Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Detection and parametric identification of structural nonlinear restoring forces from partial measurements of structural responses

  • Lei, Ying (Department of Civil Engineering, Xiamen University) ;
  • Hua, Wei (Department of Civil Engineering, Xiamen University) ;
  • Luo, Sujuan (Department of Civil Engineering, Xiamen University) ;
  • He, Mingyu (Department of Civil Engineering, Xiamen University)
  • Received : 2014.11.30
  • Accepted : 2015.03.16
  • Published : 2015.04.25
⟨ Previous Next ⟩

Abstract

Compared with the identification of linear structures, it is more challenging to conduct identification of nonlinear structure systems, especially when the locations of structural nonlinearities are not clear in structural systems. Moreover, it is highly desirable to develop methods of parametric identification using partial measurements of structural responses for practical application. To cope with these issues, an identification method is proposed in this paper for the detection and parametric identification of structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is proposed for a nonlinear structure and the locations of structural nonlinearities are detected. Then, the parameters of structural nonlinear restoring forces at the locations of identified structural nonlinearities together with the linear part structural parameters are identified by the extended Kalman filter. The proposed method simplifies the identification of nonlinear structures. Numerical examples of the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different nonlinear models and locations are used to validate the proposed method.

Keywords

References

  1. Aykan, M. and Ozguven, H.N. (2013), "Parametric identification of nonlinearity in structural systems using describing function inversion", Mech. Syst. Signal Pr., 40(1), 356-376. https://doi.org/10.1016/j.ymssp.2013.03.016
  2. Carrella, A. and Ewins, D.J. (2011), "Identifying and quantifying structural nonlinearities in engineering applications from measured frequency response functions", Mech. Syst. Signal Pr., 25(3), 1011-1027. https://doi.org/10.1016/j.ymssp.2010.09.011
  3. Dahl, P.R. (1976), "Solid friction damping of mechanical vibrations", AIAA J., 14(12), 1675-1682. https://doi.org/10.2514/3.61511
  4. Hoshiya, M. and Saito, E. (1984), "Structural identification by extended Kalman filter", J. Eng. Mech., ASCE, 110(12), 1757-1770. https://doi.org/10.1061/(ASCE)0733-9399(1984)110:12(1757)
  5. Jia, H., Xu, B. and Masri, S. (2012), "Restoring force and dynamic loadings identification for a nonlinear chain-like structure with partially unknown excitations", Nonlin. Dyn., 9(1-2), 231-245.
  6. Kaloop, M.R., Sayed, M.A., Kim, D. and Kim, E. (2014), "Movement identification model of port container crane based on structural health monitoring system", Struct. Eng. Mech., 50(1), 105-119. https://doi.org/10.12989/sem.2014.50.1.105
  7. Kerschena, G., Wordenb, K., Vakakis, A.F. and Golinvala, J.C. (2006), "Past, Present and Future of Nonlinear System Identification in Structural Dynamics", Mech. Syst. Signal Pr., 20, 505-592. https://doi.org/10.1016/j.ymssp.2005.04.008
  8. Kishore, P.R. and Shankar, K. (2009), "Parametric identification of structures with nonlinearities using global and substructure approaches in the time domain", Adv. Struct. Eng., 12(2), 195-210. https://doi.org/10.1260/136943309788251632
  9. Kumar, R.K., Sandesh, S. and Shankar, K. (2007), "Parametric identification of nonlinear dynamic systems using combined Levenberg-Marquardt and Genetic Algorithm", Int. J. Struct. Stab. Dyn., 7(4), 715-725. https://doi.org/10.1142/S0219455407002484
  10. Lee, Y.S., Vakakis, A.F., McFarland, D.M. and Bergman, L.A. (2010), "A Global-local Approach to Nonlinear System Identification: A Review", Struct. Control Hlth. Monit., 17(7), 742-760. https://doi.org/10.1002/stc.414
  11. Lei, Y., Jiang, Y.Q. and Xu, Z.Q. (2012b) "Structural damage detection with limited input and output measurement signals", Mech. Syst. Signal Pr., 28, 229-243 https://doi.org/10.1016/j.ymssp.2011.07.026
  12. Lei, Y. and He, M.Y. (2013), "Identification of the nonlinear properties of rubber-bearings in base-isolated buildings with limited seismic response data", Sci. China Technol. Sc., 56(5), 1224-1231. https://doi.org/10.1007/s11431-013-5196-3
  13. Lei, Y., Wu, Y. and Li, T. (2012a), "Identification of nonlinear structural parameters under limited input and output measurements", Int. J. Nonlin. Mech., 47(10), 1141-1146. https://doi.org/10.1016/j.ijnonlinmec.2011.09.004
  14. Li, Y.Y. and Chen, Y. (2013), "A review on recent development of vibration-based structural robust damage detection ", Struct. Eng. Mech., 45(2), 159-168. https://doi.org/10.12989/sem.2013.45.2.159
