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Structural Engineering and Mechanics
  Volume 28, Number 2, January30 2008, pages 153-166
DOI: http://dx.doi.org/10.12989/sem.2008.28.2.153
 


Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms
G. Ghodrati Amiri and A. Bagheri

 
Abstract
    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.
 
Key Words
    artificial accelerogram; wavelet transform; RBF neural network; target spectrum.
 
Address
G. Ghodrati Amiri and A. Bagheri: Center of Excellence for Fundamental Studies in Structural Engineering, College of Civil Engineering, Iran University of Science & Technology, PO Box 16765-163, Narmak, Tehran 16846, Iran
 

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