Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Investigation of mode identifiability of a cable-stayed bridge: comparison from ambient vibration responses and from typhoon-induced dynamic responses

  • Ni, Y.Q. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wang, Y.W. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Xia, Y.X. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2014.10.20
  • Accepted : 2015.01.18
  • Published : 2015.02.25
⟨ Previous Next ⟩

Abstract

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

Keywords

Acknowledgement

Supported by : Council of the Hong Kong Special Administrative Region

References

  1. Abdel-Ghaffar, A.M. and Housner, G.W. (1978), "Ambient vibration tests of suspension bridge", J. Eng. Mech. - ASCE, 104(5), 983-999.
  2. Abdel-Ghaffar, A.M. and Scanlan, R.H. (1985), "Ambient vibration studies of Golden Gate Bridge: I. suspended structure", J. Eng. Mech. - ASCE, 111(4), 463-482. https://doi.org/10.1061/(ASCE)0733-9399(1985)111:4(463)
  3. Andersen, P. (1997), Identification of civil engineering structures using vector ARMA models, PhD Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Aalborg, Denmark.
  4. Asmussen, J.C. (1997), Modal analysis based on the random decrement technique, PhD Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Aalborg, Denmark.
  5. Barney, P. and Carne, T. (1999), "Modal parameter extraction using natural excitation response data", Proceedings of the 17th International Modal Analysis Conference, Orlando, Florida, USA.
  6. Bendat, J.S. and Piersol, A.G. (1986), Random Data: Analysis and Measurement Procedures, John Wiley & Sons, New York, USA.
  7. Bergermann, R. and Schlaich, M. (1996), "Ting Kau Bridge, Hong Kong", Struct. Eng. Int., 6(3), 152-154. https://doi.org/10.2749/101686696780495563
  8. Brincker, R. and Andersen, P. (2006), "Understanding stochastic subspace identification", Proceedings of the 24th International Modal Analysis Conference, St. Louis, Missouri, USA.
  9. Brincker, R., Zhang, L. and Andersen, P. (2001), "Modal identification of output-only systems using frequency domain decomposition", Smart Mater. Struct., 10(3), 441-445. https://doi.org/10.1088/0964-1726/10/3/303
  10. Brownjohn, J.M.W., Bocciolone, M., Curami, A., Falco, M. and Zasso, A. (1994), "Humber Bridge full-scale measurement campaigns 1990-1991", J. Wind Eng. Ind. Aerod., 52, 185-218. https://doi.org/10.1016/0167-6105(94)90047-7
  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. https://doi.org/10.1016/j.engstruct.2010.02.034
  12. Chan, T.H., Li, Z.X. and Ko, J.M. (2004), "Evaluation of typhoon induced fatigue damage using health monitoring data for the Tsing Ma Bridge", Struct. Eng. Mech., 17(5), 655-670. https://doi.org/10.12989/sem.2004.17.5.655
  13. Chen, H.P. and Huang, T.L. (2012), "Updating finite element model using dynamic perturbation method and regularization algorithm", Smart Struct. Syst., 10(4-5), 427-442. https://doi.org/10.12989/sss.2012.10.4_5.427
  14. Chiu, H.Y. (2014), Beaufort wind scale, Hong Kong Observatory, Hong Kong (http://www.weather.gov.hk/education/edu01met/wxobs/ele_beaufort2_e.htm).
  15. Cunha, A., Caetano, E. and Delgado, R. (2001), "Dynamic tests on large cable-stayed bridge", J. Bridge Eng. - ASCE, 6(1), 54-62. https://doi.org/10.1061/(ASCE)1084-0702(2001)6:1(54)
  16. Gade, S., Moller, N.B., Herlufsen, H. and Konstantin-Hansen, H. (2005), "Frequency domain techniques for operational modal analysis", Proceedings of the 1st International Operational Modal Analysis Conference, edited by R. Brincker and N. Moller, Copenhagen, Denmark.
  17. Hong Kong Observatory (2000), Tropical cyclones in 1999, Report, Hong Kong Observatory, Hong Kong.
  18. Ibrahim, S.R. (1977), "Random decrement technique for modal identification of structures", J. Spacecraft Rockets, 14(11), 696-700. https://doi.org/10.2514/3.57251
  19. Jacobsen, N.J., Andersen, P. and Brincker, R. (2006), "Using enhanced frequency domain decomposition as a robust technique to harmonic excitation in operational modal analysis", Proceedings of the ISMA Conference on Advanced Acoustics and Vibration Engineering, Leuven, Belgium.
