Author
Abstract
An artificial neural network (ANN) model has been developed for the
prediction of nonlinear response for plates with built-in edges and different
sizes, thickness and uniform loads. The model is based on a six-layer neural
network with back propagation learning algorithm. The learning data were
performed using a nonlinear finite element program, the set of 1500x16
represent the deflection response of load. Incremental stages of the nonlinear
finite element analysis was generated by using 25 schemes of built-in
rectangular plates with different thickness and uniform distributed loads.
The neural network model has four input nodes representing the uniform
distributed load, thickness, length of plate and length to width ratio, four
hidden layers and sixteen output nodes representing the deflection response.
Regression analysis between finite element results and values predicted by the
neural network model shows the least error. This approach helps in the
reduction of the effort and time required determining the load-deflection
response of plate as the FE methods usually deal with only a single problem
for each run while ANN methods can solve simultaneously for a patch of
problems.
Keywords
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