Modal identification of simple structures with high-speed video using motion magnification

Chen, Justin G.; Wadhwa, Neal; Cha, Young‐Jin; Durand, Frédo; Freeman, William T.; Büyüköztürk, Oral

doi:10.1016/j.jsv.2015.01.024

Cited by 410 publications

(214 citation statements)

References 22 publications

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“…"forward simulation": (b) Project the force onto the modal domain and compute the corresponding modal responses by solving Equation (21). Obtain the full-field structural responses by synthesizing the modal responses using modal superposition Equation (25).…”

Section: Summary Of the Video-based Simulation Methodsmentioning

confidence: 99%

“…It first projects the equation of motion with the virtual force onto the modal domain (Equation (14)- (20)) and computes the simulated modal responses by solving Equation (21). Then the simulated modal responses are synthesized by the full-field mode shapes using Equation (25) to produce the simulated full-field structural responses, which are finally embedded into the original video to generate a new video (Equation (26)) to changes in illumination, perspective, and surface conditions, [22] making it preferable over image intensity information in many real-world applications. The obtained local phase ψ(x, t) corresponding to the vibration motion δ(x, t) can be expressed by the modal expansion as linear combinations of the modal responses…”

Section: Figurementioning

confidence: 99%

“…Then the modal responses are computed by solving the SDOF models Equation (21) using the Newmark-beta algorithm. Then these modal responses were synthesized by the mode shapes using the modal superposition Equation (25) to obtain the structural responses. These simulated structural responses were compared with the actual earthquake responses generated from the full 20-DOF system.…”

Section: Figurementioning

confidence: 99%

“…Recently, the phase-based video motion estimation technique [22][23][24] has been explored to perform output-only modal identification without the need to install high-contrast markers or speckle paints on the structure's surface. [25,26] The lately developed algorithm based on this technique enables extraction and visualization of full-field modal parameters in a relatively efficient and automated manner, [26] and is suited for establishing a full-field modal model and further for realistic visualization of the simulated full-field structural dynamic response in this study.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations

Yang

Dorn

Mancini

et al. 2018

Struct Control Health Monit

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SummaryStructures with complex geometries, material properties, and boundary conditions exhibit spatially local dynamic behaviors. A high-spatial-resolution model of the structure is thus required for high-fidelity analysis, assessment, and prediction of the dynamic phenomena of the structure. The traditional approach is to build a highly refined finite element computer model for simulating and analyzing the structural dynamic phenomena based on detailed knowledge and explicit modeling of the structural physics such as geometries, materials properties, and boundary conditions. These physics information of the structure may not be available or accurately modeled in many cases, however. In addition, the simulation on the high-spatial-resolution structural model, with a massive number of degrees of freedom and system parameters, is computationally demanding. This study, on a proof-of-principle basis, proposes a novel alternative approach for spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field structural dynamics by an innovative combination of the fundamentals of structural dynamic modeling and the advanced video motion manipulation techniques.Specifically, a low-modal-dimensional yet high-spatial (pixel)-resolution (as many spatial points as the pixel number on the structure in the video frame) modal model is established in the spatiotemporal video domain with full-field modal parameters first estimated from line-of-sight video measurements of the operating structure. Then in order to simulate new dynamic response of the structure subject to a new force, the force is projected onto each modal domain, and the modal response is computed by solving each individual single-degree-of-freedom system in the modal domain. The simulated modal responses are then synthesized by the full-field mode shapes using modal superposition to obtain the simulated full-field structural dynamic response. Finally, the simulated structural dynamic response is embedded into the original video, replacing the original motion of the video, thus generating a new photo-realistic, physically accurate video that enables a --

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Section: Summary Of the Video-based Simulation Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations

Yang

Dorn

Mancini

et al. 2018

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…The feature of distributed sensing has been used in laboratory structures [17,19,29,30] for multistorey displacement measurement and system identification, as well as for cable vibration monitoring [31][32][33][34] aimed at the estimation of modal frequencies or cable tensions, but applications in bridge deformation measurement are limited, with only a few examples. [27,35] …”

Section: Distributed Sensingmentioning

confidence: 99%

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Brownjohn

Kong

2018

Struct Control Health Monit

162

106

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SummaryVision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement, for example, at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multipoint displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cablestayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few centimetres induced, in this application, by pedestrian passing. The measured data enable accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads. KEYWORDSbridge displacement, cable vibration, cable-stayed bridge, pedestrian loads, vision-based system | INTRODUCTIONStructural health monitoring is aimed at providing valuable information about structural performance and characterisation of structural defects to the asset owners, especially for those civil infrastructures beyond the design life. Vibrationbased modal tests are a common way for structural condition and serviceability assessment, providing a direct view about the structural stiffness, mass properties, and their distributions. [1] As well as for validating designs of civil structures, modal parameters extracted from vibration data obtained in short-term or long-term measurements are widely believed to have potential for identifying changes in structural condition or "damage." [2] The sensitivity of these parameters to damage depends on the nature of the damage, for example, local deterioration of material, for example, due to corrosion, may not be detectable against background effects of environmental variability, whereas boundary conditions are known to have a relatively strong effect, for example, fixity of bridge supports.[3]This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Bolt looseness detection based on Canny edge detection algorithm

Song

Cui

et al. 2023

Concurrency and Computation

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SummaryAiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness.

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Modal identification of simple structures with high-speed video using motion magnification

Cited by 410 publications

References 22 publications

Spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations

Spatiotemporal video-domain high-fidelity simulation and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Bolt looseness detection based on Canny edge detection algorithm

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