Structural Engineering and Mechanics

  • 제67권4호
  • /
  • Pages.359-368
  • /
  • 2018
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural identification based on substructural technique and using generalized BPFs and GA

  • Ghaffarzadeh, Hosein (Department of Structural Engineering, University of Tabriz) ;
  • Yang, T.Y. (Department of Civil Engineering, University of British Columbia) ;
  • Ajorloo, Yaser Hosseini (Department of Civil Engineering, K. N. Toosi University of Technology)
  • 투고 : 2017.11.23
  • 심사 : 2018.06.12
  • 발행 : 2018.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, a method is presented to identify the physical and modal parameters of multistory shear building based on substructural technique using block pulse generalized operational matrix and genetic algorithm. The substructure approach divides a complete structure into several substructures in order to significantly reduce the number of unknown parameters for each substructure so that identification processes can be independently conducted on each substructure. Block pulse functions are set of orthogonal functions that have been used in recent years as useful tools in signal characterization. Assuming that the input-outputs data of the system are known, their original BP coefficients can be calculated using numerical method. By using generalized BP operational matrices, substructural dynamic vibration equations can be converted into algebraic equations and based on BP coefficient for each story can be estimated. A cost function can be defined for each story based on original and estimated BP coefficients and physical parameters such as mass, stiffness and damping can be obtained by minimizing cost functions with genetic algorithm. Then, the modal parameters can be computed based on physical parameters. This method does not require that all floors are equipped with sensor simultaneously. To prove the validity, numerical simulation of a shear building excited by two different normally distributed random signals is presented. To evaluate the noise effect, measurement random white noise is added to the noise-free structural responses. The results reveal the proposed method can be beneficial in structural identification with less computational expenses and high accuracy.

