucas‐Tomasi) algorithm for displacement identification of cable‐stayed bridges from video with higher accuracy over SCF only and higher robustness over KLT only. The SCF algorithm can robustly and quickly track the target in the video, and the KLT algorithm can accurately track the pixel locations of targets. The shaking table test of a scale cable‐stayed bridge model was conducted, and the vibration of the tower was recorded by a camera. The linear and nonlinear displacement responses of the tower under different earthquake ground motions were identified. The accuracy of the identified displacement response was validated through comparison with measurements from laser displacement transducers.

A review of computer vision–based structural health monitoring at local and global levels

“…Structural behavior analysis Tian and Pan 128 : deflection profile of railway bridge Guo and Zhu 183 : sound barriers of a viaduct Deng et al 184 : deflection profile of suspension bridge model Ji et al 185 : flexural, shear, and sliding deformation behaviors of RC wall Load distribution estimation Dong 125 : distribution factor of a pre-stressed concreted girder bridge Load carrying capacity analysis Lee et al 187 : load capacity analysis of a steel girder bridge Dong 125 : load rating of a pre-stressed concreted girder bridge Dynamic analysis Xu et al 16 : cable-stayed footbridge Zhao et al 188 : tower of a scaled model of a cable-stayed bridge under earthquake loads Dong et al 149 : grandstand, stadium Ngeljaratan and Moustafa 189 : ABC bridge Modal identification Ji and Chang: stayed cable Chen et al 156,181 : cantilever beam Yang et al 194,195 : aluminum frame structure Fioriti et al 196 : ancient constructions such as buildings and bridges Tian et al 167 : beam Dong et al 149 : grandstand Hoskere et al 163 : suspension bridge (foot bridge) Harmanci et al 200 : steel frame Model updating Dong et al 201 : a pre-stressed concreted girder bridge Feng and Feng 192 : aluminum frame structure Damage detection…”

“…Xu et al 16 monitored the dynamic displacements of a cable-stayed footbridge under human crowds using zero-mean cross-correlation-based template matching methods and analyzed the instantaneous frequency and amplitude change when a large human crowd crossed the bridge. Zhao et al 188 combined the support correlation filter algorithm and Lucas–Kanade optical flow to estimate the vibration responses of the tower of the scaled model of a cable-stayed bridge under earthquake loads. Dong et al 149 monitored the displacement of a stadium under human jumping loads during a football game by integrating Lucas–Kanade optical flow and SIFT keypoints.…”

Machine learning paradigm for structural health monitoring

“…The technique was applied successfully to automatically generate the global mode shapes of a full-scale pedestrian bridge. Zhao et al 63 proposed a target-free method to identify the displacement of structures using a single camera based on a combination of support vector filter (SCF) and Kanade-Lucas-Tomasi feature point tracking. In this combination method, SCF utilized an equivalent form of an SVM model and introduced a circulant matrix to optimize the calculation efficiency, which ensured robustness of the measurement.…”

Computer vision‐based monitoring of the 3‐D structural deformation of an ancient structure induced by shield tunneling construction

“…It is difficult to apply a universal method for all of the application environments. For the field monitoring application, the typical strategy is to conduct laboratory tests to verify the proposed method and then apply it to a field test [35]. Ye et al [33, 36] proposed a method based on grayscale pattern matching for multiobject structural deformation monitoring and investigated the laboratory and field performance of the computer vision‐based method.…”