Nondestructive Bridge Cable Tension Assessment Using Laser Vibrometry

Chen, S.E.; Petro, S.

doi:10.1111/j.1747-1567.2005.tb00210.x

Cited by 18 publications

(9 citation statements)

References 4 publications

(4 reference statements)

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“…GPS sensors too has been experimented in combination with accelerometers [3], also if their accuracy and sampling rate are again not fully satisfactory. Recently laser vibrometers has been successfully used for bridge load testing [4] and for bridge cable tension assessment [5]. These equipments are surely effective in a number of cases and have the unique capability to detect vibration without contact at a single point precisely identified, but they are rather sensitive to dust and their operating distance typically does not exceed 20-30 m. The latter is recognized to be the most significant limitation [4], furthermore distance affects considerably accuracy and acquisition speed.…”

Section: Introductionmentioning

confidence: 99%

Structural oscillation modes identification by applying controlled loads and using microwave interferometry

Fratini

Parrini

Pieraccini

et al. 2009

NDT & E International

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Section: Introductionmentioning

confidence: 99%

Structural oscillation modes identification by applying controlled loads and using microwave interferometry

Fratini

Parrini

Pieraccini

et al. 2009

NDT & E International

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“…Sensors for measuring the responses of a cable can be installed and utilized in existing bridges, but they are difficult to install. To address this problem, studies on efficient and convenient cable tension measurement methods using a laser vibrometer, elastomagnetic sensors, fiber Bragg grating sensors, and ultrasonic waves have been continually conducted.…”

Section: Introductionmentioning

confidence: 99%

Image‐based back analysis for tension estimation of suspension bridge hanger cables

Kim

Cheung

Park³

et al. 2020

Struct Control Health Monit

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Summary In suspension bridges, hanger cables are the main load‐supporting members. The tension of the hanger cables of a suspension bridge is a very important parameter for assessing the integrity and safety of the bridge. In general, indirect methods are used to measure the tension of the hanger cables of a suspension bridge in use. A representative indirect method is the vibration method, which extracts modal frequencies from the cables' responses and then measures the cable tension using the cables' geometric conditions and the modal frequencies. In this study, ambient vibration tests were conducted on a suspension bridge in use to verify the validity of the image‐based back analysis method, which can estimate the tension of remote hanger cables using the modal frequencies as a parameter. The tension estimated through back analysis, which was conducted to minimize the difference between the modal frequencies calculated using finite element analysis of the hanger cables and the measured modal frequencies, was compared with that measured using the vibration method. It was confirmed that reliable tension estimation is possible even with low‐order modal frequencies when the image‐based back analysis method is used.

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“…Noncontact optical measurement techniques, such as scanning laser vibrometers, can provide high spatial resolution sensing capacity without the need to install sensors on the structure or inducing the mass‐loading effect. However, these measurement devices are relatively expensive and perform measurements sequentially, which could be time and labor intensive when used for full‐scale cables …”

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

Yang

Sánchez

Zhang

et al. 2019

Struct Control Health Monit

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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.

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Nondestructive Bridge Cable Tension Assessment Using Laser Vibrometry

Cited by 18 publications

References 4 publications

Structural oscillation modes identification by applying controlled loads and using microwave interferometry

Structural oscillation modes identification by applying controlled loads and using microwave interferometry

Image‐based back analysis for tension estimation of suspension bridge hanger cables

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

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