Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges

Darragh Lydon*, Rolands Kromanis, Myra Lydon, Juliana Early, Su Taylor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Displacement measurements can provide valuable insights into structural conditions and in-service behaviour of bridges under operational and environmental loadings. Computer vision systems have been validated as a means of displacement estimation; the research developed here is intended to form the basis of a real-time damage detection system. This paper demonstrates a solution for detecting damage to a bridge from displacement measurements using a roving vision sensor-based approach. Displacements are measured using a synchronised multi-camera vision-based measurement system. The performance of the system is evaluated in a series of controlled laboratory tests. For damage detection, five unsupervised anomaly detection techniques: Autoencoder, K-Nearest Neighbours, Kernel Density, Local Outlier Factor and Isolation Forest, are compared. The results obtained for damage detection and localisation are promising, with an f1-Score of 0.96–0.97 obtained across various analysis scenarios. The approaches proposed in this research provide a means of detecting changes to bridges using low-cost technologies requiring minimal sensor installation and reducing sources of error and allowing for rating of bridge structures.

Original languageEnglish
JournalJournal of Civil Structural Health Monitoring
Early online date18 Aug 2022
DOIs
Publication statusEarly online date - 18 Aug 2022

Bibliographical note

Funding Information:
This research was supported by Engineering and Physical Sciences Research Council (Grant EP/S036695/1).

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Anomaly detection
  • Computer vision
  • Deep learning
  • Structural health monitoring

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality

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