Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

Most damage-assessment strategies for dynamic systems only distinguish between undamaged and damaged conditions without recognizing the level or type of damage or considering unseen conditions. This paper proposes a novel framework for structural health monitoring (SHM) that combines supervised and...

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Bibliographic Details
Main Authors: Lorenzo Stagi, Lorenzo Sclafani, Eleonora M. Tronci, Raimondo Betti, Silvia Milana, Antonio Culla, Nicola Roveri, Antonio Carcaterra
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Infrastructures
Subjects:
structural health monitoring
damage detection
cepstral coefficients
probabilistic linear discriminant analysis
Z24 bridge
Online Access:https://www.mdpi.com/2412-3811/9/3/40

Internet

https://www.mdpi.com/2412-3811/9/3/40

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