  15. Lin, J.W., Chen, C.W. and Hsu, T.C. (2013), "A novel regression prediction model for structural engineering application", Struct. Eng. Mech., 45(5), 693-702. https://doi.org/10.12989/sem.2013.45.5.693
  16. Masri, S.F., Caffrey, J.P., Caughey, T.K., Smyth, A.W. and Chassiakos, A.G. (2005), "A general data-based approach for developing reduced-order models of nonlinear MDOF systems", Nonlin. Dyn., 39(1-2), 95-112. https://doi.org/10.1007/s11071-005-1916-y
  17. Masri, S.F., Miller, R.K., Saud, A.F. and Caughey, T.K.(1987a), "Identification of nonlinear vibrating structures: part I - formulation", J. Appl. Mech., 54(4), 918-922. https://doi.org/10.1115/1.3173139
  18. Masri, S.F., Miller, R.K., Saud, A.F. and Caughey, T.K. (1987b), "Identification of nonlinear vibrating structures: part II -application", J. Appl. Mech., 54(4), 923-929. https://doi.org/10.1115/1.3173140
  19. Pai, P.F., Nguyen, B.A. and Sundaresan, M.J. (2013), "Nonlinearity identification by time-domain-only signal processing", Int. J. Nonlin. Mech., 54, 85-98. https://doi.org/10.1016/j.ijnonlinmec.2013.04.002
  20. Peng, Z.K., Lang, Z.Q. and Billings, S.A. (2008), "Nonlinear parameter estimation for multi-degree-of-freedom nonlinear systems using nonlinear output frequency-response functions", Mech. Syst. Signal Pr., 22(7), 1582-1594. https://doi.org/10.1016/j.ymssp.2008.03.011
  21. Qarib, H. and Adeli, H. (2014), "Recent advances in health monitoring of civil structures", Scientia Iranica, 21(6), 1733-1742
  22. Rochdi, Y.G., Ikhouane, F., Chaoui, F.Z. and Rodellar, J. (2009), "Parametric identification of nonlinear hysteretic systems", Nonlin. Dyn., 58(1-2), 393-404. https://doi.org/10.1007/s11071-009-9487-y
  23. Smyth, A.W., Masri, S.F., Chassiakos, A.G. and Caughey, T.K. (1999), "On-line parametric identification of MDOF nonlinear hysteretic systems", J. Eng. Mech., 125(2), 133-142. https://doi.org/10.1061/(ASCE)0733-9399(1999)125:2(133)
  24. Wang, Z.C., Geng, D., Ren, W.X., Chen, G.D. and Zhang, G.F. (2015), "Damage detection of nonlinear structures with analytical mode decomposition and Hilbert transform", Smart Struct. Syst., 15(1), 1-13. https://doi.org/10.12989/sss.2015.15.1.001
  25. Wen, Y.K. (1989), "Methods of random vibration for inelastic structures", Appl. Mech. Rev., 42(2), 39-52. https://doi.org/10.1115/1.3152420
  26. Wu, M. and Smyth, A.W. (2007), "Application of the unscented Kalman filter for real-time nonlinear structural system identification", Struct. Control Hlth., 14(7), 971-990. https://doi.org/10.1002/stc.186
  27. Yang, J.N., Huang, H. and Pan, S. (2009), "Adaptive quadratic sum-squares error for damage identification of structures", J. Eng. Mech., 135(2), 67-77 https://doi.org/10.1061/(ASCE)0733-9399(2009)135:2(67)
  28. Yang, J.N., Huang, H.W. and Lin, S. (2005), "Sequential non-linear least-square estimation for damage identification of structures" Int. J. Nonlin. Mech., 41, 124-140
  29. Yang, J.N., Lin, S., Huang, H.W. and Zhou, L. (2006), "An adaptive extended Kalman filter for structural damage identification", Struct. Control Hlth. Monit., 13, 849-867 https://doi.org/10.1002/stc.84
  30. Yi, T.H., Li, H.N. and Sun, H.M. (2013), "Multi-stage structural damage diagnosis method based on "energy-damage"theory", Smart Struct. Syst., 12(3-4), 345-361. https://doi.org/10.12989/sss.2013.12.3_4.345
  31. Yi, T.H., Li, H.N. and Zhang, X.D. (2015), "Sensor placement optimization in structural health monitoring using distributed monkey algorithm", Smart Struct. Syst., 15(1), 191-207. https://doi.org/10.12989/sss.2015.15.1.191
  32. Yuen, K.V., Liang, P.F. and Kuok, S.C. (2013), "Online estimation of noise parameters for Kalman filter", Struct. Eng. Mech., 47(3), 361-381. https://doi.org/10.12989/sem.2013.47.3.361
  33. Yun, C.B. and Shinozuka, M. (1980), "Identification of nonlinear structural dynamic systems", J. Struct. Mech., 8(2), 187-203. https://doi.org/10.1080/03601218008907359
  34. Zhang, J., Sato, T., Iai, S. and Hutchinson, T. (2008), "A pattern recognition technique for structural identification using observed vibration signals: Nonlinear case studies", Eng. Struct., 30, 1417-1423. https://doi.org/10.1016/j.engstruct.2007.08.007
  35. Zhu, J.H, Yu, L. and Yu, L.L. (2012), "An eigenspace projection clustering method for structural damage detection ", Struct. Eng. Mech., 44(2), 179-196. https://doi.org/10.12989/sem.2012.44.2.179

Cited by

  1. Synthesis of Cross-Correlation Functions of Partial Responses and the Extended Kalman Filter Approach for Structural Damage Detection Under Ambient Excitations 2017, https://doi.org/10.1142/S0219455418400035
  2. Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation vol.9, pp.2, 2019, https://doi.org/10.3390/app9020306
  3. Chaotic particle swarm optimization in optimal active control of shear buildings vol.61, pp.3, 2017, https://doi.org/10.12989/sem.2017.61.3.347
  4. On-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures vol.63, pp.6, 2017, https://doi.org/10.12989/sem.2017.63.6.789