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Mode identifiability of a cable-stayed bridge based on a Bayesian method
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  • Journal title : Smart Structures and Systems
  • Volume 17, Issue 3,  2016, pp.471-489
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2016.17.3.471
 Title & Authors
Mode identifiability of a cable-stayed bridge based on a Bayesian method
Zhang, Feng-Liang; Ni, Yi-Qing; Ni, Yan-Chun;
 Abstract
Modal identification based on ambient vibration data has attracted extensive attention in the past few decades. Since the excitation for ambient vibration tests is mainly from the environmental effects such as wind and traffic loading and no artificial excitation is applied, the signal to noise (s/n) ratio of the data acquired plays an important role in mode identifiability. Under ambient vibration conditions, certain modes may not be identifiable due to a low s/n ratio. This paper presents a study on the mode identifiability of an instrumented cable-stayed bridge with the use of acceleration response data measured by a long-term structural health monitoring system. A recently developed fast Bayesian FFT method is utilized to perform output-only modal identification. In addition to identifying the most probable values (MPVs) of modal parameters, the associated posterior uncertainties can be obtained by this method. Likewise, the power spectral density of modal force can be identified, and thus it is possible to obtain the modal s/n ratio. This provides an efficient way to investigate the mode identifiability. Three groups of data are utilized in this study: the first one is 10 data sets including six collected under normal wind conditions and four collected during typhoons; the second one is three data sets with wind speeds of about 7.5 m/s; and the third one is some blind data. The first two groups of data are used to perform ambient modal identification and help to estimate a critical value of the s/n ratio above which the deficient mode is identifiable, while the third group of data is used to perform verification. A couple of fundamental modes are identified, including the ones in the vertical and transverse directions respectively and coupled in both directions. The uncertainty and s/n ratio of the deficient mode are investigated and discussed. A critical value of the modal s/n ratio is suggested to evaluate the mode identifiability of the deficient mode. The work presented in this paper could provide a base for the vibration-based condition assessment in future.
 Keywords
mode identifiability;cable-stayed bridge;ambient vibration;fast Bayesian FFT method;uncertainty;
 Language
English
 Cited by
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Operational modal analysis of a high-rise multi-function building with dampers by a Bayesian approach, Mechanical Systems and Signal Processing, 2017, 86, 286  crossref(new windwow)
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Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection, Structural Control and Health Monitoring, 2018, e2140  crossref(new windwow)
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Evaluation of the dynamic characteristics of a super tall building using data from ambient vibration and shake table tests by a Bayesian approach, Structural Control and Health Monitoring, 2017, e2121  crossref(new windwow)
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Bayesian structural model updating using ambient vibration data collected by multiple setups, Structural Control and Health Monitoring, 2017, 24, 12, e2023  crossref(new windwow)
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Operational modal identification of a boat-shaped building by a Bayesian approach, Engineering Structures, 2017, 138, 381  crossref(new windwow)
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Calculation of Hessian under constraints with applications to Bayesian system identification, Computer Methods in Applied Mechanics and Engineering, 2017, 323, 373  crossref(new windwow)
 References
1.
Au, S.K. (2011). "Fast Bayesian FFT method for ambient modal identification with separated modes", J. Eng. Mech.-ASCE, 137, 214-226. crossref(new window)

2.
Au, S.K. (2012a), "Fast Bayesian ambient modal identification in the frequency domain, Part I: Posterior most probable value", Mech. Syst. Signal Pr., 26, 60-75. crossref(new window)

3.
Au, S.K. (2012b), "Fast Bayesian ambient modal identification in the frequency domain, Part II: posterior uncertainty", Mech. Syst. Signal Pr., 26, 76-90. crossref(new window)

4.
Au, S.K., Ni, Y.C., Zhang, F.L. and Lam, H.F. (2012a), "Full scale dynamic testing of a coupled slab system", Eng. Struct., 37, 167-178. crossref(new window)

5.
Au, S.K. and Zhang, F.L. (2012a), "Fast Bayesian ambient modal identification incorporating multiple setups", J. Eng. Mech.-ASCE, 138(7), 800-815. crossref(new window)

6.
Au, S.K. and Zhang, F.L. (2012b), "Ambient modal identification of a primary-secondary structure by Fast Bayesian FFT method", Mech. Syst. Signal Pr., 28, 280-296. crossref(new window)

7.
Au, S.K., Zhang, F.L. and Ni, Y.C. (2013), "Bayesian operational modal analysis: theory, computation, practice", Comput. Struct., 126, 3-15. crossref(new window)

