| Title |
Damage Localization of Bridges with Variational Autoencoder |
| Authors |
이강혁(Lee, Kanghyeok) ; 정민웅(Chung, Minwoong) ; 전찬웅(Jeon, Chanwoong) ; 신도형(Shin, Do Hyoung) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.2.0233 |
| Keywords |
교량 손상 위치 추정;딥러닝;비지도학습 Bridge damage localization;Variational Autoencoder (VAE);Deep learning;Unsupervised learning |
| Abstract |
Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization. |