DOI QR코드

DOI QR Code

Application of neural networks and an adapted wavelet packet for generating artificial ground motion

  • Asadi, A. (Department of Civil Engineering, Yazd University) ;
  • Fadavi, M. (Department of Computer Engineering, Shomal University) ;
  • Bagheri, A. (Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science & Technology) ;
  • Ghodrati Amiri, G. (Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science & Technology)
  • 투고 : 2009.08.15
  • 심사 : 2010.11.17
  • 발행 : 2011.03.25

초록

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

키워드

참고문헌

  1. Abhijeet Shinde, D. (2004), "A wavelet packet based sifting process and its application for structural health monitoring", Master Thesis, Faculty of Worcester Polytechnic Institute.
  2. Benedetto, J.J. and Frazier, M.W. (1994), WAVELETS: Mathematics and Applications, CRC Press, Boca Raton.
  3. Building and Housing Research Center (BHRC) (2006), Iranian Code of Practice for Seismic Resistance Design of Buildings, Standard No.2800, 3rd Edition, Tehran, Iran.
  4. Chui, C.K. and Wang, J.Z. (1992), "On compactly supported spline wavelets and a duality principle", T. Am. Math. Soc., 330, 903-915. https://doi.org/10.1090/S0002-9947-1992-1076613-3
  5. Coifman, R.R. and Wickerhauser, M.V. (1992), "Entropy-based algorithms for best-basis selection", IEEE T. Inform. Theory, 38(2), 713-718. https://doi.org/10.1109/18.119732
  6. Daubechies, I. (1992), "Ten lectures on wavelets", CBMS-NSF Conference Series in Applied Mathematics, Montpelier, Vermont.
  7. Daubechies, I. (1988), "Orthonormal bases of compactly supported wavelets", Commun. Pure Appl. Math., 41, 909-996. https://doi.org/10.1002/cpa.3160410705
  8. Fan, F.G. and Ahmadi, G. (1990), "Nonstationary Kanai-Tajimi models for El Centro 1940 and Mexico City 1985 earthquake", Prob. Eng. Mech., 5, 171-181. https://doi.org/10.1016/0266-8920(90)90018-F
  9. Fan, X. and Zuo, M.Z. (2006), "Gearbox fault detection using Hilbert and wavelet packet transform", Mech. Syst. Signal Pr., 20, 966-982. https://doi.org/10.1016/j.ymssp.2005.08.032
  10. Chopra, A.K. (1995), Dynamics of Structures, Englewood Cliffs, NJ, Prentice-Hall.
  11. Ghaboussi, J. and Lin, C.J. (1998), "New method of generating spectrum compatible accelerograms using neural networks", Earthq. Eng. Struct. D., 27, 377-396. https://doi.org/10.1002/(SICI)1096-9845(199804)27:4<377::AID-EQE735>3.0.CO;2-2
  12. Ghaboussi, J. (1999), "Biologically inspired soft computing method in structural mechanics and engineering", Proceeding of the 1st International Conference of Artificial Neural Networks in Engineering (ANNIE'97), Seoul, Korea.
  13. Ghodrati Amiri, G., Bagheri, A. and Fadavi, M. (2007), "New method for generation of artificial ground motion by a nonstationary Kanai-Tajimi model and wavelet transform", Struct. Eng. Mech., 26(6), 709-723. https://doi.org/10.12989/sem.2007.26.6.709
  14. Ghodrati Amiri, G. and Bagheri, A. (2008), "Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms", Struct. Eng. Mech., 28(2), 153-166. https://doi.org/10.12989/sem.2008.28.2.153
  15. Ghodrati Amiri, G., Ashtari, P. and Rahami, H. (2006), "New development of artificial record generation by wavelet theory", Struct. Eng. Mech., 22(2), 185-195. https://doi.org/10.12989/sem.2006.22.2.185
  16. Ghodrati Amiri, G., Bagheri, A. and Razaghi, A. (2008), "Generation of multiple earthquake accelerograms compatible with spectrum via the wavelet packet transform and stochastic neural networks", J. Earthq. Eng. (Accepted for publication).
  17. Ghodrati Amiri, G. and Asadi, A. (2009), "Processing ground motion records by using an advanced method in wavelet packet", Intl. J. Eng. Sci., Iran University of Science & Technology (Submitted for review).
  18. Hancock, J., Waston-Lamprey, J., Abrahamson, N.A., Bommer, J.J., Markatis, A., Macoy, E. and Mendis, R. (2006), "An improved method of matching response spectra of recorded earthquake ground motion using wavelets", J. Earthq. Eng., 10(special issue 1), 67-89. https://doi.org/10.1142/S1363246906002736
  19. Haykin, S. (1998), Neural Networks: A Comprehensive Foundation, 2nd Edition, Pearson Education.
