Research Article Open Access

Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks

Reza Kamyab Moghadas and Kok Keong Choong

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.

American Journal of Applied Sciences
Volume 5 No. 11, 2008, 1429-1432

DOI: https://doi.org/10.3844/ajassp.2008.1429.1432

Submitted On: 17 January 2008 Published On: 30 November 2008

How to Cite: Moghadas, R. K. & Choong, K. K. (2008). Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks. American Journal of Applied Sciences, 5(11), 1429-1432. https://doi.org/10.3844/ajassp.2008.1429.1432

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Keywords

  • Backpropagation
  • radial basis function
  • neural networks
  • maximum deflection
  • double layer grids