Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

New development of artificial record generation by wavelet theory

  • Amiri, G. Ghodrati (Center of Excellence for Fundamental Studies in Structural Engineering, Department of Civil Engineering, Iran University of Science & Technology) ;
  • Ashtari, P. (Center of Excellence for Fundamental Studies in Structural Engineering, Department of Civil Engineering, Iran University of Science & Technology) ;
  • Rahami, H. (Center of Excellence for Fundamental Studies in Structural Engineering, Department of Civil Engineering, Iran University of Science & Technology)
  • Received : 2005.01.05
  • Accepted : 2005.10.18
  • Published : 2006.01.30
⟨ Previous Next ⟩

Abstract

Nowadays it is very necessary to generate artificial accelerograms because of lack of adequate earthquake records and vast usage of time-history dynamic analysis to calculate responses of structures. According to the lack of natural records, the best choice is to use proper artificial earthquake records for the specified design zone. These records should be generated in a way that would contain seismic properties of a vast area and therefore could be applied as design records. The main objective of this paper is to present a new method based on wavelet theory to generate more artificial earthquake records, which are compatible with target spectrum. Wavelets are able to decompose time series to several levels that each level covers a specific range of frequencies. If an accelerogram is transformed by Fourier transform to frequency domain, then wavelets are considered as a transform in time-scale domain which frequency has been changed to scale in the recent domain. Since wavelet theory separates each signal, it is able to generate so many artificial records having the same target spectrum.

Keywords

References

  1. Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, CA
  2. Ghaboussi, J. and Lin., C.J. (1998), 'New method of generating spectrum compatible accelerograms using neural networks', Earthq. Eng. Struct. Dyn., 27, 377-396 https://doi.org/10.1002/(SICI)1096-9845(199804)27:4<377::AID-EQE735>3.0.CO;2-2
  3. Iranian Code of Practice for Seismic Resistance Design of Buildings, Standard No.2800, 2nd Edition (1999), Building and Housing Research Center, Tehran, Iran
  4. Iyama, J. and Kuwamura, H. (1999), 'Application of wavelets to analysis and simulation of earthquake motions', Earthq. Eng. Struct. Dyn., 28, 255-272 https://doi.org/10.1002/(SICI)1096-9845(199903)28:3<255::AID-EQE815>3.0.CO;2-C
  5. Lin, C.J. and Ghaboussi, J. (2000), 'Recent progress on neural network based methodology for generating artificial earthquake accelerograms', Proc. of the 12th World Conf. on Earthquake Engineering, Auckland, New Zealand, 29 Jan.-5 Feb
  6. MacCann, W.M. and Shah, H.C. (1979), 'Determining strong-motion duration of earthquake', Bulletin of the Seismological Society of America, 69, 1253-1265
  7. MATLAB Reference Guide (1999), The Math Works Inc
  8. Naeim, F. (1999), The Seismic Design Handbook, Van Nostrand
  9. Newland, D.E. (1994), Random Vibrations. Spectral and Wavelet Analysis, 3rd Edition, Longman Singapore Publishers
  10. Ramezi, R (1997), Base Accelerogram Data ofIranian Accelerograph Network, Building and Housing Research Center, BHRC-PN S 253, Tehran, Iran
  11. Suzuki, S., Hada, K. and Asano, K. (1998), 'Simulation of strong ground motions based on recorded accelerograms and the stochastic method', Soil Dyn. Earthq. Eng., 17, 551-556 https://doi.org/10.1016/S0267-7261(98)00015-3
  12. Williams, J.R. and Amaratunga, K. (1994), 'Introduction to wavelet in engineering', Int. J. Numer. Meth. Eng., 17, 2365-2388

Cited by

  1. Wavelet-Based Method for Generating Nonstationary Artificial Pulse-Like Near-Fault Ground Motions vol.29, pp.10, 2014, https://doi.org/10.1111/mice.12110
  2. Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform vol.15, pp.1, 2011, https://doi.org/10.1080/13632469.2010.517281
  3. Generation of Near-Field Artificial Ground Motions Compatible with Median-Predicted Spectra Using PSO-Based Neural Network and Wavelet Analysis vol.27, pp.9, 2012, https://doi.org/10.1111/j.1467-8667.2012.00783.x
  4. Combinatorial continuous non-stationary critical excitation in M.D.O.F structures using multi-peak envelope functions vol.7, pp.6, 2014, https://doi.org/10.12989/eas.2014.7.6.895
  5. GENERATION OF UNIFORM HAZARD EARTHQUAKE ACCELEROGRAMS AND NEAR-FIELD GROUND MOTIONS vol.06, pp.02, 2012, https://doi.org/10.1142/S1793431112500133
  6. Generation of Multiple Earthquake Accelerograms Compatible with Spectrum Via the Wavelet Packet Transform and Stochastic Neural Networks vol.13, pp.7, 2009, https://doi.org/10.1080/13632460802687728
  7. New method for generation of artificial ground motion by a nonstationary Kanai-Tajimi model and wavelet transform vol.26, pp.6, 2007, https://doi.org/10.12989/sem.2007.26.6.709
  8. Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms vol.28, pp.2, 2006, https://doi.org/10.12989/sem.2008.28.2.153
  9. Simulation of earthquake records using combination of wavelet analysis and non-stationary Kanai-Tajimi model vol.33, pp.2, 2006, https://doi.org/10.12989/sem.2009.33.2.179
  10. Application of neural networks and an adapted wavelet packet for generating artificial ground motion vol.37, pp.6, 2006, https://doi.org/10.12989/sem.2011.37.6.575
  11. Seismic design of structures using a modified non-stationary critical excitation vol.4, pp.4, 2013, https://doi.org/10.12989/eas.2013.4.4.383
  12. Simulation of Endurance Time Excitations via Wavelet Transform vol.43, pp.3, 2006, https://doi.org/10.1007/s40996-018-0208-y