An unsupervised machine learning approach for real-time damage detection in bridges

Author:

Bayane Imane,Leander John,Karoumi Raid

Publisher

Elsevier BV

Reference40 articles.

1. “A more true picture of the actual behaviour of the bridge structure | FP6,” CORDIS | European Commission. Accessed: Jun. 07, 2023. [Online]. Available: https://cordis.europa.eu/project/id/1653/reporting.

2. I. Bayane, “Fatigue examination of reinforced-concrete bridge slabs using acoustic emission and strain monitoring data,” EPFL, Lausanne, 2021. doi: 10.5075/epfl-thesis-8587.

3. I. Bayane, “Quantification of the conditional value of SHM data for the fatigue safety evaluation of a road viaduct,” South Korea, 2019.

4. Structural health monitoring: a machine learning perspective;Farrar,2012

5. Application of time series analysis for bridge monitoring;Omenzetter;Smart Mater Struct,2006

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