TY - GEN
T1 - Use of a Roving Vision Sensor Setup to Train an Autoencoder for Damage Detection of Bridge Structures
AU - Lydon, Darragh
AU - Lydon, Myra
AU - Early, Juliana
AU - Taylor, Su
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Anomaly Detection
KW - Computer Vision
KW - Deep Learning
KW - Structural Health Monitoring
UR - https://www.scopus.com/pages/publications/85115079488
U2 - 10.1007/978-3-030-74258-4_24
DO - 10.1007/978-3-030-74258-4_24
M3 - Conference Publication
AN - SCOPUS:85115079488
SN - 9783030742577
T3 - Lecture Notes in Civil Engineering
SP - 367
EP - 372
BT - Civil Structural Health Monitoring - Proceedings of CSHM-8 Workshop
A2 - Rainieri, Carlo
A2 - Fabbrocino, Giovanni
A2 - Caterino, Nicola
A2 - Ceroni, Francesca
A2 - Notarangelo, Matilde A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th Civil Structural Health Monitoring Workshop, CSHM-8 2021
Y2 - 31 March 2021 through 2 April 2021
ER -