Dynamic measurement of stay-cable force using digital image techniques

Du, Wenkang; Dong, Li Min; Bai, Peng; Zhu, Feipeng; Huang, Zhentian

doi:10.1016/j.measurement.2019.107211

Cited by 57 publications

(17 citation statements)

References 34 publications

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“…Du et al, in 2020, investigated the dynamic response of stay cable Guanhe bridge by using two measurement techniques, Digital Image Correlation (DIC) and Digital Im-age Processing (DIP), together with the use of the traditional accelerometer [104]. Two laboratory experiments, including single and multipoint cable force measurement, were performed and compared with the accelerometer and the numerical simulation to assess the validity of digital image methods.…”

Section: Suspension Bridgesmentioning

confidence: 99%

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

et al. 2021

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A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, long-distance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

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Section: Suspension Bridgesmentioning

confidence: 99%

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

et al. 2021

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“…However, the magnetic flux method is susceptible to chemical corrosion leading to MFL [ 14 ], and the current size affects the electromagnetic force [ 15 ]. Du et al [ 16 ] used two measurement methods based on digital image technology, namely, digital image processing (DIP) and digital image correlation (DIC). A camera captures single-point and multi-point images to identify the cable force, however, as shown by Jo et al [ 17 ], it is vulnerable to fluctuations in external loads such as temperature and wind, and it is troublesome to measure the instantaneous tension of the cable.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Cable Force through a Fiber Bragg Grating-Type Thin Rod Vibration Sensor and Its Application

Zhu

Teng

Liu

et al. 2022

Sensors

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The key to evaluating the health status of cable-stayed bridges lies in the accuracy of cable force measurement. When measuring the cable force using the conventional frequency method, the clearance between the bracing cable and the protective tube is typically disregarded. Moreover, due to their large size, existing vibration sensors are difficult to install into protective tubes for steel strand-type bracing cables to measure the cable force. To address the above difficulties, a type of thin rod vibration sensor only 5 mm in diameter was designed based on the high sensitivity of Fiber Bragg grating (FBG), and high-throughput data processing software for engineering calculation (EC) was self-developed. Then, the recognition principle of the thin rod vibration sensor was theoretically analyzed and a step-by-step tension test was carried out. The results demonstrated that the relative error of the cable force measured by the thin rod vibration sensor within 12.865 Hz was less than 5% and the sensitivity reached 28.7 pm/Hz, indicating its high measurement precision. Upon subsequent application of the thin rod vibration sensor to a monitoring test in the field, the relative error of the fundamental frequency between artificial and natural excitations was less than 4%. In addition, the error relative to both the theoretical frequency and the third-party sampling frequency was less than 5%, further verifying the accuracy and applicability for monitoring the cable force of bridges under natural excitation. Compared with the traditional cantilever FBG sensor, the improved sensor with supporting data processing software has the advantages of small cross-section, high reliability, and good sensitivity. The research results can provide a reference for the subsequent accurate measurement of cable force and the development of a supporting sensor data processing system.

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“…Feng et al 8 measured cable forces of a cable-supported roof structure during construction using an innovative vision-based sensor. Du et al 9 used digital image processing and correlation methods to measure cable forces with single and multiple points images.…”

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

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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

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Dynamic measurement of stay-cable force using digital image techniques

Cited by 57 publications

References 34 publications

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

Measurement of Cable Force through a Fiber Bragg Grating-Type Thin Rod Vibration Sensor and Its Application

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

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