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Neural Network Based Damage Detection of Dynamically Loaded Structures

  • Conference paper
Engineering Applications of Neural Networks (EANN 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 43))

Abstract

The aim of the paper is to describe a methodology of damage detection which is based on artificial neural networks in combination with stochastic analysis. The damage is defined as a stiffness reduction (bending or torsion) in certain part of a structure. The key stone of the method is feed-forward multilayer network. It is impossible to obtain appropriate training set for real structure in usage, therefore stochastic analysis using numerical model is carried out to get training set virtually. Due to possible time demanding nonlinear calculations the effective simulation Latin Hypercube Sampling is used here. The important part of identification process is proper selection of input information. In case of dynamically loaded structures their modal properties seem to be proper input information as those are not dependent on actual loading (traffic, wind, temperature). The methodology verification was carried out using laboratory beam.

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Author information

Authors and Affiliations

  1. Institute of Structural Mechanics, Faculty of Civil Engineering, Brno University, of Technology, Veveří 95, 602 00, Brno, Czech Republic

    David Lehký & Drahomír Novák

Authors
  1. David Lehký
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  2. Drahomír Novák
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Editor information

Editors and Affiliations

  1. Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, N7 8DB, London, UK

    Dominic Palmer-Brown

  2. School of Computing, IT and Engineering, University of East London, Docklands Campus, 4-6 University Way, E16 2RD, London, UK

    Chrisina Draganova  & Haris Mouratidis  & 

  3. School of Computing, IT and Engineering, University of East London, London, UK

    Elias Pimenidis

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© 2009 Springer-Verlag Berlin Heidelberg

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Lehký, D., Novák, D. (2009). Neural Network Based Damage Detection of Dynamically Loaded Structures. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-03969-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03968-3

  • Online ISBN: 978-3-642-03969-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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