Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Use of a Roving Vision Sensor Setup to Train an Autoencoder for Damage Detection of Bridge Structures
This paper will demonstrate a solution for detecting damage to a bridge structure from measured displacements gathered using a roving vision sensor based approach. The measurement of displacement was accomplished using a synchronised multi-camera vision-based displacement measurement system. Displacement measurements can provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Computer Vision systems have been validated as a means of displacement calculation, the research developed here is intended to form the basis of a real time damage detection system. This is done through the use of unsupervised deep learning methods for anomaly detection which could form the basis of a low cost durable alternative. The performance of the system was evaluated in a series of controlled laboratory tests. This research provides a means of detecting changes to a bridge structure through use of minimal sensor installation, reducing potential sources of error and allowing for potential live rating of bridge structures.
Use of a Roving Vision Sensor Setup to Train an Autoencoder for Damage Detection of Bridge Structures
This paper will demonstrate a solution for detecting damage to a bridge structure from measured displacements gathered using a roving vision sensor based approach. The measurement of displacement was accomplished using a synchronised multi-camera vision-based displacement measurement system. Displacement measurements can provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Computer Vision systems have been validated as a means of displacement calculation, the research developed here is intended to form the basis of a real time damage detection system. This is done through the use of unsupervised deep learning methods for anomaly detection which could form the basis of a low cost durable alternative. The performance of the system was evaluated in a series of controlled laboratory tests. This research provides a means of detecting changes to a bridge structure through use of minimal sensor installation, reducing potential sources of error and allowing for potential live rating of bridge structures.
Use of a Roving Vision Sensor Setup to Train an Autoencoder for Damage Detection of Bridge Structures
Lecture Notes in Civil Engineering
Rainieri, Carlo (Herausgeber:in) / Fabbrocino, Giovanni (Herausgeber:in) / Caterino, Nicola (Herausgeber:in) / Ceroni, Francesca (Herausgeber:in) / Notarangelo, Matilde A. (Herausgeber:in) / Lydon, Darragh (Autor:in) / Lydon, Myra (Autor:in) / Early, Juliana (Autor:in) / Taylor, Su (Autor:in)
International Workshop on Civil Structural Health Monitoring ; 2021 ; Naples, Italy
25.08.2021
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
| Springer Verlag | 2024
| Online Contents | 2013