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AuthorAvci O.
AuthorAbdeljaber O.
AuthorKiranyaz, Mustafa Serkan
AuthorInman D.
Available date2022-04-26T12:31:21Z
Publication Date2020
Publication NameConference Proceedings of the Society for Experimental Mechanics Series
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-030-12684-1_24
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85065980544&doi=10.1007%2f978-3-030-12684-1_24&partnerID=40&md5=246842eac559bcceec6bfdb65fd6a188
URIhttp://hdl.handle.net/10576/30616
AbstractThe 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.
Languageen
PublisherSpringer New York LLC
SubjectConformal mapping
Damage detection
Modal analysis
Neural networks
Pattern recognition
Personnel training
Structural analysis
Structural dynamics
Structural health monitoring
Ambient vibrations
Damage localization
Damage scenarios
Grid structures
Multilayer feedforward
Non-parametric
Stiffness reduction
Structural damage detection
Self organizing maps
TitleStructural health monitoring with self-organizing maps and artificial neural networks
TypeConference Paper
Pagination237-246
dc.accessType Abstract Only


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