Use of A Roving Vision Sensor Setup to Train an Autoencoder for Damage Detection of Bridge Structures

Research output: Chapter in Book/Report/Conference proceedingChapter


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 contactless vision based multiple point displacement measurement system using wireless action cameras. 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 which is rapidly deployable in the field. 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.
Original languageEnglish
Title of host publicationCivil Structural Health Monitoring Proceedings of CSHM-8 Workshop
ISBN (Print)978-3-030-74257-7
Publication statusPublished - 25 Aug 2021
EventCSHM-8 Civil Structural Health Monitoring Workshop - Pacanowski Palace, Naples, Italy
Duration: 16 Sep 202018 Sep 2020


ConferenceCSHM-8 Civil Structural Health Monitoring Workshop
Internet address


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