Abstract:
The paper presents Artificial Neural Networks developed for typical steel railway bridges
for the purpose of damage detection. Multilayer perceptrons have been used for generating
the architecture for the bridges of different configurations. The back propagation
algorithm has been adopted for training the network with simulated damage states. The
training pairs have been generated using a standard finite element program. The weights
of the trained networks have been stored and can be used as a knowledge source
independently. It is demonstrated that the trained networks have practical relevance.