Volume 2, Issue 1 (3-2012)                   2012, 2(1): 29-45 | Back to browse issues page

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Gholizadeh S, Sheidaii M, Farajzadeh S. SEISMIC DESIGN OF DOUBLE LAYER GRIDS BY NEURAL NETWORKS. International Journal of Optimization in Civil Engineering. 2012; 2 (1) :29-45
URL: http://ijoce.iust.ac.ir/article-1-77-en.html
Abstract:   (5727 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.
Full-Text [DOC 1671 kb]   (1675 Downloads)    
Type of Study: Research | Subject: Optimal design
Received: 2012/05/27

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