Volume 2, Issue 1 (3-2012)
IJOCE 2012, 2(1): 29-45 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Gholizadeh S, Sheidaii M, Farajzadeh S. SEISMIC DESIGN OF DOUBLE LAYER GRIDS BY NEURAL NETWORKS. IJOCE 2012; 2 (1) :29-45
URL: http://ijoce.iust.ac.ir/article-1-77-en.html
URL: http://ijoce.iust.ac.ir/article-1-77-en.html
Abstract: (26776 Views)
The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the variable length of span and height are considered. Back-propagation (BP), radial basis function (RBF) and generalized regression (GR) neural networks are trained for efficiently prediction of the seismic design of the structures. The numerical results demonstrate the superiority of the GR over the BP and RBF neural networks.
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
