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Variability analysis on modal parameters of Runyang Bridge during Typhoon Masta

  • Mao, Jian-Xiao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Wang, Hao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Xun, Zhi-Xiang (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Zou, Zhong-Qin (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University)
  • Received : 2016.09.19
  • Accepted : 2017.01.17
  • Published : 2017.06.25

Abstract

The modal parameters of the deck of Runyang Suspension Bridge (RSB) as well as their relationships with wind and temperature are studied based on the data recorded by its Structural Health Monitoring System (SHMS). Firstly, frequency analysis on the vertical responses at the two sides of the deck is carried out to distinguish the vertical and torsional vibration modes. Then, the vertical, torsional and lateral modal parameters of the deck of RSB are identified using Hilbert-Huang Transform (HHT) and validated by the identified results before RSB was opened to traffic. On the basis of this, the modal frequencies and damping ratios of RSB during the whole process of Typhoon Masta are obtained. And the correlation analysis on the modal parameters and wind environmental factors is then conducted. Results show that the HHT can achieve an accurate modal identification of RSB and the damping ratios show an obvious decay trend as the frequencies increase. Besides, compared to frequencies, the damping ratios are more sensitive to the environmental factors, in particular, the wind speed. Further study on configuring the variation law of modal parameters related with environmental factors should be continued.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, University of Ministry of Education, Higher Education Institutions of China

References

  1. 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)
  2. Brincker, R., Zhang, L. and Andersen, P. (2001), "Modal identification of output-only systems using frequency domain decomposition", Smart Mater. Struct., 10(3), 441. https://doi.org/10.1088/0964-1726/10/3/303
  3. Brownjohn, J.M.W. (2003), "Ambient vibration studies for system identification of tall buildings". Earthq. Eng. Struct. D., 32(1), 71-95. https://doi.org/10.1002/eqe.215
  4. Chen, J., Xu, Y.L. and Zhang, R.C. (2004), "Modal parameter identification of Tsing Ma suspension bridge under Typhoon Victor: EMD-HT method", J. Wind Eng. Ind. Aerod., 92(10), 805-827. https://doi.org/10.1016/j.jweia.2004.04.003
  5. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Digest, 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  6. Feng, M.Q., Kim, J.M. and Xue, H. (1998), "Identification of a dynamic system using ambient vibration measurements", J. Appl. Mech., 65(4), 1010-1021. https://doi.org/10.1115/1.2791895
  7. Gao, F. and Wang, H.Q. (2008), "Numerical simulation and structure analysis of typhoon MATSA (0509)", Acta Scientiarum Naturalium Universitatis Pekinensis, 44(3), 385-390. (In Chinese)
  8. Hua, X.G., Ni, Y.Q., Ko, J.M., et al. (2007), "Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique", J. Comput. Civil Eng., 21(2), 122-135. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:2(122)
  9. Kendall, M.G. (1979), The Advanced Theory of Statistics (4th Ed). London, Macmillan.
  10. Kim, R.E., Moreu, F. and Spencer, Jr. B.F. (2015), "System identification of an in-service railroad bridge using wireless smart sensors", Smart Struct. Syst., 15(3), 683-698. https://doi.org/10.12989/sss.2015.15.3.683
  11. Li, H., Li, S.L., Ou, J.P., et al (2010), "Modal identification of bridges under varying environmental conditions: temperature and wind effects", Struct. Control Health Monit., 17(5), 495-512. https://doi.org/10.1002/stc.319
  12. Li, J., Ruiz-Sandoval, M., Spencer, Jr. B.F., et al. (2014), "Parametric time-domain identification of multiple-input systems using decoupled output signals", Earthq. Eng. Struct. D., 43(9), 1307-1324. https://doi.org/10.1002/eqe.2398
  13. Nazarian, E., Ansari, F., Zhang, X., et al (2016), "Detection of tension loss in cables of cable-stayed bridges by distributed monitoring of bridge deck strains", J. Struct. Eng.-ASCE, 142(6), 04016018. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001463
  14. Ni, Y.Q., Hua, X.G., Fan, K.Q., et al (2005), "Correlating modal properties with temperature using long-term monitoring data and support vector machine technique", Eng. Struct., 27(12), 1762-1773. https://doi.org/10.1016/j.engstruct.2005.02.020
  15. Ou, J.P. and Li, H. (2010), "Structural health monitoring in mainland China: review and future trends", Struct. Health Monit., 9(3), 219-231. https://doi.org/10.1177/1475921710365269
  16. Pearson, K. (1901), "On lines and planes of closest fit to systems of points in space", Philosoph. Mag., 2(6), 559-572.
  17. Peeters, B. and De Roeck, G. (2001), "Stochastic system identification for operational modal analysis: a review", J. Dynam. Syst. Meas. Control, 123(4), 659-667. https://doi.org/10.1115/1.1410370
  18. Peterson, L.D., Bullock, S.J. and Doebling, S.W. (1996), "The statistical sensitivity of experimental modal frequencies and damping ratios to measurement noise", Int. J. Anal. Exp.Modal Anal., 11(1), 63-75.
  19. Qin, Q., Li, H.B., Qian, L.Z., et al. (2001), "Modal identification of Tsing Ma bridge by using improved eigensystem realization algorithm", J. Sound Vib., 247(2), 325-341. https://doi.org/10.1006/jsvi.2001.3751
  20. Ren, W.X. and De Roeck, G. (2002), "Structural damage identification using modal data. I: Simulation verification", J. Struct. Eng.-ASCE, 128(1), 87-95. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:1(87)
  21. Reynders, E. (2012), "System identification methods for (operational) modal analysis: review and comparison", Arch. Comput. Method. E., 19(1), 51-124. https://doi.org/10.1007/s11831-012-9069-x
  22. Shlens, J. (2014), A tutorial on principal component analysis [Online]. Available: http://arxiv.org/abs/1404.1100.
  23. Spencer, Jr. B.F., Ruiz-Sandoval, M.E. and Kurata, N. (2004), "Smart sensing technology: opportunities and challenges", Struct.Control Health Monit., 11(4), 349-368. https://doi.org/10.1002/stc.48
  24. Wang, H., Li, A.Q. and Li, J. (2010). "Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements", Eng. Struct., 32(9), 2546-2556. https://doi.org/10.1016/j.engstruct.2010.04.028
  25. Wang, H., Mao, J.X., Huang, J.H., et al (2016), "Modal identification of Sutong cable-stayed bridge during typhoon Haikui using wavelet transform method", J. Perform. Constr. Fac., 30(5), 04016001. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000856
  26. Yan, B. and Miyamoto, A. (2006), "A comparative study of modal parameter identification based on wavelet and Hilbert-Huang transforms", Comput.-Aided Civil Infrastruct. Eng., 21(1), 9-23. https://doi.org/10.1111/j.1467-8667.2005.00413.x
  27. Yarnold, M.T., Moon, F.L. and Aktan, A.E. (2015), "Temperaturebased structural identification of long-span bridges", J. Struct. Eng.-ASCE, 141(11), 04015027. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001270
  28. Zhang, J., Yan, R.Q. and Yang, C.Q. (2013), "Structural modal identification through ensemble empirical modal decomposition", Smart Struct. Syst., 11(1), 123-134. https://doi.org/10.12989/sss.2013.11.1.123
  29. Zhang, Y. and Song, H.W. (2016). "Non-overlapped random decrement technique for parameter identification in operational modal analysis", J. Sound Vib., 366, 528-543. https://doi.org/10.1016/j.jsv.2015.12.025

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