Abstract
It’s natural and direct to identify the structural stiffness based on the measurement of static displacement; In addition, considering that the lower frequencies of structures can be tested with high precision and can reflect the global dynamic properties of structures, static displacements at partial nodes and several low frequencies were used to constitute the input parameter vectors for neural networks. A damage numerical verification on an arch bridge model was carried out using a radical basis function (RBF) network. Identification results indicate that the neural network has an excellence capability to identify the location and extent of structural damage with the limited noises and incomplete measured data.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yuan, Xd., Fan, Hb., Gao, C., Gao, Sx. (2006). A Numerical Simulation Study of Structural Damage Based on RBF Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_37
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DOI: https://doi.org/10.1007/11893295_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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