Structural identification of Humber Bridge for performance prognosis

Rahbari, Rahi; Jin, Niu; Koo, Ki-Young

doi:10.12989/sss.2015.15.3.665

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…From the previous studies on the bridge, it is known that (vertical) modal frequencies exist at 0.117, 0.31, and 0.46 Hz (Rahbari et al, 2015). The DMS captured the first and second vertical modal frequencies while the GPS captured only the first one.…”

Section: Measurement Evaluation In Time Domainmentioning

confidence: 99%

Vision-Based Bridge Deformation Monitoring

Brownjohn

Hester

2017

Front. Built Environ.

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Optics-based tracking of civil structures is not new, due to historical application in surveying, but automated applications capable of tracking at rates that capture dynamic effects are now a hot research topic in structural health monitoring. Recent innovations show promise of true non-contacting monitoring capability avoiding the need for physically attached sensor arrays. The paper reviews recent experience using the Imetrum Dynamic Monitoring Station (DMS) commercial optics-based tracking system on Humber Bridge and Tamar Bridge, aiming to show both the potential and limitations. In particular, the paper focuses on the challenges to field application of such a system resulting from camera instability, nature of the target (artificial or structural feature), and illumination. The paper ends with evaluation of a non-proprietary system using a consumer-grade camera for cable vibration monitoring to emphasize the potential for lower cost systems where if performance specifications can be relaxed.

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Section: Measurement Evaluation In Time Domainmentioning

confidence: 99%

Vision-Based Bridge Deformation Monitoring

Brownjohn

Hester

2017

Front. Built Environ.

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“…The bridge is exposed to prevailing southwesterly cyclonic winds that can reach hurricane force (exceeding 32.7 m/s), with atmospheric temperatures ranging from −10 to 30°C. Further details of the structure of the Humber bridge may be found in Rahbari et al (2015). The bridge has been instrumented with various sensors, including GPS antennas, accelerometers, inclinometers and extensometers.…”

Section: Data Description and Preprocessingmentioning

confidence: 99%

Gaussian Process Time-Series Models for Structures under Operational Variability

Avendaño-Valencia

Chatzi

Koo

et al. 2017

Front. Built Environ.

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A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue, the present contribution introduces a stochastic modeling methodology via Gaussian Process (GP) time-series models. In the presently introduced approach, the vibration response is represented by means of a random coefficient time-series model, whose coefficients comply with a GP regression on the environmental and operational parameters. The approach may be implemented in conjunction to any type of linear-in-the-parameters time-series model, ranging from simple AR models to more complex non-linear or nonstationary time-series models. The obtained GP time-series modeling approach provides an effective and compact global representation of the vibrational response of a structure under a wide span of environmental and operational conditions. The effectiveness of the postulated GP time-series models is demonstrated through two case studies: the first involves the identification of the vertical vibration response of the Humber bridge, evaluated over a period of three years; the second considers the long-term simulated vibration response of a wind turbine featuring non-stationary dynamics stemming from the rotor speed. In both cases, the variation of the average wind speed is the main driver of uncertainty, while, through application of the proposed GP time-series models, it is possible to track the resulting variation in modal quantities.

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“…Hamming window and the results are shown in Figure 7. From the previous studies on the bridge it is known that (vertical) modal frequencies exist at 0.117 Hz, 0.31 Hz and 0.46 Hz [41]. The GPS system…”

Section: Enhanced Dynamic Information In Displacement Estimate Followmentioning

confidence: 99%

Long-span bridges: Enhanced data fusion of GPS displacement and deck accelerations

Brownjohn

Hester

et al. 2017

Engineering Structures

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Displacement data under operational loads are an important aid for the estimation of structural performance, but accurate measurement of structural displacement remains as a challenging task, especially for long-span bridges. The global positioning system (GPS) is the common choice for longspan bridge displacement monitoring but the measurement accuracy is not satisfactory. The purpose of this study is to improve the GPS accuracy using a practical data fusion method. Although the main algorithms of data fusion method based on multi-rate Kalman filter are already reported, the detail about how to select the noise parameters required for Kalman filter is not provided. This paper demonstrates that maximum likelihood estimation (MLE) can be used to determine the necessary noise parameters. The proposed approach was validated on numerical and field data, the latter from a single-day displacement monitoring campaign on the Humber Bridge in the UK. The direct measurement by GPS was merged with the collocated acceleration data and the resulting displacement signal was evaluated by comparing it to the displacement signal from an independent vision-based system. Through the comparison, it is shown that MLE enhanced data fusion is practical to improve the GPS measurement accuracy and to widen the frequency bandwidth. The MLE provides an estimation about the GPS noise (assumed as zero-mean Gaussian process) with the standard deviation varying from 6 mm to 16 mm in the test day. The normalised root mean square deviation of GPS 2 measurement compared with the vision-based measurement was decreased from 3.17% to 2.37% after applying the data fusion.

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Structural identification of Humber Bridge for performance prognosis

Cited by 9 publications

References 23 publications

Vision-Based Bridge Deformation Monitoring

Vision-Based Bridge Deformation Monitoring

Gaussian Process Time-Series Models for Structures under Operational Variability

Long-span bridges: Enhanced data fusion of GPS displacement and deck accelerations

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