Author/Authors :
S. Gholizadeh، S. Gholizadeh نويسنده دانشكده فني و مهندسي دانشگاه Urmia S. Gholizadeh, S. Gholizadeh , M.R. Sheidaii، M.R. Sheidaii نويسنده Department of Civil Engineering, Urmia University, Urmia, Iran M.R. Sheidaii, M.R. Sheidaii , S. Farajzadeh، S. Farajzadeh نويسنده Department of Civil Engineering, Urmia University, Urmia, Iran S. Farajzadeh, S. Farajzadeh
Abstract :
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-onsquare
double layer grids with the variable length of span and height are considered. Backpropagation
(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.
