Vortex‐induced vibration measurement of a long‐span suspension bridge through noncontact sensing strategies

Zhang, Jian; Zhou, Liming; Tian, Yongding; Yu, Shanshan; Zhao, Weian; Cheng, Yi

doi:10.1111/mice.12712

Cited by 32 publications

(18 citation statements)

References 40 publications

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“…The second evaluation method has the advantages of short evaluation cycle and few indicators; however, its disadvantage is that it is relatively intuitive. Static and dynamic load tests can only evaluate the safety status of the bridge at the later stage of the bridge, but it is difficult to find hidden quality problems during the construction process [12][13]. Therefore, it is necessary to propose a method that can not only reflect the construction quality, but also avoid large amount of data and reduce the evaluation difficulty and workload.…”

Section: Health Status Assessment Of Super Span Sbsmentioning

confidence: 99%

Machine Learning based Health Status Assessment of Super-span Suspension Bridges

2020

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With the rapid development of bridge design and analysis theory, building materials development and construction technology, more and more long-span bridges are built across rivers and seas. At the same time, the bridge construction scheme of over 1000 meters of bridges across rivers and seas is becoming more and more mature. Among them, the mainstream long-span bridges are suspension bridge (SB). The accuracy of research on key structure technology of bridge body needs to be continuously improved to ensure the structural safety of long-span bridges. Therefore, based on machine learning (ML) technology, the health status of super long span SBs is evaluated in this paper. The purpose and significance of long-span bridge structure health monitoring are briefly analyzed. Through the analysis of ML neural network algorithm, the evaluation model index layer is determined; finally, this paper takes the supporting project as an example to evaluate the safety status of the completed SB, which verifies the feasibility and effectiveness of the algorithm in this paper.

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Section: Health Status Assessment Of Super Span Sbsmentioning

confidence: 99%

Machine Learning based Health Status Assessment of Super-span Suspension Bridges

2020

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“…For example, the abnormal reduction of modal frequency is often regarded as an indicator of the loss of structural integrity, and Salawu [13] concluded that 5% reduction of modal frequency was a reliable sign for structural damage. As for damping ratio, it is the key parameter of long-span bridge's wind-resistant capacity, especially for the performance of vortex-induced vibration [14][15][16]. Te reduction of damping ratio may lead to large-amplitu devortex-induced vibration [14].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Time Series Decomposition for Structural Dynamic Properties: Long-Term Trend and Ambient Interference

Chu

Cui

et al. 2023

Structural Control and Health Monitoring

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In structural health monitoring (SHM), variations of structural dynamic properties are paramount to indicate the health status of structures. Structural dynamic properties are time variant due to various long-term effects (e.g., structural deterioration) and periodic effects (e.g., periodic variations of temperature, humidity, and traffic). Sometimes periodic effects will interfere with the quantification of long-term effects. Though important, there are still limited research studies aiming to distinguish these two effects. Given the amount of SHM data, it is possible to solve the issue from a data-driven perspective. This article proposes a time series decomposition methodology to divide the time series of structural dynamic properties into long-term parts, multiscale periodic parts, holiday parts, and error parts. We extract the 10-year dynamic properties of a long-span bridge using the fast Bayesian FFT identification algorithm and choose two dynamic modes of that bridge as examples to explain how our proposed methodology works. We use the long-term parts to extract the rules of structural deterioration. The multiscale periodic parts are utilized to find the relationships with the periodically varying ambient conditions (i.e., temperature and humidity) in different time scales (i.e., yearly, weekly, and daily). Then, the long-term and periodic effects can be distinguished. For the long-term effects, the modal frequencies tend to decrease but the damping ratios seem to increase. For the periodic effects, we find that the increment of temperature will lead to the decrease of both modal frequencies and damping ratios. The variations of modal frequencies induced by deterioration and temperature are of the same amplitudes. The variations of both modal frequencies and damping ratios are not significantly related with humidity. This article could provide references for damage detection and safety assessment for similar bridges.

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“…J. Zhang et al (2021) measured the vortex-induced vibration of a long-span suspension bridge using different noncontact techniques, including the DIC method. Khuc and Catbas (2017) proposed a target-free approach to track scale-invariant feature transform descriptors for displacement measurement, which was validated on an elevated bridge.…”

Section: Introductionmentioning

confidence: 99%

Wireless SmartVision system for synchronized displacement monitoring of railroad bridges

Shajihan

Hoang

Mechitov

et al. 2022

Computer aided Civil Eng

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The deflection of railroad bridges under in-service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision-based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate displacements. To date, vision-based techniques applied to long-term structural health monitoring have yet to be proven effective as an alternative to the traditional displacement measurement methods, such as linear variable differential transformers. This paper proposes a wireless SmartVision system (WSVS) that uses edge computing to directly output bridge displacements that can be sent to the end user. The system estimates displacements using both target-free and target-based approaches. A synchronized sensing framework is developed for multipoint displacement estimation using several wireless vision-based nodes for full-scale displacement-based modal analysis of structures. Pose estimation using an AprilTag, a fiducial marker, is employed with a modified algorithm for improved displacement tracking of targets installed on a bridge, yielding subpixel accuracy. The robustness of the results in field conditions is enhanced by linking a tracking quality factor to each timestamp to handle vision-related uncertainties. To meet the need for precise error metrics evaluation, an inexpensive cyber-physical setup using a synthetic testing environment is also developed in this study. Following laboratory validation, field tests on a cable-stayed pedestrian bridge were performed to demonstrate the efficacy of the proposed WSVS.

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Vortex‐induced vibration measurement of a long‐span suspension bridge through noncontact sensing strategies

Cited by 32 publications

References 40 publications

Machine Learning based Health Status Assessment of Super-span Suspension Bridges

Machine Learning based Health Status Assessment of Super-span Suspension Bridges

Multiscale Time Series Decomposition for Structural Dynamic Properties: Long-Term Trend and Ambient Interference

Wireless SmartVision system for synchronized displacement monitoring of railroad bridges

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