Measurement of full-field displacement time history of a vibrating continuous edge from video

Bhowmick, Sutanu; Nagarajaiah, Satish; Lai, Zhilu

doi:10.1016/j.ymssp.2020.106847

Cited by 75 publications

(44 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Basic optical flow‐based technique 28 and robust subpixel orientation code matching (OCM) algorithm 29 are recently developed for motion estimation of a cable. Video‐based motion estimation combined with blind source separation techniques are used for structural response estimation in Yang et al 30 Modal identification and the constant tension force estimation of the cable are executed using phase‐based motion estimation in Yang et al 30 Tension force in cable can also be calculated using a recently published full‐field edge response estimation algorithm using static camera 31,32 . Vision‐based measurements give rise to big data, which is beneficial for intensive structural health monitoring of such bridges 33,34 …”

Section: Introductionmentioning

confidence: 99%

Computer vision‐based real‐time cable tension estimation in Dubrovnik cable‐stayed bridge using moving handheld video camera

Jana

Nagarajaiah

2021

Struct Control Health Monit

Self Cite

View full text Add to dashboard Cite

Summary Cables are essential components of the cable‐stayed bridges as they serve as the main load‐bearing component. Hence, continuous monitoring of such cables becomes necessary as they are vulnerable to the fatigue damage induced by dynamic loads. Sensors are attached to the cables to examine the health of the cables; however, these contact‐based sensors can malfunction in harsh weather condition, which makes impossible to estimate the cable health in such unfavorable condition. Therefore, in this paper, we propose a completely noncontact video‐based stay‐cable tension measurement technique where the video is recorded using a moving handheld camera at a significant distance from the structure itself. Here, the cable tension is determined from vibration‐based measurement, but the vibration of the cable recorded in the video includes the true vibration of the cable along with the camera motion. Hence, we amalgamated a series of image processing techniques to nullify the camera movement. First, we detect the camera movement based on the movement of the bridge deck and pylon, which are fixed objects, using Kanade–Lucas–Tomasi (KLT) feature tracking algorithm. Then we nullify the camera movement by using the affine transformation matrix obtained by random sample consensus (RANSAC) algorithm. Subsequently from the steady video, the cable motions are estimated using the phase‐based motion estimation technique. From the time history of the cable vibration, real‐time frequency variations are estimated using Short‐Time Fourier Transform (STFT). Finally, the real‐time tension is determined from this dominant frequency variation history using the taut‐string theory. This paper shows the significant potential of camera‐based sensing techniques in structural health monitoring as the mean estimated tension and the design cable tension are found to be comparable.

show abstract

Section: Introductionmentioning

confidence: 99%

Computer vision‐based real‐time cable tension estimation in Dubrovnik cable‐stayed bridge using moving handheld video camera

Jana

Nagarajaiah

2021

Struct Control Health Monit

Self Cite

View full text Add to dashboard Cite

show abstract

“…12,13 The developed methodologies of CV-based deformation measurement are mostly focused on displacement tracking. These monitoring methods may be categorized into target-based and target-less approaches by utilizing algorithms such as feature matching, 6 digital image correlation, 14 optical flow estimation, [15][16][17] and phase-based motion magnification. 18,19 In the case of target-based algorithms, the noncontact and full-field benefits of CV techniques may not be fully utilized by the required installation of artificial targets with well-defined geometry.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based displacement and joint rotation tracking of frame structure using feature mix with single consumer‐grade camera

Wang

Zhao

Liu

et al. 2021

Struct Control Health Monit

View full text Add to dashboard Cite

A monocular vision-based response tracking method is proposed for simultaneously identifying translational floor displacement and rotational joint deformation of frame structures at multiple locations. The framework incorporates subpixel-level feature matching and tracking and is designed by introducing the feature-mix concept and feature quality evaluation for the initialization and postprocessing stage, respectively. The necessity of monitoring frame joint rotation is presented with the damage observations. A large-scale reinforced concrete frame structure is employed to investigate the proposed response estimation method through comprehensive shaking table tests by using a consumer-grade camera. The initial feature population, time-history agreement, overall estimation accuracy, survival feature ratio, and measurement uncertainty are further analyzed. In addition, the potential values and advantages of the proposed response tracking method for dynamic measurement and seismic evaluation are discussed illustratively.

show abstract

“…Zhao et al 3 verified the feasibility of estimating cable force using a smartphone camera. Bhowmick et al 4 proposed a technique for full-field displacement measurement and accurately obtained the cable vibration of a realworld bridge. In addition, the dominating vibrational mode of cable was extracted using the Hankel dynamic mode decomposition method in their further research.…”

Section: Introductionmentioning

confidence: 99%

Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

Zhang

Tian

Zhang

2021

Structural Contr & Hlth

View full text Add to dashboard Cite

Drone-assisted structural health monitoring has aroused extensive attention due to its high mobility and low cost. However, drone motion and complex image backgrounds severely impede its application in the cable force measurement of urban bridges. To fill this research gap, this paper proposed a deep learning-based complex background segmentation approach for cable force estimation of urban bridges from the drone-captured video. The main contribution of this article includes two aspects: (1) A pre-trained fully convolutional network (FCN) model was first adopted to identify bridge cables from dronecaptured video and further to extract sub-pixel-level displacement using line segment detection (LSD) algorithm, and (2) an empirical mode decomposition algorithm was employed for extracting the vibration signal of bridge cables by

show abstract

Measurement of full-field displacement time history of a vibrating continuous edge from video

Cited by 75 publications

References 26 publications

Computer vision‐based real‐time cable tension estimation in Dubrovnik cable‐stayed bridge using moving handheld video camera

Computer vision‐based real‐time cable tension estimation in Dubrovnik cable‐stayed bridge using moving handheld video camera

Vision‐based displacement and joint rotation tracking of frame structure using feature mix with single consumer‐grade camera

Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

Contact Info

Product

Resources

About