Research Article Open Access

Dynamic Analysis of Structures Using Neural Networks

N. Ahmadi, R. Kamyab Moghadas and A. Lavaei


In the recent years, neural networks are considered as the best candidate for fast approximation with arbitrary accuracy in the time consuming problems. Dynamic analysis of structures against earthquake has the time consuming process. We employed two kinds of neural networks: Generalized Regression neural network (GR) and Back-Propagation Wavenet neural network (BPW), for approximating of dynamic time history response of frame structures. GR is a traditional radial basis function neural network while BPW categorized as a wavelet neural network. In BPW, sigmoid activation functions of hidden layer neurons are substituted with wavelets and weights training are achieved using Scaled Conjugate Gradient (SCG) algorithm. Comparison the results of BPW with those of GR in the dynamic analysis of eight story steel frame indicates that accuracy of the properly trained BPW was better than that of GR and therefore, BPW can be efficiently used for approximate dynamic analysis of structures.

American Journal of Applied Sciences
Volume 5 No. 9, 2008, 1251-1256


Submitted On: 25 October 2007 Published On: 30 September 2008

How to Cite: Ahmadi, N., Moghadas, R. K. & Lavaei, A. (2008). Dynamic Analysis of Structures Using Neural Networks. American Journal of Applied Sciences, 5(9), 1251-1256.

  • 11 Citations



  • Earthquake
  • wavelet
  • wavenet
  • generalized regression