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

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 damages. standard approaches, after stage, decision rule is manually defined anomalous data. However, this process could be made automatic using machine learning methods. This paper proposes anomalies. The methodology consists of: (i) Variational Autoencoder (VAE) approximate distribution and (ii) One-Class Support Vector Machine (OC-SVM) discriminate different health conditions damage-sensitive features extracted VAE’s signal reconstruction. applied scale steel structure that was tested nine damage scenarios by IASC-ASCE Task Group.