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Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0003-0530-9552
2020 (English)In: Topics in Modal Analysis & Testing, 2020, Vol. 8Conference paper, Published paper (Other academic)
Abstract [en]

The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting damage indices from the ambient vibration response of a structure. The algorithm is based on self-organizing maps with a multilayer feedforward pattern recognition neural network. After the training of the self-organizing maps, the algorithm was tested analytically under various damage scenarios based on stiffness reduction of beam members and boundary condition changes of a grid structure. The results indicated that proposed algorithm can successfully locate and quantify damage on the structure.

Place, publisher, year, edition, pages
2020. Vol. 8
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-89754OAI: oai:DiVA.org:lnu-89754DiVA, id: diva2:1362692
Conference
The 37th IMAC, A Conference and Exposition on Structural Dynamics
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21

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Abdeljaber, Osama
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Total: 36 hits
CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf