Structural Engineering and Mechanics

  • 제37권6호
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  • Pages.575-592
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  • 2011
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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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
⟨ 이전 논문 다음 논문 ⟩

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

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  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