This paper describes development of a contactless, low cost vision-based system for displacement measurement of civil structures. Displacement measurements provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Conventional displacement gauges or GPS based systems have limitations in terms of access to the infrastructure and accuracy. The system introduced in this paper provides a low cost durable alternative which is rapidly deployable in the field and does not require direct contact or access to the infrastructure or its vicinity. A commercial action camera was modified to facilitate the use of a telescopic lens and paired with the development of robust displacement identification algorithms based on pattern matching. Performance was evaluated first in a series of controlled laboratory tests and validated against displacement measurements obtained using a fibre optic displacement gauge. The efficiency of the system for field applications was then demonstrated by capturing the validated bridge response of two structures under live loading including the iconic peace bridge. Located in the City of Derry, Northern Ireland, the Peace Bridge is a 310m curved self-anchored suspension pedestrian bridge structure. The vision-based results of the field experiment were confirmed against displacements calculated from measured accelerations during a dynamic assessment of the structure under crowd loading. In field applications the developed system can achieve a root mean square error (RMSE) of 0.03mm against verified measurements.

The engineering education and research sectors are interlinked, and there exists a need within both for readily deployable low-cost systems. Smartphones are affordable and easy to use technology available to almost everyone. Images or video frames taken with smartphone cameras, of structures subjected to loadings, can be analyzed to measure structural deformations. Such applications are very useful for university students and researchers when performing tests in laboratory environments. This paper investigates the feasibility of using smartphone technologies to measure structural deformation in the laboratory environment. Images and videos collected while structures are subjected to static, dynamic, and quasi-static loadings are analyzed with freeware and proprietary software. This study demonstrates capabilities of smartphone technologies, when coupled with suitable image processing software, for providing accurate information about structural deformations. Smartphones and open source software are affordable and available in comparison to professional cameras and proprietary software. The technology can be further developed to be used in real world environments to monitor deformation of engineering structures.

This paper presents a contactless multi-point displacement measurement system using multiple synchronized wireless cameras. Our system makes use of computer vision techniques to perform displacement calculations, which can be used to provide a valuable insight into the structural condition and service behaviour of bridges under live loading. The system outlined in this paper provides a low cost durable solution which is rapidly deployable in the field. The architecture of this system can be expanded to include up to ten wireless vision sensors, addressing the limitation of current existing solutions limited in scope by their inability to reliably track multiple points on medium and long span bridge structures. Our multi-sensor approach facilitates multi-point displacement and additional vision sensors for vehicle identification and tracking that could be used to accurately relate the bridge displacement response to the load type in the time domain. The performance of the system was validated in a series of controlled laboratory tests. This research will significantly advance current vision-based Structural health monitoring (SHM) systems which can be cost prohibitive and provides a rapid method of obtaining data which accurately relates to measured bridge deflections.

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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.