Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification

Researchers propose a self-supervised learning framework using disentangled representations to identify structural damage from vibration signals while filtering out environmental noise. The approach uses an autoencoder with VICReg regularization to separate damage-induced changes from operational variability, addressing a key challenge in structural health monitoring.

MentionsVICReg · Autoencoder

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