  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 the ANN training on data acquired from an undamaged
structural condition to detect structural damages. In standard approaches,
after the training stage, a decision rule is manually defined to detect
anomalous data. However, this process could be made automatic using machine
learning methods, whom performances are maximised using hyperparameter
optimization techniques. The paper proposes a semi-supervised method with a
data-driven approach to detect structural anomalies. The methodology consists
of: (i) a Variational Autoencoder (VAE) to approximate undamaged data
distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to
discriminate different health conditions using damage sensitive features
extracted from VAE's signal reconstruction. The method is applied to a scale
steel structure that was tested in nine damage's scenarios by IASC-ASCE
Structural Health Monitoring Task Group.
