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Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification
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  • Journal title : Smart Structures and Systems
  • Volume 17, Issue 3,  2016, pp.363-389
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2016.17.3.363
 Title & Authors
Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification
Wu, Wen-Hwa; Wang, Sheng-Wei; Chen, Chien-Chou; Lai, Gwolong;
 Abstract
Deficient modes that cannot be always identified from different sets of measurement data may exist in the application of operational modal analysis such as the stochastic subspace identification techniques in large-scale civil structures. Based on a recent work using the long-term ambient vibration measurements from an instrumented cable-stayed bridge under different wind excitation conditions, a benchmark problem is launched by taking the same bridge as a test bed to further intensify the exploration of mode identifiability. For systematically assessing this benchmark problem, a recently developed SSI algorithm based on an alternative stabilization diagram and a hierarchical sifting process is extended and applied in this research to investigate several sets of known and blind monitoring data. The evaluation of delicately selected cases clearly distinguishes the effect of traffic excitation on the identifiability of the targeted deficient mode from the effect of wind excitation. An additional upper limit for the vertical acceleration amplitude at deck, mainly induced by the passing traffic, is subsequently suggested to supplement the previously determined lower limit for the wind speed. Careful inspection on the shape vector of the deficient mode under different excitation conditions leads to the postulation that this mode is actually induced by the motion of the central tower. The analysis incorporating the tower measurements solidly verifies this postulation by yielding the prevailing components at the tower locations in the extended mode shape vector. Moreover, it is also confirmed that this mode can be stably identified under all the circumstances with the addition of tower measurements. An important lesson learned from this discovery is that the problem of mode identifiability usually comes from the lack of proper measurements at the right locations.
 Keywords
stochastic subspace identification;cable-stayed bridge;mode identifiability;alternative stabilization diagram;hierarchical sifting process;
 Language
English
 Cited by
1.
Assessment of environmental and nondestructive earthquake effects on modal parameters of an office building based on long-term vibration measurements, Smart Materials and Structures, 2017, 26, 5, 055034  crossref(new windwow)
 References
1.
Alicioglu, B. and Lus, H. (2008), "Ambient vibration analysis with subspace methods and automated mode selection: case studies", J. Struct. Eng.-ASCE, 134(6), 1016-1029. crossref(new window)

2.
Bakir, P.G. (2011), "Automation of the stabilization diagram for subspace based system identification", Expert Syst. Appl., 38(12), 14390-14397. crossref(new window)

3.
Bendat, J.S. and Piersol, A.G. (1986), Random Data: Analysis and Measurement Procedures, John Wiley & Sons, New York, USA.

4.
Brinker, R., Zhang, L. and Andersen, P. (2001), "Modal identification of output-only systems using frequency domain decomposition", Smart Mater. Struct., 10(3), 441-445. crossref(new window)

5.
Carden, E.P. and Brownjohn J.M. (2008), "Fuzzy clustering of stability diagrams for vibration-based structural health monitoring", Comput.-Aided Civil Infrastruct. Eng., 23(5), 360-372. crossref(new window)

6.
Faravelli, L., Ubertini, F. and Fuggini, C. (2011), "System identification of a super high-rise building via a stochastic subspace approach", Smart Struct. Syst., 7(2), 133-152. crossref(new window)

7.
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, September.

8.
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. crossref(new window)

9.
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)

10.
Ljung, L. (1987), System Identification: Theory for the User, Prentice-Hall, Englewood Cliffs, New Jersey, USA.

11.
Ni, Y.Q., Wong, K.Y. and Xia, Y.X. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. crossref(new window)

12.
Ni, Y.Q., Wang, Y.W. and Xia, Y.X. (2015), "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)

13.
Parloo, E., Guillaume, P., Cauberghe, B. (2003), "Maximum likelihood identification of non-stationary operational data", J. Sound Vib., 268(5), 971-991. crossref(new window)

14.
Peeters, B. (2000), "System identification and damage detection in civil engineering", Ph.D. Dissertation, Katholieke Universiteit Leuven, Leuven.

15.
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. crossref(new window)

16.
Reynders, E., Houbrects, J. and De Roeck, G. (2012), "Fully automated (operational) modal analysis", Mech. Syst. Signal Pr., 29, 228-250. crossref(new window)

17.
Scionti, M. and Lanslots, J.P. (2005), "Stabilisation diagrams: pole identification using fuzzy clustering techniques", Adv. Eng. Softw., 36(11), 768-779. crossref(new window)

18.
Ubertini, F., Gentile, C. and Materazzi, A.L. (2013), "Automated modal identification in operational conditions and its application to bridges", Eng. Struct., 46, 264-278. crossref(new window)

19.
Van Overschee, P. and De Moor, B. (1991), "Subspace algorithm for the stochastic identification problem", Proceedings of the 30th IEEE Conference on Decision and Control, Brighton, England, December.

20.
Van Overschee, P. and De Moor, B. (1993), "Subspace algorithms for the stochastic identification problem", Automatica, 29(3), 649-660. crossref(new window)

21.
Van Overschee, P. and De Moor, B. (1996), Subspace Identification for Linear Systems: Theory-Implementation-Applications, Kluwer Academic Publishers, Dordecht, Netherlands.

22.
Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Control Health Monit., 11(2), 91-124. crossref(new window)

23.
Wong, K.Y. (2007), "Design of a structural health monitoring system for long-span bridges", Struct. Infrastruct. E., 3(2), 169-185. crossref(new window)

24.
Wu, W.H., Chen, C.C., Wang, S.W. and Lai, G. (2014), "Modal parameter determination of stay cable with an improved algorithm based on stochastic subspace identification", Proceedings of 7th European Workshop on Structural Health Monitoring, Nantes, France, July.

25.
Wu, W.H., Wang. S.W., Chen, C.C. and Lai, G. (2016), "Application of stochastic subspace identification for stay cables with an alternative stabilization diagram and hierarchical sifting process", Struct. Control Health Monit., in press (DOI: 10.1002/stc.1836). crossref(new window)