Video based structural health monitoring using virtual visual sensors

Abstract

Structural health monitoring (SHM) has become a viable tool to provide owners of structures and mechanical systems with quantitative and objective data for maintenance and repair. Traditionally, discrete contact sensors such as strain gages or accelerometers have been used for SHM. However, distributed remote sensors could be advantageous since they don’t require cabling and can cover an area rather than just a few discrete points. Along this line a novel monitoring methodology based on video analysis is proposed. By employing commercially available digital cameras combined with efficient signal processing methods, measurement of natural frequencies using a computationally less demanding algorithm was possible. In this dissertation, the basic concept of the proposed so-called Eulerian-based virtual visual sensors (VVS) is first introduced. In order to improve the signal-to-noise ratio (SNR), the application of oversampling as well as two different targets were considered. The proposed methodology was evaluated on a set of laboratory experiments to demonstrate the accuracy of the considered approach. In-service monitoring examples of different bridges are further provided to show the practical aspects. A discussion of further work to improve the methodology is also discussed.