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The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage states generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and...
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