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.

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.

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.

Recent skills surveys of engineering graduates have highlighted a deficit in critical thinking among graduates. A possible solution to this is to increase the number of hands-on exercises in the curriculum. This could be carried out through the integration of 3D learning tools, specifically a virtual reality (VR) program, to effectively teach civil engineering practical studies and allow repeatable and measurable exercises for students. This study aims to assess the suitability of the VR program as an additional resource alongside existing learning exercises or a substitute for hands-on experiments when needed. The methodology involved creating a VR program, compatible with VR headsets to replicate an engineering experiment, namely the loading of a concrete beam to observe its failure. Students’ understanding of the virtual experiment was evaluated through end-of-experiment questions. The findings indicate that the VR learning tool was successful in enhancing students’ understanding of the civil engineering experiment. The immersive and interactive nature of VR contributed to a solid grasp of the concepts presented, proving its potential as a valuable educational resource. By leveraging VR technology, educational institutions can provide an engaging and effective alternative to traditional laboratory sessions, ensuring uninterrupted and high-quality learning experiences for civil engineering students.