6th International Young Scientist Congress (IYSC-2021) and workshop on Intellectual Property Rights on 8th and 9th May 2021.  10th International Science Congress (ISC-2020) will be Postponed to 8th and 9th December 2021 Due to COVID-19.  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Application of artificial Intelligence in Generating Artificial Accelerograms using Kanai-Tajimi model

Author Affiliations

  • 1Civil Engineering Dept.,
  • 2 K. N. Toosi university of technology, Tehran, IRAN 2K.N. Toosi University of Technology, Tehran, IRAN

Res. J. Recent Sci., Volume 4, Issue (2), Pages 120-129, February,2 (2015)


Several civil engineering activities need dynamic time history analysis or other numerical simulations. In such cases, it is very important to have accurate and adequate accelerograms. However in most situations, there is not enough data for a specific site or region. Many powerful methods are developed in order to generate artificial earthquake records. This paper is aimed at combining non-stationary Kanai-Tajimi model and artificial neural networks for generation artificial earthquake records. More precisely, two radial basis neural networks (RBF) are applied to conjecture filter parameters from response spectrum. Moreover, in non-stationary Kanai-Tajimi method one needs to guess proper pattern for filter parameters, based on human intelligence or experience. These patterns vary from one accelerogram to other because of record characteristics. General regression neural network (GRNN) are used in order to find better approximation of filter parameters without use of human judgment, applied to original non-stationary Kanai-Tajimi method. A new method in selecting proper Moving-Time-Window size, used in non-stationary Kanai-Tajimi method is presented in this paper. Finally, RBFs are trained and used in artificial accelerogram generation for a given velocity response spectrum. Three earthquake records, including Bam 2003, Gheshm 2005 and Zanjiran 1994, occurred in Iran are used to verify proposed method. At the end, the performance of proposed method is investigated statistically. Statistical results indicate the accuracy of the proposed method.


  1. Jennings P.C, Housner G.W and Tsai N.C, Simulated Earthquake motions, Earthquake engineering research Laboratory, (1968)
  2. Housner G.W. and Jennings P.C, Generation of artificial earthquakes, Journal of the Engineering Mechanics Division, Proceedings of ASCE, 90 (EM1), 113–150 (1964)
  3. Kanai K., Semi-empirical formula for the seismic characteristics of the ground, Earthquake Research Inst., Univ. Tokyo Bull., 35, 309-325 (1957)
  4. Tajimi H., A statistical method of determining the maximum response of a building structure during an earthquake, In : Proc. 2nd WCEE, Vol.II, Tokyo: Science Council of Japan, 781-798 (1960)
  5. Ahmadi G. and Fan F.G., Nonstationary Kanai-Tajimi model for El Centro 1940 and Mexico City 1985 earthquake, Probabilistic Engineering Mechanics, 5, 171-181 (1990)
  6. Rofooei F.R., Mobarake A.A. and Ahmadi G., Generation of Artificial Earthquake Records with a Nonstationary Kanai-Tajimi Model, Engineering Structures, 23, 827-837 (2001)
  7. Ghodrati Amiri G., Bagheri A. and Fadavi Amiri M., New method for generation of artificial ground motion by a nonstationary Kanai-Tajimi model and wavelet transform, Structural Engineering and Mechanics, 26(6), 709-723 (2007)
  8. Ghodrati Amiri G. and Bagheri A., Simulation of earthquake records using combination of wavelet analysis and nonstationary Kanai-Tajimi model, Structural Engineering and Mechanics, 33(2), 179-191 (2009)
  9. Ghaboussi J. and Lin C.J., New method of generating spectrum compatible accelerograms using neural networks, Earthquake Engineering Structural Dynamics, 27, 377-396 (1998)
  10. Lin C.J. and Ghaboussi J., Generating multiple spectrum compatible accelerograms using stochastic neural networks, Earthquake Engineering Structural Dynamics, 30, 1021-1042 (2001)
  11. Lee S.C. and Han S.W, Neural-network-based models for generating artificial earthquakes and response spectra, Computers and Structures, 80, 1627–1638 (2002)
  12. Bargi K.H, Rahami H. and Loux C, Generation of artificial accelerograms using neural networks for data of IRAN, Journal of faculty of engineering (University of Tehran), 36(2), 177-183 (2002)
  13. Ghodrati Amiri G. and Bagheri A., Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms, Structural Engineering and Mechanics, 28(2), 153-166 (2008)
  14. Ghodrati Amiri G., Bagheri A. and Seyed Razaghi, S.A., Generation of Multiple Earthquake Accelerograms Compatible with Spectrum via the Wavelet Packet Transform and Stochastic Neural Networks, Journal of Earthquake Engineering, 13(7), 899-915 (2009)
  15. Robert J. Marks ll, Introduction to Shannon sampling and interpolation theory, Springer-Verlag New York, (1991)
  16. Menhaj M., Calculation Intelligence: Neural Network, Amirkabir University, Iran, (1998)
  17. Picton P., Neural network, second edition, Grassroots Series, (2000)
  18. D.F.A. Specht, A general regression neural network, IEEE Transactions on Neural Networks, 2, 568-576 (1991)
  19. Building and Housing Research Center (BHRC), Available from URL: http://www.bhrc.ac.ir/., (2014)
  20. Naeim F., The Seismic Design Handbook, Van Nostrand, (1999)
  21. Chopra A.K., Dynamics of Structures, Englewood Cliffs, NJ: Prentice-Hall (1995)
  22. Clough R.W. and Penzien J., Dynamics of Structures, Second Edition, McGraw-Hill, (1993)
  23. Reza Khodaie Mahmoodi, Sedigheh Sarabi Nejad and Mehdi Ershadi sis , Expert Systems and Artificial Intelligence Capabilities Empower Strategic Decisions : A Case study, Res. J. Recent Sci., 3(1), 116-121 (2014)
  24. Blesslin Sheeba T. and Rangarajan P., SOC Implementation of Hybrid Cryptography Techniques using Hight and RC4 Algorithm, Res. J. Recent Sci., 3(5), 65-70 (2014)
  25. Jawad Ali Shah, I.M. Qureshi, Amir A. Khaliq and Hammad Omer, Sparse Signal Recovery based on Hybrid Genetic Algorithm, Res. J. Recent Sci., 3(5), 86-93 (2014)
  26. Hamid Reza Samadi, Asghar Teymoorian and Mostafa GhasemiLandslide analysis to estimate probability occurrence of earthquakes by software ArcGIS in central of Iran, Res. J. Recent Sci., 3(5), 104-109 (2014)
  27. Faisal Nadeem Saher, Nasly M.A., Tuty Asmawaty Binti Abdul Kadir, Nasehir Khan E.M. Yahaya, Wan Mohd Faizal Wan Ishak Harnessing Floodwater of Hill Torrents for Improved Spate Irrigation System Using Geo-Informatics Approach, Res. J. Recent Sci., 3(1), 14-22 (2014)