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Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0003-0530-9552
2017 (English)In: Structural Health Monitoring & Damage Detection, Springer, 2017, Vol. 7Conference paper, Published paper (Other academic)
Abstract [en]

Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the application of the classical methods in real-time structural health monitoring applications. Furthermore, in many cases, the hand-crafted features extracted by the classical methods fail to accurately characterize the acquired signal, resulting in poor classification performance. In an attempt to overcome these issues, this paper presents a novel, fast and accurate structural damage detection and localization system utilizing one dimensional convolutional neural networks (CNNs) arguably for the first time in SHM applications. The proposed method is capable of extracting optimal damage-sensitive features automatically from the raw acceleration signals, allowing it to be used for real-time damage detection. This paper presents the preliminary experiments conducted to verify the proposed CNN-based approach.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 7
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-89746OAI: oai:DiVA.org:lnu-89746DiVA, id: diva2:1362666
Conference
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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CiteExportLink to record
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  • apa
  • harvard1
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