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    Structural health monitoring with self-organizing maps and artificial neural networks

    Thumbnail
    Date
    2020
    Author
    Avci O.
    Abdeljaber O.
    Kiranyaz, Mustafa Serkan
    Inman D.
    Metadata
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    Abstract
    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.
    URI
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065980544&doi=10.1007%2f978-3-030-12684-1_24&partnerID=40&md5=246842eac559bcceec6bfdb65fd6a188
    DOI/handle
    http://dx.doi.org/10.1007/978-3-030-12684-1_24
    http://hdl.handle.net/10576/30616
    Collections
    • Civil and Environmental Engineering [‎892‎ items ]
    • Electrical Engineering [‎2850‎ items ]

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