Establishment of Full-Field, Full-Order Dynamic Model of Cable Vibration by Video Motion Manipulations

Struct Control Health Monit

Jung

Dorn

et al. 2019

Self Cite

Summary Strain is an essential quantity to characterize local structural behaviors and directly correlates with structural damage initiation and development that is within local regions. Strain measurement at high spatial resolution (density) locations is thus required to characterize local structural behaviors and detect potential local damage. Traditional contact‐type strain gauges are mostly discrete point‐wise sensors that can only be placed in a limited number of positions. Distributed optical fiber sensing techniques can measure strains at spatially dense measurement points, but their instrumentation is a time‐ and labor‐intensive process associated with the issue of the fragility of fibers. Noncontact optical measurement techniques, such as a family of interferometry techniques using laser beams (e.g., laser Doppler vibrometers), can provide vibration measurement at high density spatial points without the need to install sensors on the structure. However, these measurement devices are active sensing methods that are relatively expensive and vulnerable to ambient motion. Photogrammetry is an alternative noncontact optical measurement method using (passive) white‐light imaging of digital video cameras that are relatively low‐cost, agile, and provides simultaneous measurements at high spatial density locations where every pixel becomes a measurement point. Among others, digital image correlation can achieve full‐field deformation measurements and subsequently estimate the full‐field strains. However, it is computationally extensive. This study develops a new efficient approach to estimate the full‐field (as many measurement points as the pixel number of the video frame on the structure) dynamic strains at high‐spatial (pixel)‐resolution/density location points from the digital video measurement of output‐only vibrating structures. The developed approach is based on phase‐based video motion estimation and modal superposition of structural dynamic response. Furthermore, the method is augmented by a high‐fidelity finite element model, which is updated with the full‐field experimental modal parameters “blindly” identified from the video measurement of the output‐only structure. Laboratory experiments are conducted to validate the method on a bench‐scale cantilever beam structure. Results demonstrate that the full‐field dynamic strain estimated by the developed approach from the video measurement of the output‐only vibrating beam match very well those directly measured by the strain gauges (at discrete measurement points). Some factors associated with the effectiveness of the method are experimentally studied and discussed.

Section: Introductionmentioning

confidence: 99%

Estimation of full‐field dynamic strains from digital video measurements of output‐only beam structures by video motion processing and modal superposition

Struct Control Health Monit

Jung

Dorn

et al. 2019

Self Cite

“…Using image processing algorithms such as digital image correlation, video camera imaging measurements have been successfully used for vibration measurements of civil structures and more recently for full‐field experimental modal analysis where full‐field modal parameters (mode shapes) can be obtained. Lately, phase‐based video motion processing technique has been explored to perform output‐only modal identification without the need of installing markers or speckle paints on a structure's surface . The latest development of this technique enables extraction and visualization of full‐field, high spatial resolution modal parameters in a relatively efficient and automated manner .…”

Section: Introductionmentioning

confidence: 99%

Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision

Struct Control Health Monit

Sánchez

Zhang

et al. 2019

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Cables are critical components for a variety of structures such as stay cables and suspenders of cable-stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform highresolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full-modal-order dynamics model of cable vibration can be established. This study develops a physics-guided, unsupervised machine learning-based video processing approach that can blindly and efficiently extract the fullfield (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output-only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full-order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full-field, full-order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low-cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench-scale cable are conducted to validate the developed approach.

“…Recently, a phase-based video motion processing technique (originally proposed by Fleet and Jepson (1990) and further developed by Gautama and Van Hulle (2002) and Wadhwa et al (2013) has been explored to perform full-field structural dynamics response measurements and output-only modal identification without the need for installing markers or speckle paints on a structure’s surface (Cha et al, 2017; Chen et al, 2015a, 2015b; Dasari et al, 2017; Dorn et al, 2016, 2017; Poozesh et al, 2017; Roeder et al, 2017; Sanchez et al, 2017; Sarrafi et al, 2017; Yang et al, 2017a, 2017b, 2017c, 2017d, 2017e). Furthermore, the phase-based video motion processing is found computationally efficient to extract from the video the local motion that corresponds to the structural vibration (Yang et al, 2017a; Yang et al, 2017c).…”

Section: Introductionmentioning

confidence: 99%

A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

Dasari

Dorn

Journal of Intelligent Material Systems and Structures

et al. 2018

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Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.