Computer vision‐based real‐time cable tension estimation algorithm using complexity pursuit from video and its application in Fred‐Hartman cable‐stayed bridge

Jana, Debasish; Nagarajaiah, Satish; Yang, Yongchao

doi:10.1002/stc.2985

Cited by 36 publications

(18 citation statements)

References 74 publications

(134 reference statements)

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“…The proposed methodology is invariant to the properties of the external forcing function; therefore, the reconstructed full-field time histories are accurate and comparable with the measured time history. In this article, the performance of the proposed framework is presented and validated on the video of cantilever beam vibration; this can be adapted for cable type structures also [ 49 , 50 ].…”

Section: Experimental Verificationmentioning

confidence: 99%

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing

Jana

Nagarajaiah

2022

Sensors

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In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.

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Section: Experimental Verificationmentioning

confidence: 99%

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing

Jana

Nagarajaiah

2022

Sensors

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“…In this section, the sparsity‐enforcement method was used to evaluate the structural modal identification, and this includes the structural natural frequencies and damping ratios (Jana et al., 2022; Wangchuk et al., 2022). To calculate these results, at least two degrees of freedom of dynamics are required; therefore, like the qualitative comparisons of structural displacements, only the results in the y direction of Case 1 were used.…”

Section: Case Studymentioning

confidence: 99%

“…To offset these drawbacks, the phase-based method, as one type of target-free methods, established by complexvalued steerable pyramids, has been developed (Wadhwa et al, 2013). It has been applied in structural motion estimation (Yang et al, 2017;Shang & Shen, 2018), dynamic strain analysis (Yang et al, 2019), and modal identification (Jana et al, 2022;Wangchuk et al, 2022). The phase-based method is essentially one type of video magnification methods, which can be classified into Lagrangian and Eulerian methods.…”

Section: Introductionmentioning

confidence: 99%

Estimating small structural motions based on sparsity enforcement

Cai

Zhang

et al. 2022

Computer aided Civil Eng

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This paper proposed a novel, efficient method to estimate challenging small structural motions from noisy video. To eliminate the phase limitation, ill-posed problem, and high computational burden, the structural motion function is resampled and recovered. Because video signals have tremendous redundancies in spatial, block, and time domains, the objective is achieved by enforcing three levels of sparsity constraints. In the first step, the ill-posed optimization is solved using the multi-hypothesis prediction embedded with the Tikhonov regularization, to resample and recover the video signal in the spatial domain. The second step is to characterize the measurement uncertainty in the block domain, in which two weight matrices are proposed into the regularization terms of the sparse coding. Finally, the temporal correlation of video frames is exploited by the reweighted residual sparsity. The superiority of the sparsity-enforcement method over existing methods was demonstrated through several case studies. Among the comparisons, the sparsity-enforcement method yielded video magnifications, structural motions, and modal information more accurately. Meanwhile, it has the lowest computational burden.

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“…Bhowmick et al 35 developed a method for measuring continuous displacement of 1‐D structural element by using digital image correlation, and based on the method, Bhowmick and Nagarajaiah 36 identified full‐field dynamic properties of the structure. Lee et al 37 proposed a method for measuring cable displacements from a side‐view video, and Jana et al 38 used several computer vision methods with frequency domain analysis for estimating tension of cables.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based displacement measurement using a camera mounted on a structure with stationary background targets outside the structure

Lee

Moon

et al. 2022

Structural Contr & Hlth

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While structural displacements are essential information for structural health monitoring, they are not being widely used in practice due to the inconvenience. Recently, vision-based displacement measurement methods have been introduced, which are more convenient and cost effective. However, visionbased methods have generally not been used primarily for the continuous monitoring of structures, due to spatial constraints on obtaining an appropriate location to secure the field of view. A vision device shows not only the changes of objects in the scene but also the relative changes of view according to changes in the position to which it is mounted. Accordingly, this study proposes a methodology for measuring the structural displacement of a location where a camera is mounted, based on a camera motion-induced relative view change. The method is organized into three steps. First, camera calibration is conducted with background targets to derive the camera parameters and coordinates of the target feature points. Second, by tracking the relative changes in the feature points according to the camera motion, the changed 2D-image coordinates of the points are derived. Third, the displacement is calculated through the relationship between the changed 2D-image coordinates and fixed 3D-world coordinates of the target feature points using the camera parameters.The changes in view according to the camera motion are analyzed with simulation tests, and the applicability of the proposed method is verified through experimental tests. The results show that the proposed method can be used to rationally measure structural displacements.

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Computer vision‐based real‐time cable tension estimation algorithm using complexity pursuit from video and its application in Fred‐Hartman cable‐stayed bridge

Cited by 36 publications

References 74 publications

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing

Estimating small structural motions based on sparsity enforcement

Vision‐based displacement measurement using a camera mounted on a structure with stationary background targets outside the structure

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