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

  • Xu-dong Yuan
  • Hou-bin Fan
  • Cao Gao
  • Shao-xia Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


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.


Damage Location Quantification Result Damage Identification Hide Layer Node Identification Capability 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xu-dong Yuan
    • 1
  • Hou-bin Fan
    • 2
  • Cao Gao
    • 1
  • Shao-xia Gao
    • 1
  1. 1.Civil Engineering InstituteDalian Fisheries UniversityDalianChina
  2. 2.ZheJiang Provincial Transportation Engineering Construction GroupHangzhouChina

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