most citedSemi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

53 citations · 53 across the 1 of their papers we have counts for

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1 paper

cs.LG2022★ 53 cited

Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

Andrea Pollastro, Giusiana Testa, Antonio Bilotta +1

In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows t…