Sag-based cable tension force evaluation of cable-stayed bridges using multiple digital images

Jo, Hyeon Cheol; Kim, Sung Hyun; Lee, Jisang; Sohn, Hong-Gyoo; Lim, Yun Mook

doi:10.1016/j.measurement.2021.110053

Cited by 22 publications

(12 citation statements)

References 32 publications

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“…Transformation of an object from 3D space to 2D-image is associated with the impact of perspective projection (defined by camera focal length fi), translation (ti) and rotation (ri) components. A more detailed description of the algorithms for 3D-2D transformation usually used in DIC, (e.g., holography) can be found in several recent studies [3,9,20].…”

Section: Dic-the Main Principles Of the Methodsmentioning

confidence: 99%

Digital Image Correlation for Assessment of Bridges’ Technical State and Remaining Resource

Kopiika,

Blikharskyy

2024

Preprint

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Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

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Section: Dic-the Main Principles Of the Methodsmentioning

confidence: 99%

Digital Image Correlation for Assessment of Bridges’ Technical State and Remaining Resource

Kopiika,

Blikharskyy

2024

Preprint

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“…The cable force is converted by the vibration frequency method using the following formula [ 17 , 39 ]:

where m , l , and

represent the linear density, the length of the bracing cable, and the n-order natural vibration frequency, respectively.…”

Section: Design Of Fbg-type Thin Rod Vibration Sensormentioning

confidence: 99%

“…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. Wang et al [ 18 ] noted that even if high-performance and expensive cameras are used, they may not provide sufficient resolution.…”

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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“…Dan et al 25 also used PSO for the identification of tension and lateral force components (e.g., additional mass and dampers); however, only tension and damping coefficient were successfully identified with acceptable accuracy. Other studies into real-time cable tension estimation 26,27 or vision-based measurement of cable vibration 28,29 have recently been of interest. Apart from the above mentioned approaches, Zarbaf et al 30 was among the first to introduce an application of artificial neural networks (ANNs) in estimating tension.…”

Section: Introductionmentioning

confidence: 99%

Stay cable tension estimation of cable‐stayed bridge under limited information on cable properties using artificial neural networks

Siringoringo

Katsuchi

et al. 2022

Structural Contr & Hlth

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Summary This paper proposes a framework for estimating tensions of stay cables of cable‐stayed bridge with and without lateral attachments under limited information on cable properties. For stay cables without lateral attachments, tension is estimated using three known parameters, namely, cable length, mass per unit length, and measured frequency while still accounting for unknown features like cable bending stiffness, axial stiffness, cable inclination, and restraint boundary conditions at the cable ends. The methodology is then extended to stay cables with lateral attachments (dampers and cross ties), whose properties are also considered as unknown parameters. The framework is proposed via the application of back‐propagation artificial neural networks (ANNs). The finite difference formulation of the cable model is derived to create datasets for training, validation, and testing in the ANNs scheme. The feasibility and robustness of the proposed framework were confirmed through numerical verifications. The results indicated that tension in cables without lateral attachments was successfully evaluated using at least two measured frequencies, whereas cables with lateral attachments needed at least three measured frequencies to achieve high accuracy. Finally, the proposed framework was applied to estimate tensions in stay cables of Tatara Bridge, the longest cable‐stayed bridge in Japan. The results demonstrated that the proposed method could estimate the cable tension with acceptable accuracy.

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Sag-based cable tension force evaluation of cable-stayed bridges using multiple digital images

Cited by 22 publications

References 32 publications

Digital Image Correlation for Assessment of Bridges’ Technical State and Remaining Resource

Digital Image Correlation for Assessment of Bridges’ Technical State and Remaining Resource

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

Stay cable tension estimation of cable‐stayed bridge under limited information on cable properties using artificial neural networks

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