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A Numerical Simulation Study of Structural Damage Based on RBF Neural Network

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Book cover Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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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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Author information

Authors and Affiliations

  1. Civil Engineering Institute, Dalian Fisheries University, Postfach 116021, Dalian, China

    Xu-dong Yuan, Cao Gao & Shao-xia Gao

  2. ZheJiang Provincial Transportation Engineering Construction Group, Postfach 310030, Hangzhou, China

    Hou-bin Fan

Authors
  1. Xu-dong Yuan
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  2. Hou-bin Fan
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  3. Cao Gao
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  4. Shao-xia Gao
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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