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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 216860, 11 pages
Research Article

Statistical Estimation of Changes in the Dominant Frequencies of Structures in Long Noisy Series of Monitoring Data

1Laboratory of Geodesy and Geodetic Applications, Department of Civil Engineering, University of Patras, 26500 Patras, Greece
2GFZ German Research Centre for Geosciences, Section 5.4 Hydrology, Telegrafenberg, 14473 Potsdam, Germany

Received 24 September 2013; Accepted 12 November 2013

Academic Editor: Ting-Hua Yi

Copyright © 2013 Fanis Moschas and Eva Steirou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Damage in structures is reflected in permanent changes of their natural frequencies and theoretically can be derived through measurements. Still, measurement-derived frequencies of structures usually reflect a superimposition of various effects, fluctuations due to environmental and loading conditions, noise, and possible permanent changes (damage or repair). The amplitude of the latter is usually of the same order of magnitude with the other effects; hence permanent shifts are masked by noise and cannot be identified, especially in long monitoring records. In order to overcome this problem, essential for the assessment of the structural health of various key structures, we adopt a statistical approach developed for the identification of shifts (inhomogeneities) in normally distributed climatological data, in particular the SNHT test. The efficiency of the SNHT was first tested on synthetic data and then on sets of estimates of dominant frequencies of a decaying pedestrian bridge. It was found that under certain conditions the SNHT can identify the location of shifts in dominant frequencies of structures; the amplitude of the shifts can then be easily computed. Since the efficiency of the test increases with the length of the time series, this test seems especially suitable for the analysis of long monitoring records.