Abstract
Frequency response function (FRF) data are sensitive indicators of structural physical integrity and thus can be used to detect damage. This paper deals with structural damage detection by using back propagation neural networks (BPNN). Features extracted from FRFs by applying Independent component analysis (ICA) are used as input data to NN. The Latin hypercube sampling (LHS) is adapted for efficient generation of the patterns for training the neural network. Substructural identification technique is also used to cope with complex structure. A truss is presented to demonstrate the effectiveness of the present method with good accuracy, even if the measurements are incomplete and full of noise.
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© 2004 Springer-Verlag Berlin Heidelberg
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Qu, F., Zou, D., Wang, X. (2004). Substructural Damage Detection Using Neural Networks and ICA. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_123
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DOI: https://doi.org/10.1007/978-3-540-28647-9_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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