American Journal of Applied Sciences

Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks

Reza Kamyab Moghadas and Kok Keong Choong

DOI : 10.3844/ajassp.2008.1429.1432

American Journal of Applied Sciences

Volume 5, Issue 11

Pages 1429-1432

Abstract

Efficient neural networks models are trained to predict the maximum deflection of two-way on two-way grids with variable geometrical parameters (span and height) as well as cross-sectional areas of the element groups. Backpropagation (BP) and Radial Basis Function (RBF) neural networks are employed for the mentioned purpose. The inputs of the neural networks are the length of the spans, L, the height, h and cross-sectional areas of the all groups, A and the outputs are maximum deflections of the corresponding double layer grids, respectively. The numerical results indicate that the RBF neural network is better than BP in terms of training time and performance generality.

Copyright

© 2008 Reza Kamyab Moghadas and Kok Keong Choong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.