Structural Engineering and Mechanics

  • 제28권2호
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  • Pages.153-166
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  • 2008
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms

  • Amiri, G. Ghodrati (Center of Excellence for Fundamental Studies in Structural Engineering, College of Civil Engineering,Iran University of Science & Technology) ;
  • Bagheri, A. (Center of Excellence for Fundamental Studies in Structural Engineering, College of Civil Engineering,Iran University of Science & Technology)
  • 투고 : 2006.08.16
  • 심사 : 2007.10.02
  • 발행 : 2008.01.30
⟨ 이전 논문 다음 논문 ⟩

초록

This paper suggests the use of wavelet multiresolution analysis (WMRA) and neural network for generation of artificial earthquake accelerograms from target spectrum. This procedure uses the learning capabilities of radial basis function (RBF) neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, WMRA is used to decompose earthquake accelerograms to several levels that each level covers a special range of frequencies, and then for every level a RBF neural network is trained to learn to relate the response spectrum to wavelet coefficients. Finally the generated accelerogram using inverse discrete wavelet transform is obtained. An example is presented to demonstrate the effectiveness of the method.

키워드

참고문헌

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  3. 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
  4. Seismic Design Value Evaluation Based on Checking Records and Site Geological Conditions Using Artificial Neural Networks vol.2013, 2013, https://doi.org/10.1155/2013/242941
  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 spectrum-correspondent accelerogram by using artificial neural networks vol.18, pp.3, 2016, https://doi.org/10.21595/jve.2016.16623
  7. 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
  8. Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey vol.2008, 2008, https://doi.org/10.1155/2008/919420
  9. 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
  10. Simulation of earthquake records using combination of wavelet analysis and non-stationary Kanai-Tajimi model vol.33, pp.2, 2008, https://doi.org/10.12989/sem.2009.33.2.179
  11. Application of neural networks and an adapted wavelet packet for generating artificial ground motion vol.37, pp.6, 2008, https://doi.org/10.12989/sem.2011.37.6.575
  12. Simulation of Endurance Time Excitations via Wavelet Transform vol.43, pp.3, 2008, https://doi.org/10.1007/s40996-018-0208-y
  13. Generation of critical aftershocks using stochastic neural networks and wavelet packet transform vol.26, pp.5, 2020, https://doi.org/10.1177/1077546319879536