Structural health monitoring with self-organizing maps and artificial neural networks
| Author | Avci O. |
| Author | Abdeljaber O. |
| Author | Kiranyaz, Mustafa Serkan |
| Author | Inman D. |
| Available date | 2022-04-26T12:31:21Z |
| Publication Date | 2020 |
| Publication Name | Conference Proceedings of the Society for Experimental Mechanics Series |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1007/978-3-030-12684-1_24 |
| 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. |
| Language | en |
| Publisher | Springer New York LLC |
| Subject | Conformal 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 |
| Type | Conference Paper |
| Pagination | 237-246 |
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Civil & Architectural Engineering [713 items ]
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Electrical Engineering [2544 items ]
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