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Comparison of black and gray box models of subspace identification under support excitations

  • Datta, Diptojit (Department of Civil Engineering, Indian Institute of Technology Guwahati) ;
  • Dutta, Anjan (Department of Civil Engineering, Indian Institute of Technology Guwahati)
  • Received : 2017.11.14
  • Accepted : 2017.12.05
  • Published : 2017.12.25

Abstract

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

Keywords

References

  1. Borasikia, A.C., Dutta, A. and Deb, S.K. (2011), "System Identification of Multistoreyed nonstandard shear building using parametric state-space modeling", Struct. Control Health Monit., 18(4), 471-480. https://doi.org/10.1002/stc.385
  2. Caicedo, J.M., Dyke, S.J. and Johnson E.A. (2004), "Natural excitation technique and eigen system realization algorithm for phase I of the IASC-ASCE benchmark problem: Simulated data", J. Eng. Mech. - ASCE, 130(1), 49-60. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(49)
  3. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "The phase-I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech. - ASCE, 130(1), 207-218.
  4. Kim, J. and Lynch, J.P. (2012), "Subspace system identification of support excited structures-Part i: theory and black-box system identification", Earthq. Eng. Struct. D., 41(15), 2235-2251. https://doi.org/10.1002/eqe.2184
  5. Kim, J. and Lynch, J.P. (2012), "Subspace system identification of support excited structures-Part ii: gray-box interpretations and damage detection", Earthq. Eng. Struct. D., 41(15), 2253-2271. https://doi.org/10.1002/eqe.2185
  6. Liu, K. (1997), "Identification of linear time-varying systems", J. Shock Vib., 206(4), 487-505.
  7. Ljung, L. (1999), System Identification: Theory for the User, Prentice Hall, Upper Saddle River, NJ, USA.
  8. Lus, H., Angelis, D.M. and Betti, R. (2003), "A new approach for reduced order modeling of mechanical systems using vibration measurements", J. Appl. Mech., 70(5), 715-723. https://doi.org/10.1115/1.1602482
  9. Marchesiello S., Bellino, A. and Garibaldi, L. (2010), "Prediction of modal parameters of linear time-varying systems", J. Shock Vib., 17(5), 483-490. https://doi.org/10.1155/2010/184087
  10. Moaveni, B. and Asgarieh, E. (2012), "Deterministic-stochastic subspace identification method for identification of nonlinear structures as time-varying linear systems", Mech. Syst. Signal Pr., 31(1), 40-55. https://doi.org/10.1016/j.ymssp.2012.03.004
  11. Nagarajaiah, S. and Erazo, K. (2016), "Structural monitoring and identification of civil infrastructure in the United States", Struct. Monit. Maint., 3(1), 51-69. https://doi.org/10.12989/smm.2016.3.1.051
  12. Overschee, P.V. and Moor, B.D. (1994), "N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems", Automatica, 30(1), 75-93. https://doi.org/10.1016/0005-1098(94)90230-5
  13. Ranieri, C. and Fabbrocino, G. (2014), Operational Modal Analysis for Civil Engineering Structures, Springer-Verlag, New York City, NY, USA.
  14. Shahmedr, R. and Mussa-Ilvaldi, S. (2012), Biological Learning and Control, The MIT Press Cambridge, London, UK.
  15. Shi, Z.Y., Law, S.S. and Li, H.N. (2007), "Subspace-based identification of linear time-varying systems", AIAA J., 45(8), 2042-2050. https://doi.org/10.2514/1.28555
  16. Verdult, V. and Verhaegan, M. (2002), "Subspace identification of multivariable linear parameter-varying systems", Automatica, 38(1), 805-814. https://doi.org/10.1016/S0005-1098(01)00268-0
  17. Verhaegan, M. and Yu, X. (1995), "A class of subspace model identification algorithms to identify periodically and arbitrarily time-varying systems", Automatica, 31(2), 201-216. https://doi.org/10.1016/0005-1098(94)00091-V
  18. Xiao, H., Bruhns, O.T., Waller, H. and Meyers, A. (2001), "An input/output-based procedure for fully evaluating and monitoring dynamic properties of structural systems via a subspace identification method", J. Sound Vib., 246(4), 601-623. https://doi.org/10.1006/jsvi.2001.3650