Introducing a Method for Identification of All Dynamic Characteristics Matrices of Shear Buildings Using Output-Only Data

Document Type : Articles

Authors

1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Structural Engineering Research Center, International Institute of Earthquake Engineering and Seismology, Tehran, Iran

Abstract

Nowadays, system identification methods have found special place in civil engineering due to extended application in health monitoring and damage detection of structures. Among these, due to limitations caused by stimulation of large scale actual structures, structure engineers are mostly leading toward identification methods based on output-only data. In this study, a method was proposed based on identification of Stochastic Subspace Identification (SSI) method in time domain to identify all structural matrices including mass, damping and stiffness in shear buildings. This method is highly able to identify structural matrices even in working with noise-polluted data. When using SSI with reviewing realization theory, we have never proper information about real degrees of freedom of the system. Hence, system matrices are identifiable in various forms, all of which are true realizations of the system. To explore the main matrices of structural model, the proposed method relies on finding realization of the minimal matrix of the system in the classical form. To evaluate the effectiveness of suggested method, two numerical models of 3 and 5 stories is used. Results of the numerical analysis indicate accuracy of suggested identification method, even in using high noise polluted data.

Keywords

References

  1. Juang J-N (1994) Applied System Identification, NASA Langley Research Center. Prentice Hall PTR.
  2. Van Overschee, P. and De Moor, B. (1996) Subspace Identification for Linear Systems: Theory, Implementation, Applications.
  3. Bendat, J.S, Piersol, A.G. (1993) Engineering Application of Correlation and Spectral Analysis. 2nd edition, John Wiley & Sons, New York, NY, USA
  4. Benjamin. B. (2001) Output only modal Analysis using Frequency Domain Decomposition. Bachelor of Engineering Thesis, University of Queensland.
  5. Brinvker, R., Lingmi, Z., Anderson, P. (2000) Modal Identification from Ambient Response using Frequency Domain Decomposition. 18th International Modal Analysis Conference, San Antonio, Texas, Society for Experimental Mechanics.
  6. Peeters, B., De Roeck, G. (1999) Reference Based Stochastic Subspace Identification in Civil Engineering. 2nd International Conference on Identification in Engineering Systems, Swansea, UK, March.
  7. Yuan, P., Wu, Z., & Ma, X. (1998) Estimated mass and stiffness matrices of shear building from modal test data. Earthquake Engineering and Structural Dynamics, 27(5), 415-422.
  8. Takewaki, I. and Nakamura, M. (2000) Stiffness-damping simultaneous identification using limited earthquake records. Earthquake Engineering & Structural Dynamics, 29(8), 1219-1238.
  9. Ghafory-Ashtiany, M., Adhami, B., Khanlari, K. (2014) Identification of structural systems with full characteristic matrices under single point excitation. Journal of Sound and Vibration, 333(24), 6381-6394.
  10. Du, X.L. and Wang, F.Q. (2009) New modal identification method under the nonstationary Gaussian ambient excitation. Applied Mathematics and Mechanics, 30(10), 1295-1304
  11. Rainiery, C., Fabbrocino, G. (2010) Automaated output-only dynamic identification of civil engineering structure. Mechanical Systems and Signal Processing.
  12. Facchini, L., Betti, M., Biagini, P. (2014) Neural network based modal identification of structural systems through output-only measurement. Computers and Structures.
  13. Ni, P., Xia, Y., Hao, H., (2018) Improved decentralized structural identification with output-only measurement. Measurement, 597-610.
  14. Peeters, B. (2000) System Identification and Dmage Detection in Civil Engineering. Ph.D. Thesis, Katholieke Universiteit, Leuven, Belgium.
  15. Katayama, T. (2005) Subsapace Methods for System Identification. Springer.
  16. Brincker, R., Anderson, P. (2006) Understanding Stochastic Subspace Identification. Proceeding of International Modal Analysis Conference, IMAC.
  17. Lardies, J. (2017) Modal parameter identification by an iterative approach and by the state space model. Mechanical Systems and Signal Processing, 239-251.
  18. Nilvetti, F., Pappalardo, C.M. (2012) Mass stiffness and damping identification of a two story building model. International Journal of Mechanical Engineering and Industrial Design.
  19. Parloo, E., Verboven, P., Guillame, P. and Overmeire, M. (2000) Sensitivity-based operational mode shape normalization. Mech. Systems and Signal Proc., 16, 757-767.
  20. Parloo, E., Guillaume, P., Anthonis, J., Heylen, W. and Swers, N. (2003) Modelling of sprayer boom dynamics by means of maximum likelihood identification techniques, part 2: Sensivity-based mode shape normalization. Biosystem Engineering.
  21. Aenelle, M.L., Brincker, R., Fernandez-Canteli, A. (2005) Some methods to determine scaled mode shapes in natural input modal analysis. Proc. of the International Modal Analysis Conference (IMAC) XXIII.
  22. Malekjafarian, A., Ashory, M.R., Khatibi, M.M., Saber Latibari, M. (2016) Rigid body stiffness matrix for identification of inertia properties from output-only data. European Journal of Mechanics A/Solids, 85-94.
  23. Khatibi, M.M., Ashory, M.R., Malekjafarian, A. (2012) Mass stiffness change method for scaling of operational mode shapes. Mechanical Systems and Signal Processing.
  24. Brincker, R. and Anderson, P. (2003) A way of getting scaled mode shapes in output only modal analysis. Proc. of the International Modal Analysis Conference (IMAC) XXI.
  25. Bernal. D. (2004) Modal scaling from known mass perturbation. Journal of Engineering Mechanics, 130-1083.

Files

History

  • Receive Date: 29 November 2017
  • Revise Date: 08 February 2021
  • Accept Date: 26 December 2020

Share

How to cite

Statistics