An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring

Structural health monitoring (SHM) is critical for ensuring the safety and longevity of structures, yet existing methodologies often face challenges such as high data dimensionality, lack of interpretability, and reliance on extensive label datasets. Current research in SHM has primarily focused on...

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Bibliographic Details
Main Authors:	Tales Boratto, Heder Soares Bernardino, Alex Borges Vieira, Tiago Silveira Gontijo, Matteo Bodini, Dmitriy A. Martyushev, Camila Martins Saporetti, Alexandre Cury, Flávio Barbosa, Leonardo Goliatt
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Infrastructures
Subjects:	unsupervised learning anomaly detection structural health monitoring vibration feature selection
Online Access:	https://www.mdpi.com/2412-3811/10/2/32
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An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring

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