  20. Iyama, J. and Kuwamura, H. (1999), "Application of wavelets to analysis and simulation of earthquake motions", Earthq. Eng. Struct. D., 28, 255-272. https://doi.org/10.1002/(SICI)1096-9845(199903)28:3<255::AID-EQE815>3.0.CO;2-C
  21. Jaffard, S., Meyer, Y. and Ryan, R.D. (2001), "Wavelets: tools for science & technology", Soc. Ind. Appl. Math., Philadelphia.
  22. Karabalis, D.L., Cokkinides, G.J. Rizos, D.C. and Mulliken, J.S. (2000), "Simulation of earthquake ground motions by a deterministic approach", Adv. Eng. Soft., 31, 329-338. https://doi.org/10.1016/S0965-9978(99)00057-5
  23. Lee, S.C. and Han, S.W. (2002), "Neural-network-based models for generating artificial earthquakes and response spectra", Comput. Struct., 80, 1627-1638. https://doi.org/10.1016/S0045-7949(02)00112-8
  24. Lin, C.J. and Ghaboussi, J. (2000), "Recent progress on neural network based methodology for generating artificial earthquake accelerograms", Proceedings of the 12th World Conference on Earthquake Engineering, Auckland, New Zealand, January-February.
  25. Mac Can, W.M. and Shah, H.C. (1979), "Determining strong-motion duration of earthquake", B. Seismol. Soc. Am., 69, 1253-1265.
  26. Mallat, S.G. (1989), "A theory of multi-resolution signal decomposition, the wavelet representation", IEEE T. Pattern Anal., 11, 674-693. https://doi.org/10.1109/34.192463
  27. MATLAB Reference Guide (2004), The Math Works Inc.
  28. Meyer, Y. (1989), Orthonormal Wavelets in Wavelets, Springer, Berlin.
  29. Mukherjee, S. and Gupta, K. (2002a), "Wavelet-based characterization of design ground motions", Earthq. Eng. Struct. D., 31, 1173-1190. https://doi.org/10.1002/eqe.155
  30. Mukherjee, S. and Gupta, K. (2002b), "Wavelet-based generation of spectrum-compatible time-histories", Earthq. Eng. Struct. D., 22, 799-804.
  31. Naeim, F. (1999), The Seismic Design Handbook, Van Nostr.
  32. Newland, D.E. (1994), Random Vibrations, Spectral and Wavelet Analysis, 3rd Edition, Longman Singapore Publishers.
  33. Ogden, R.T. (1997), Essential Wavelets for Statistical Applications and Data Analysis, Birkhauser, Boston.
  34. Rajasekaran, S., Latha, V. and Lee, S.C. (2006), "Generation of artificial earthquake motion records using wavelets and principal component analysis", J. Earthq. Eng., 10(5), 665-691.
  35. Ramezi, H. (1997), "Base accelerogram data of iranian accelerograph network", Building and Housing Research Center, BHRC-PN S 253, Tehran, Iran.
  36. Refooei, F.R., Mobarake, A. and Ahmadi, G. (2001), "Generation of artificial earthquake records with a nonstationary Kanai-Tajimi model", Eng. Struct., 23, 827-837. https://doi.org/10.1016/S0141-0296(00)00093-6
  37. Strang, G. and Nguyen, T. (1996), Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, Massachusetts
  38. Suarez, L.E. and Montejo, L.A. (2005), "Generation of artificial earthquake via the wavelet transform", Solid. Struct., 42, 5905-5919. https://doi.org/10.1016/j.ijsolstr.2005.03.025
  39. Suarez, L.E. and Montejo, L.A. (2007), "Applications of the wavelet transform in the generation and analysis of spectrum-compatible records", Struct. Eng. Mech., 27(2), 185-198.
  40. Wickerhauser, M.V. (1994), Adapted Wavelet Analysis from Theory to Software, (Ed. Peters, A.K.), Wellesley, MA.

피인용 문헌

  1. New method for the estimation of strong ground motions based on the colonial competitive algorithm vol.18, pp.5, 2014, https://doi.org/10.1007/s12205-014-0034-0
  2. SIMULATION OF EARTHQUAKE RECORDS BY MEANS OF EMPIRICAL MODE DECOMPOSITION AND HILBERT SPECTRAL ANALYSIS vol.08, pp.01, 2014, https://doi.org/10.1142/S179343111450002X
  3. Simulation of spectrum-correspondent accelerogram by using artificial neural networks vol.18, pp.3, 2016, https://doi.org/10.21595/jve.2016.16623
  4. Neural network-based generation of artificial spatially variable earthquakes ground motions vol.4, pp.5, 2013, https://doi.org/10.12989/eas.2013.4.5.509
  5. Estimation of spectral acceleration based on neural networks vol.167, pp.8, 2014, https://doi.org/10.1680/stbu.12.00059
  6. Simulation of Endurance Time Excitations via Wavelet Transform vol.43, pp.3, 2011, https://doi.org/10.1007/s40996-018-0208-y