Journal of Civil Structural Health Monitoring

, Volume 5, Issue 4, pp 457–468

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

Original Paper

DOI: 10.1007/s13349-015-0128-5

Cite this article as:
Shariati, A., Schumacher, T. & Ramanna, N. J Civil Struct Health Monit (2015) 5: 457. doi:10.1007/s13349-015-0128-5

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.

Keywords

Natural frequencies Structural vibrations Structural health monitoring Video analysis Eulerian-based virtual visual sensors Linear gradient pattern targets 

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.University of DelawareNewarkUSA

Personalised recommendations