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Eulerian-based virtual visual sensors to detect natural frequencies of structures

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Abstract

Natural frequency of vibration data are often used to study the behavior of structures. They are also used to calibrate finite element models and some studies have proposed that the presence and location of damage can be estimated using these data. Along this line, we earlier proposed the concept of Eulerian-based virtual visual sensors to estimate natural frequencies of structural vibrations based on the change of pixel intensity captured in a digital video. Benefits of this approach are that it allows for distributed sensing and is contactless. However, as intensity does not reflect any physical quantity, such as displacement, and the range of values is difficult to control, the signal-to-noise ratio (SNR) can be relatively low. Furthermore, impulsive changes of intensity caused by large deformations compared to the pixel size can result in an impulse train in the frequency domain which leads to ambiguity in determining peak frequencies. As a result, it is often only possible to estimate the first fundamental mode of vibration. In this paper, we present strategies using targets mounted to the structure combined with signal processing methods that significantly improve the SNR and allow for detecting higher natural frequencies of vibration. The concepts, their mathematical background, laboratory tests to prove the accuracy and enhancement of SNR, as well as an example of an in-service pedestrian bridge are presented and discussed.

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Acknowledgments

This research was supported by a USDOT-RITA UTC Program, contract no. DTRT12-G-UTC16, coordinated by the Center for Advanced Infrastructure and Transportation (CAIT). The experimental work was performed in the Structures Laboratory at the University of Delaware. Special thanks go to Prof. Branko Glisic and his research team at Princeton University for coordinating the field experiment on the Streicker Bridge in Princeton, NJ presented in Sect. 6. Finally, we would like to thank Dan Aronovitz and his team from Tech Imaging, Inc. for providing the two Photron high-speed cameras for this study.

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  1. University of Delaware, Newark, DE, USA

    Ali Shariati, Thomas Schumacher & Nakul Ramanna

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  1. Ali Shariati
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  2. Thomas Schumacher
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  3. Nakul Ramanna
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Correspondence to Ali Shariati.

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Shariati, A., Schumacher, T. & Ramanna, N. Eulerian-based virtual visual sensors to detect natural frequencies of structures. J Civil Struct Health Monit 5, 457–468 (2015). https://doi.org/10.1007/s13349-015-0128-5

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  • DOI: https://doi.org/10.1007/s13349-015-0128-5

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