8.
Au, S.K., Zhang, F.L. and To, P. (2012b), "Field observations on modal properties of two tall buildings under strong wind", J. Wind Eng. Ind. Aerod., 101, 12-23. crossref(new window)

9.
Bao, Y., Beck, J.L. and Li, H. (2011), "Compressive sampling for accelerometer signals in structural health monitoring", Struct. Health Monit., 10(3), 235-246 crossref(new window)

10.
Brincker, R., Zhang, L. and Anderson, P. (2001), "Modal identification of output-only systems using frequency domain decomposition", Smart Mater. Struct., 10, 441-455. crossref(new window)

11.
Brownjohn, J.M.W., Magalhaes, F., Caetano, E. and Cunha, A. (2010), "Ambient vibration re-testing and operational modal analysis of the Humber Bridge", Eng. Struct., 32(8), 2003-2018. crossref(new window)

12.
Brownjohn, J.M.W., Moyo, P., Omenzetter, P. and Chakraborty, S. (2005), "Lessons from monitoring the performance of highway bridge", Struct. Control Health Monit., 12, 227-244. crossref(new window)

13.
Cross, E.J., Koo, K.Y., Brownjohn, J.M.W. and Worden, K. (2013), "Long-term monitoring and data analysis of the Tamar Bridge", Mech. Syst. Signal Pr., 35, 16-34. crossref(new window)

14.
Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. crossref(new window)

15.
Koo, K.Y., Brownjohn, J.M.W., List, D.I. and Cole, R. (2013), "Structural health monitoring of the Tamar suspension bridge", Struct. Control Health Monit., 20(4), 609-625. crossref(new window)

16.
Lam, H.F., Peng, H.Y. and Au, S.K. (2014), "Development of a practical algorithm for Bayesian model updating of a coupled slab system utilizing field test data", Eng. Struct., 79, 182-194. crossref(new window)

17.
Li, H., Ou, J., Zhao, X., Zhou, W., Li, H., Zhou, Z. and Yang, Y. (2006), "Structural health monitoring system for the Shandong Binzhou Yellow River highway bridge", Comput.-Aided Civil Infrastructu. Eng., 21(4), 306-317. crossref(new window)

18.
Ni, Y.C. and Au, S.K. (2014), "Fast Bayesian modal identification of structures using known single-input forced vibration data", Struct. Control Health Monit., 21(3), 381-402. crossref(new window)

19.
Ni, Y.C., Lu, X.L. and Lu, W.S. (2016). "Field dynamic test and Bayesian modal identification of a special structure - the Palms Together Dagoba", Struct. Control Health Monit., In press. DOI: 10.1002/stc.1816. crossref(new window)

20.
Ni, Y.Q., Wang, Y.W. and Xia, Y.X. (2015a), "Investigation of mode identifiability of a cable-stayed bridge: comparison from ambient vibration responses and from typhoon-induced dynamic responses", Smart Struct. Syst., 15(2), 447-468. crossref(new window)

21.
Ni, Y.Q., Wong, K.Y. and Xia, Y. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. crossref(new window)

22.
Ni, Y.Q., Zhang, F.L., Xia, Y.X. and Au, S.K. (2015b), "Operational modal analysis of a long-span suspension bridge under different earthquake events", Earthq. Struct., 8(4), 859-887. crossref(new window)

23.
Peeters, B. and De Roeck, G. (2001), "Stochastic system identification for operational modal analysis: a review", J. Dynam. Syst. Measurement Control-ASME, 123(4), 659-667. crossref(new window)

24.
Schoukens, J. and Pintelon, R. (1991), Identification of Linear Systems: A Practical Guideline for Accurate Modelling, London: Pergamon Press.

25.
Yuen, K.V. and Katafygiotis, L.S. (2003), "Bayesian fast fourier transform approach for modal updating using ambient data", Adv. Struct. Eng., 6(2), 81-95. crossref(new window)

26.
Zhang, F.L. and Au, S.K. (2013), "Erratum for Fast Bayesian FFT method for ambient modal identification with separated modes by Siu-Kui Au", J. Eng. Mech.-ASCE, 139, 545-545. crossref(new window)

27.
Zhang, F.L., Au, S.K. and Lam, H.F. (2015), "Assessing uncertainty in operational modal analysis incorporating multiple setups using a Bayesian approach", Struct. Control Health Monit., 22, 395-416. crossref(new window)

28.
Zhang, F.L., Ni, Y.Q., Ni, Y.C. and Wang, Y.W. (2016), "Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method", Smart Struct. Syst., 17(2), 209-230. crossref(new window)