Earthquakes and Structures

  • 제3권6호
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  • Pages.839-852
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  • 2012
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Earthquake time-frequency analysis using a new compatible wavelet function family

  • Moghaddam, Amir Bazrafshan (Department of Civil Engineering,Shahrood University of Technology) ;
  • Bagheripour, Mohammad H. (Faculty of Engineering, ShahidBahonar University of Kerman)
  • 투고 : 2012.03.27
  • 심사 : 2012.06.14
  • 발행 : 2012.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

Earthquake records are often analyzed in various earthquake engineering problems, making time-frequency analysis for such records of primary concern. The best tool for such analysis appears to be based on wavelet functions; selection of which is not an easy task and is commonly carried through trial and error process. Furthermore, often a particular wavelet is adopted for analysis of various earthquakes irrespective of record's prime characteristics, e.g. wave's magnitude. A wavelet constructed based on records' characteristics may yield a more accurate solution and more efficient solution procedure in time-frequency analysis. In this study, a low-pass reconstruction filter is obtained for each earthquake record based on multi-resolution decomposition technique; the filter is then assigned to be the normalized version of the last approximation component with respect to its magnitude. The scaling and wavelet functions are computed using two-scale relations. The calculated wavelets are highly efficient in decomposing the original records as compared to other commonly used wavelets such as Daubechies2 wavelet. The method is further advantageous since it enables one to decompose the original record in such a way that a clear time-frequency resolution is obtained.

키워드

참고문헌

  1. Abry, P. (1997), Ondelettes et turbulence. Multiresolutions, algorithmes de decomposition, invariance dechelles, Diderot Editeur, Paris (in French).
  2. Al-Badour, F., Sunar, M. and Cheded, L. (2011), "Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques", Mech. Syst. Signal Pr., 25(6), 2083-2101. https://doi.org/10.1016/j.ymssp.2011.01.017
  3. Bagheripour, M.H., Rahgozar, R. and Malekinejad, M. (2010), "Efficient analysis of SSI problems using infinite elements and wavelet theory", Geomech. Eng., 2(4), 229-252. https://doi.org/10.12989/gae.2010.2.4.229
  4. Bazrafshan Moghaddam, A. and Bagheripour, M.H. (2011), "Ground response analysis using non-recursive matrix implementation of hybrid frequency-time domain (HFTD) approach", Sci. Iran., 18(6), 1188-1197. https://doi.org/10.1016/j.scient.2011.11.019
  5. Boggess, A. and Narcowich, F.J. (2001), A first course in wavelets with Fourier analysis, Prentice-Hall, Englewood Cliffs, NJ.
  6. Chang, C. and Shi, Y. (2010), "Identification of time-varying hysteretic structures using wavelet multiresolution analysis", Int. J. Nonlinear Mech., 45(1), 21-34. https://doi.org/10.1016/j.ijnonlinmec.2009.08.009
  7. Das, S. and Gupta, V.K. (2011), "A wavelet-based parametric characterization of temporal features of earthquake accelerograms", Eng. Struct., 33(7), 2173-2185. https://doi.org/10.1016/j.engstruct.2011.03.008
  8. Daubechies, I. (1992), "Ten lectures on wavelets", CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM, 61, Philadelphia, PA.
  9. Dick, A.J., Phan, Q.M., Foley, J.R. and Spanos, P.D. (2012), "Calculating scaling function coefficients from system response data for new discrete wavelet families", Mech. Syst. Signal Pr., 27, 362-369. https://doi.org/10.1016/j.ymssp.2011.09.015
  10. Giaralis, A. and Spanos, P.D. (2009), "Wavelet-based response spectrum compatible synthesis of accelerograms-Eurocode application (EC8)", Soil Dyn. Earthq. Eng., 29(1), 219-235. https://doi.org/10.1016/j.soildyn.2007.12.002
  11. Goswami, J.C. and Chan, A.K. (1999), Fundamentals of wavelets: theory, algorithms, and applications, John Wiley & Sons, Inc., New York.
  12. Gupta, A., Joshi, S.D. and Prasad, S. (2005), "A new method of estimating wavelet with desired features from a given signal", Signal Process., 85(1), 147-161. https://doi.org/10.1016/j.sigpro.2004.09.008
  13. Iyama, J. (2005), "Estimate of input energy for elasto-plastic SDOF systems during earthquakes based on discrete wavelet coeffcients", Earthq. Eng. Struct. D., 34(15), 1799-1815. https://doi.org/10.1002/eqe.511
  14. Liu, W. and Tang, B. (2011), "A hybrid time-frequency method based on improved Morlet wavelet and auto terms window", Expert Syst. Appl., 38(6), 7575-7581. https://doi.org/10.1016/j.eswa.2010.12.107
  15. MATLAB (2011), The language of technical computing, Mathworks Inc., USA.
  16. Misiti, M., Misity, Y., Oppenheim, G. and Poqqi, J.M. (2011), Matlab wavelet toolbox user's guide, The MathWorks Inc., USA.
  17. Radunovic, D.P. (2009), Wavelets from math to practice, Springer, Serbia.
  18. Takewaki, I. and Tsujimoto, H. (2011), "Scaling of design earthquake ground motions for tall buildings based on drift and input energy demands", Earthq. Struct., 2(2), 171-187. https://doi.org/10.12989/eas.2011.2.2.171
  19. To, C.A., Moore, J.R. and Glaser, S.D. (2009), "Wavelet denoising techniques with applications to experimental geophysical data", Signal Process., 89(2), 144-160. https://doi.org/10.1016/j.sigpro.2008.07.023
  20. Yan, R. and Gao, R.X. (2010), "Design of an impulse wavelet for structural defect identification", IEEE PHM, Macau, China, (art. no. 5414567).

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  1. Seismic response analysis of layered soils considering effect of surcharge mass using HFTD approach. Part II: Nonlinear HFTD and numerical examples vol.6, pp.6, 2014, https://doi.org/10.12989/gae.2014.6.6.531
  2. Optimization of ground response analysis using wavelet-based transfer function technique vol.7, pp.2, 2014, https://doi.org/10.12989/gae.2014.7.2.149
  3. Identification of soil liquefaction occurrence using wavelet analysis vol.2, pp.10, 2012, https://doi.org/10.1007/s42452-020-03479-3