키워드

참고문헌

  1. Ajorloo, Y.H. and Ghaffarzadeh, H. (2017), "Identification of structural dynamic parameters using block pulse functions and recursive least-squares algorithm", Iran J. Sci. Technol. Tran. Civil Eng., 41(2), 149-158.
  2. Babolian, E. and Masouri, Z. (2008), "Direct method to solve Volterra integral equation of the first kindusing operational matrix with block-pulse functions", J. Comput. Appl. Math., 220, 51-57. https://doi.org/10.1016/j.cam.2007.07.029
  3. Bouafoura, M.K., Moussi, O. and Braiek, N.B. (2010), "A fractional state space realization method with block pulse basis", Signal Pr., 91, 492-497.
  4. De callafon, R.A., Moaveni, B., Ponte, J.P., He, X. and Udd, E. (2008), "General realization algorithm for modal identification of linear dynamic systems", J. Eng. Mech., 134(9), 712-722. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:9(712)
  5. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: A review and comparative study", Struct. Hlth. Manit., 10(1), 83-111.
  6. Garevski, M. (2013), Earthquakes and Health Monitoring of Civil Structures, Springer Dordrecht Heidelberg.
  7. Ghaffarzadeh, H. and Younespour, A. (2015), "Block pulse transform method for linearization of nonlinear SDOF systems", Nonlin. Eng., 4(2), 77-82.
  8. James, G.H., Carne, T.G. and Lauffer, J.P. (1993), The Natural Excitation Technique for Modal Parameters Extraction from Operating wind Turbines, SAND92-1666, UC-261, Sandia National Laboratories, Sandia, New Mexico.
  9. Jiang, Z.H. and Schaufelberger, W. (1992), Block Pulse Functions and Their Applications in Control Systems, Springer-Verlag, Berlin Heidelberg.
  10. Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization algorithm for modal parameter identification and model reduction", Guid. Control Dyn., 8, 620-627. https://doi.org/10.2514/3.20031
  11. Khanmirza, E., Khaji, N. and Majd, V.J. (2011), "Model updating of multistory shear buildings for simultaneous identification of mass, stiffness and damping matrices using two different soft-computing methods", Exp. Syst. Appl., 38, 5320-5329. https://doi.org/10.1016/j.eswa.2010.10.026
  12. Koh, C.G. and Perry, M.J. (2010), Structural Identification and Damage Detection using Genetic Algorithms, CRC Press.
  13. Koh, C.G. and Shankar, K. (2003), "Substructural identification method without interface measurement", J. Eng. Mech., 129(7), 769-776. https://doi.org/10.1061/(ASCE)0733-9399(2003)129:7(769)
  14. Koh, C.G., Hong, B. and Liaw, C.Y. (2000), "Parameter identification of large structural systems in time domain", J. Struct. Eng., 126, 957-963. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:8(957)
  15. Koh, C.G., Hong, B. and Liaw, C.Y. (2003), "Substructural and progressive structural identification methods", Eng. Struct., 25(12), 1551-63. https://doi.org/10.1016/S0141-0296(03)00122-6
  16. Koh, C.G., See, L.M. and Balendra, T. (1991), "Estimation of structural parameters in the time domain: a substructure approach", Earthq. Eng. Struct. Dyn., 20(8), 787-801. https://doi.org/10.1002/eqe.4290200806
  17. Kuwabara, M., Yoshitomi, S. and Takewaki, I. (2013), "A new approach to system identification and damage detection of high-rise buildings", Struct. Control Hlth. Manit., 20(5), 703-727. https://doi.org/10.1002/stc.1486
  18. Maleknejad, K., Safdari, H. and Nouri, M. (2011), "Numerical solution of an integral equations system of the first kind by using an operational matrix with block pulse functions", Int. J. Syst. Sci., 42(1), 195-199. https://doi.org/10.1080/00207720903499824
  19. Marwala, T. (2010), Finite-Element-Model Updating using Computational Intelligence Techniques, Springer.
  20. Mei, L., Mita, A. and Zhou, J. (2015), "A substructural damage identification approach for shear structure based on changes in the first AR model coefficient matrix", Journal of Structures, 2015, Article ID 976349, 16.
  21. Mei, L., Mita, A. and Zhou, J. (2016), "An improved substructural damage detection approach of shear structure based on ARMAX model residual", Struct. Control Hlth. Manit., 23, 218-236. https://doi.org/10.1002/stc.1766
  22. Moaveni, B. (2007), "System and damage identification of civil structures", Ph.D. Dissertation, University of California, San Diego.
  23. Mohan, B.M. and Kar, S.K. (2013), Continous Time Dynamical Systems State Estimation and Optimal Control with Orthogonal Functions, CRC Press.
  24. Monti, G., Quaranta, G. and Marano, G.C. (2010), "Genetic-algorithm-based strategies for dynamic identification of nonlinear systems with noise-orrupted response", J. Comput. Civil Eng., 24, 173-187. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000024
  25. Overschee, P.V. and Moor, B.D. (1996), Subspace Identification for Linear Systems, Kluwer Academic Publishers, Massachusetts.
  26. Pacheco, R.P. and Steffen, Jr, V. (2002), "Using orthogonal functions techniques for identification and sensitivity analysis of mechanical system", J. Vib. Control, 8, 993-1021.
  27. Pastor, M., Binda, M. and Harcarik, T. (2012), "Modal assurance criterion", Procedia Eng., 48, 543-548. https://doi.org/10.1016/j.proeng.2012.09.551
  28. Perry, M.J., Koh, C.G. and Choo, Y.S. (2006), "Modified genetic algorithm strategy for structural identification", Comput. Struct., 84, 529-540. https://doi.org/10.1016/j.compstruc.2005.11.008
  29. Sannuti, P. (1997), "Analysis and synthesis of dynamic system via block pulse functions", IEE Proc., 124, 569-571.
  30. Sirca, Jr. G.F. and Adeli, H. (2012), "System identification in structural engineering", Scientia Iranica, 19, 1355-1364. https://doi.org/10.1016/j.scient.2012.09.002
  31. Tee, K.F., Koh, C.G. and Quek, S.T. (2005), "Substructural firstand second-order model identification for structural damage assessment", Earthq. Eng. Struct. Dyn., 34(15), 1755-1775. https://doi.org/10.1002/eqe.500
  32. Wang, C.H. (1982), "Generalized block pulse operational matrices and their applications to operational calculus", Int. J. Control, 36, 67-76. https://doi.org/10.1080/00207178208932875
  33. Wang, G.S. (2009), "Application of hybrid genetic algorithm to system identification", Struct. Control Hlth. Monit., 16, 125-153. https://doi.org/10.1002/stc.306
  34. Weng, S.H., Xia, Y., Zhou, X.Q., Xu, Y.L and Zhu, H.P. (2012), "Inverse substructur emethod for model updating of structures", J. Sound Vib., 331, 5449-5468. https://doi.org/10.1016/j.jsv.2012.07.011
  35. Xing, Z. and Mita, A. (2012), "A substructure approach to local damage detection of shear structure", Struct. Control Hlth. Monit., 19(2), 309-318. https://doi.org/10.1002/stc.439
  36. Yinggan, T., Haifang, L. and Weiwei, W. (2015), "Parameter identification of fractional order systems using block pulse functions", Signal Pr., 107, 272-281. https://doi.org/10.1016/j.sigpro.2014.04.011
  37. Younespour, A. and Ghaffarzadeh, H. (2014), "Structural active vibration control using active mass damper by block pulse functions", J. Vib. Control, 21(14), 2787-2795. https://doi.org/10.1177/1077546313519285
  38. Younespour, A. and Ghaffarzadeh, H. (2016), "Semi-active control of seismically excited structures with variable orifice damper using block pulse functions", Smart Struct. Syst., 18(6), 1111-1123. https://doi.org/10.12989/sss.2016.18.6.1111
  39. Zhang, D., Li, H. and Bao, Y. (2014), "Substructure parameter estimation for shear structures with limited measurements and unknown structural mass", Proceedings of SPIE in Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, San Diego, Calif, USA.