Machine learning models in structural engineering research and a secured framework for structural health monitoring
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15853-5.pdf
Reference127 articles.
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2. Abdeljaber O, Avci O, Kiranyaz MS, Boashash B, Sodano H, Inman DJ (2018) 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomput 275:1308–1317. https://doi.org/10.1016/j.neucom.2017.09.069
3. Abu-Mahfouz I, Banerjee A (2017) Crack detection and identification using vibration signals and fuzzy clustering. Proced Comput Sci 114:266–274. https://doi.org/10.1016/j.procs.2017.09.038
4. Alamdari MM, Rakotoarivelo T, Khoa NLD (2017) A spectral-based clustering for structural health monitoring of the Sydney harbour bridge. Mech Syst Signal Process 87(May 2016):384–400. https://doi.org/10.1016/j.ymssp.2016.10.033
5. Altunişik, AC (2018) Determination of natural frequencies of arch dams based on reservoir levels using ambient and forced vibration tests. Civil Eng Res J, 3(5). https://doi.org/10.19080/cerj.2018.03.555623
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