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Adaptive Detection of Structural Changes Based on Unsupervised Learning and Moving Time-Windows

Abstract : The present paper addresses data-driven structural health monitoring to propose a real time strategy for adaptive structural assessment. The adaptive character is achieved using unsupervised discrimination machine-learning methods, widely known as clustering algorithms. Real-time capability is based on the definition of symbolic data, which allow describing large amounts of information without loss of related information. The efficiency of the proposed methodology is illustrated using an experimental case study in which structural changes were imposed to a suspended bridge during an extensive rehabilitation program.
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https://hal.inria.fr/hal-01021195
Contributor : Anne Jaigu <>
Submitted on : Wednesday, July 9, 2014 - 10:17:47 AM
Last modification on : Tuesday, December 8, 2020 - 10:20:41 AM
Long-term archiving on: : Thursday, October 9, 2014 - 11:19:58 AM

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  • HAL Id : hal-01021195, version 1

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João Santos, Luís Calado, André Orcesi, Christian Crémona. Adaptive Detection of Structural Changes Based on Unsupervised Learning and Moving Time-Windows. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021195⟩

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