TY  - JOUR
AU  - Moghadas, Reza Kamyab 
AU  - Choong, Kok Keong 
PY  - 2008
TI  - Prediction of Double Layer Grids' Maximum Deflection Using Neural Networks
JF  - American Journal of Applied Sciences
VL  - 5
IS  - 11
DO  - 10.3844/ajassp.2008.1429.1432
UR  - https://thescipub.com/abstract/ajassp.2008.1429.1432
AB  - 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.