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

arXiv: https://arxiv.org/abs/2210.05674 · HTML: https://mlsanity.com/paper/2210.05674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 91 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders](https://mlsanity.com/paper/2202.00570/) (2022)

## Built on by (most cited first, 2 of 2)

- [SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder](https://mlsanity.com/paper/2406.17537/) (2024)
- [Instance-Adaptive Parametrization for Amortized Variational Inference](https://mlsanity.com/paper/2604.06796/) (2026)

## Abstract (arXiv)

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.