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Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Qatar University, Qatar.ORCID iD: 0000-0003-0530-9552
Qatar University, Qatar.
Qatar University, Qatar.
Tampere University of Technology, Finland.
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2017 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 388, p. 154-170Article in journal (Refereed) Published
Abstract [en]

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 388, p. 154-170
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
URN: urn:nbn:se:lnu:diva-88128DOI: 10.1016/j.jsv.2016.10.043OAI: oai:DiVA.org:lnu-88128DiVA, id: diva2:1344147
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved

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Abdeljaber, Osama

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CiteExportLink to record
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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
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf