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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.


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