Model-free damage detection of a laboratory bridge using artificial neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Safety, Risk, Reliability and Quality,Civil and Structural Engineering
Link
http://link.springer.com/content/pdf/10.1007/s13349-019-00375-2.pdf
Reference33 articles.
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2. Bell B (2004) European railway bridge demography—deliverable D 1.2. Technical report, Sustainable Bridges Consortium
3. Wenzel H (2009) Health monitoring of bridges. Wiley, Vienna
4. Gonzalez I, Karoumi R (2015) BWIM aided damage detection in bridges using machine learning. J Civ Struct Health Monit 5(5):715–725. https://doi.org/10.1007/s13349-015-0137-4
5. Neves AC, González I, Leander J, Karoumi R (2017) Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Health Monit 7(5):689–702. https://doi.org/10.1007/s13349-017-0252-5
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