DOI QR코드

DOI QR Code

Parametric identification of a cable-stayed bridge using least square estimation with substructure approach

  • Huang, Hongwei (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University) ;
  • Yang, Yaohua (Department of Bridge Engineering, Tongji University) ;
  • Sun, Limin (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University)
  • Received : 2014.11.19
  • Accepted : 2014.01.16
  • Published : 2015.02.25

Abstract

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

Keywords

Acknowledgement

Supported by : Science and Technology Commission of Shanghai Municipality

References

  1. Bernal, D., and Beck, J. (2004), "Special section: Phase I of the IASC-ASCE structural health monitoring benchmark", J. Eng. Mech. - ASCE, 130(1), 1-127. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(1)
  2. Caravani, P., Watson, M.L. and Thomson, W.T. (1977), "Recursive least-squares time domain identification of structural parameters", J. Appl. Mech. - T ASME, 44(1), 135-140. https://doi.org/10.1115/1.3423979
  3. Dong, S. and Sun, L.M. (2010), Extreme load identification and early warning research of cable-stayed bridge based on monitoring data, Master Thesis, Tongji University, Shanghai, China.
  4. Koh, C.G., Hong, B. and Liaw, C.Y. (2003), "Substructural and progressive structural identification methods", Eng. Struct., 25(12), 1551-1563. https://doi.org/10.1016/S0141-0296(03)00122-6
  5. Koh, C.G., See, L.M. and Balendra, T. (1991), "Estimation of structural parameters in time domain: a substructure approach", Earthq. Eng. Struct. D., 20(8), 787-801. https://doi.org/10.1002/eqe.4290200806
  6. Law, S.S. and Yong, D. (2011), "Substructure methods for structural condition assessment", J. Sound Vib., 330(15), 3606-3619. https://doi.org/10.1016/j.jsv.2011.03.003
  7. Lei, Y, Jiang, Y.Q. and Xu, Z.Q. (2012), "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
  8. Lei, Y., Liu, C., Jiang, Y.Q. and Mao, Y.K. (2013), "Substructure based structural damage detection with limited input and output measurements", Smart Struct. Syst., 12(6), 619-640. https://doi.org/10.12989/sss.2013.12.6.619
  9. Lin, S., Yang, J.N. and Zhou, L. (2005), "Damage identification of a benchmark problem for structural health monitoring", J. Smart Mater. Struct., 14, S162-S169. https://doi.org/10.1088/0964-1726/14/3/019
  10. Weng, S., Xia, Y. and Zhou, X.Q. (2012), "Inverse substructure method for model updating of structures", J. Sound Vib., 331(25), 5449-5468. https://doi.org/10.1016/j.jsv.2012.07.011
  11. Yang, J.N. and Lin, S. (2004), "On-line identification of nonlinear hysteretic structures using an adaptive tracking technique", Int. J. Nonlinear Mech., 39, 1481-1491. https://doi.org/10.1016/j.ijnonlinmec.2004.02.010
  12. Yang, J.N. and Lin, S. (2005), "Identification of parametric variations of structures based on least squares estimation and adaptive tracking technique", J. Eng. Mech.- ASCE, 131(3), 290-298. https://doi.org/10.1061/(ASCE)0733-9399(2005)131:3(290)
  13. Yang, J.N., Pan, S. and Lin, S. (2007a), "Least squares estimation with unknown excitations for damage identification of structures", J. Eng. Mech.- ASCE, 133(1), 12-21. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:1(12)
  14. Yi, T.H., Li, H.N. and Gu, M. (2010), "Full-scale measurement of dynamic response of a suspension bridge subjected to environmental loads using GPS technology", Science China: Technol. Sci., 53(2), 469-479. https://doi.org/10.1007/s11431-010-0051-2
  15. Yi, T.H., Li, H.N. and Gu, M. (2013a), "Wavelet based multi-step filtering method for bridge health monitoring using GPS and accelerometer", Smart Struct. Syst., 11(4), 331-348. https://doi.org/10.12989/sss.2013.11.4.331
  16. Yi, T.H., Li, H.N. and Sun, H.M. (2013b), "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
  17. Zhou, L. and Yan, G. (2006), "HHT method for system identification and damage detection: an experimental study", Smart Struct. Syst., 2(2), 141-154. https://doi.org/10.12989/sss.2006.2.2.141

Cited by

  1. Optimization of the selective catalytic reduction structure of a vehicle based on the improvement in the uniformity of the nitrogen oxides distribution vol.232, pp.2, 2018, https://doi.org/10.1177/0954407017694276
  2. Identification of structural dynamic characteristics based on machine vision technology 2018, https://doi.org/10.1016/j.measurement.2017.09.043
  3. Numerical studies on the effect of measurement noises on the online parametric identification of a cable-stayed bridge vol.19, pp.3, 2015, https://doi.org/10.12989/sss.2017.19.3.259
  4. Identification of plastic deformations and parameters of nonlinear single-bay frames vol.22, pp.3, 2018, https://doi.org/10.12989/sss.2018.22.3.315