  20. James III, G.H., Carne, T.G. and Lauffer, J.P. (1995), "The natural excitation technique (NExT) for modal parameter extraction from operating wind turbines", Int. J. Anal. Exper. Modal Anal., 10(4), 260-277.
  21. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", J. Guid. Control Dynam., 8(5), 620-627. https://doi.org/10.2514/3.20031
  22. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  23. Ko, J.M., Sun, Z.G. and Ni, Y.Q. (2002), "Multi-stage identification scheme for detecting damage in cable-stayed Kap Shui Mun Bridge", Eng. Struct., 24(7), 857-868. https://doi.org/10.1016/S0141-0296(02)00024-X
  24. Liu, Y.C., Loh, C.H. and Ni, Y.Q. (2013), "Stochastic subspace identification for output-only modal analysis: application to super high-rise tower under abnormal loading condition", Earthq. Eng. Struct. D., 42(4), 477-498. https://doi.org/10.1002/eqe.2223
  25. Loh, C.H., Liu, Y.C. and Ni, Y.Q. (2012), "SSA-based stochastic subspace identification of structures from output-only vibration measurements", Smart Struct. Syst., 10(4-5), 331-351. https://doi.org/10.12989/sss.2012.10.4_5.331
  26. Magalhaes, F., Caetano, E. and Cunha, A . (2007). "Challenges in the application of stochastic modal identification methods to a cable-stayed bridge", J. Bridge Eng. - ASCE, 12(6), 746-754. https://doi.org/10.1061/(ASCE)1084-0702(2007)12:6(746)
  27. Magalhaes, F., Cunha, A . and Caetano, E. (2008), "Dynamic monitoring of a long span arch bridge", Eng. Struct., 30(11), 3034-3044. https://doi.org/10.1016/j.engstruct.2008.04.020
  28. Nayeri, R.D., Tasbihgoo, F., Wahbeh, M., Caffrey, J.P., Masri, S.F., Conte, J.P. and Elgamal, A. (2009), "Study of time-domain techniques for modal parameter identification of a long suspension bridge with dense sensor arrays", J. Eng. Mech. - ASCE, 135(7), 669-683. https://doi.org/10.1061/(ASCE)0733-9399(2009)135:7(669)
  29. 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. https://doi.org/10.1260/1369-4332.14.1.103
  30. Peeters, B. and De Roeck, G. (1999), "Reference-based stochastic subspace identification for output-only modal analysis", Mech. Syst. Signal Pr., 13(6), 855-878. https://doi.org/10.1006/mssp.1999.1249
  31. Peeters, B. and De Roeck, G. (2001), "Stochastic system identification for operational modal analysis: a review", J. Dynam. Syst. Measurement Control, 123(4), 659-667. https://doi.org/10.1115/1.1410370
  32. Peterson, L.D. (1995), "Efficient computation of the eigensystem realization algorithm", J. Guid. Control, Dynam., 18(3), 395-403. https://doi.org/10.2514/3.21401
  33. Pi, Y.L. and Mickleborough, N.C. (1989), "Modal identification of vibrating structures using ARMA model", J. Eng. Mech. - ASCE, 115(10), 2232-2250. https://doi.org/10.1061/(ASCE)0733-9399(1989)115:10(2232)
  34. Siringoringo, D.M. and Fujino, Y. (2008), "System identification of suspension bridge from ambient vibration response", Eng. Struct., 30(2), 462-477. https://doi.org/10.1016/j.engstruct.2007.03.004
  35. Van Overschee, P. and De Moor, B. (1993), "Subspace algorithms for the stochastic identification problem", Automatica, 29(3), 649-660. https://doi.org/10.1016/0005-1098(93)90061-W
  36. Vandiver, J.K., Dunwoody, A.B., Campbell, R.B. and Cook, M.F. (1982), "A mathematical basis for the random decrement vibration signature analysis technique", J. Mech. Design, 104(2), 307-313. https://doi.org/10.1115/1.3256341
  37. Weng, J.H., Loh, C.H., Lynch, J.P., Lu, K.C., Lin, P.Y. and Wang, Y. (2008), "Output-only modal identification of a cable-stayed bridge using wireless monitoring systems", Eng. Struct., 30(7), 1820-1830. https://doi.org/10.1016/j.engstruct.2007.12.002
  38. Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Control Health Monit., 11(2), 91-124. https://doi.org/10.1002/stc.33
  39. Wong, K.Y. (2007), "Design of a structural health monitoring system for long-span bridges", Struct. Infrastruct. E., 3(2), 169-185. https://doi.org/10.1080/15732470600591117

