A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 (editor) / Fabbrocino, Giovanni (editor) / Caterino, Nicola (editor) / Ceroni, Francesca (editor) / Notarangelo, Matilde A. (editor) / Lydon, Darragh (author) / Lydon, Myra (author) / Early, Juliana (author) / Taylor, Su (author)
International Workshop on Civil Structural Health Monitoring ; 2021 ; Naples, Italy
2021-08-25
6 pages
Article/Chapter (Book)
Electronic Resource
English
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
| Springer Verlag | 2024
| Online Contents | 2013