Cited by

  1. Investigation of modal identification and modal identifiability of a cable-stayed bridge with Bayesian framework vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.445
  2. Wind and traffic-induced variation of dynamic characteristics of a cable-stayed bridge - benchmark study vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.491
  3. Mode identifiability of a cable-stayed bridge based on a Bayesian method vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.471
  4. Mode identifiability of a multi-span cable-stayed bridge utilizing stabilization diagram and singular values vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.391
  5. Automated identification of the modal parameters of a cable-stayed bridge: Influence of the wind conditions vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.431
  6. Operational modal identification and finite element model updating of a coupled building following Bayesian approach vol.25, pp.2, 2018, https://doi.org/10.1002/stc.2089
  7. Modal identifiability of a cable-stayed bridge using proper orthogonal decomposition vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.413
  8. Optimal reduction from an initial sensor deployment along the deck of a cable-stayed bridge vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.523
  9. Structural identification of cable-stayed bridge under back-to-back typhoons by wireless vibration monitoring vol.88, 2016, https://doi.org/10.1016/j.measurement.2016.03.032
  10. Investigation of Operational Modal Identification of a Cable-Stayed Bridge Based on Bayesian Estimation Considering Stochastic Uncertainty vol.72, pp.2, 2016, https://doi.org/10.2208/jscejam.72.I_751
  11. Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification vol.17, pp.3, 2016, https://doi.org/10.12989/sss.2016.17.3.363
  12. Dynamic Property Evaluation of a Long-Span Cable-Stayed Bridge (Sutong Bridge) by a Bayesian Method pp.1793-6764, 2018, https://doi.org/10.1142/S0219455419400108
  13. System identification of the suspension tower of Runyang Bridge based on ambient vibration tests vol.19, pp.5, 2015, https://doi.org/10.12989/sss.2017.19.5.523
  14. Mode identifiability of a cable-stayed bridge using modal contribution index vol.20, pp.2, 2017, https://doi.org/10.12989/sss.2017.20.2.115
  15. Markov chain Monte Carlo-based Bayesian model updating of a sailboat-shaped building using a parallel technique vol.193, pp.None, 2019, https://doi.org/10.1016/j.engstruct.2019.05.023
  16. Automatic identification of modal parameters for structures based on an uncertainty diagram and a convolutional neural network vol.28, pp.None, 2015, https://doi.org/10.1016/j.istruc.2020.08.077
  17. Fast operational modal analysis of a single-tower cable-stayed bridge by a Bayesian method vol.174, pp.None, 2021, https://doi.org/10.1016/j.measurement.2021.109048