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Get Free AccessVibration-based structural health monitoring of civil structures relies on the repeated identification of dynamic structural characteristics of the structure from output-only vibration data. Natural frequencies and displacement mode shapes are the most commonly employed dynamic characteristics; yet their sensitivity to local damage of moderate severity is rather low with respect to their sensitivity to other factors such as temperature, necessitating data normalization. Strain mode shapes offer a higher sensitivity to local damage, but their accurate identification in a dense grid is challenging given the very small dynamic strain levels that are encountered under ambient excitation. In this article, a method is presented for tackling this challenge. It consists of three stages. First, fiber-optic Bragg grating strain sensors are attached to the structure and interrogated with a tunable laser performing a wavelength sweep. In this way, the measured strain amplitudes have the required accuracy but synchronization errors are introduced between the different Bragg sensors. Second, a modal analysis is performed on the dynamic strain data using an accurate parametric system identification technique. This is followed by a synchronization step which compensates for the delays introduced by the wavelength sweep. Finally, the synchronized strain mode shapes are employed as damage-sensitive features, either directly or via a newly proposed quantity, the top-to-bottom strain ratio. The method is validated by progressive damage testing of a complex, prestressed concrete “roof” beam, reinforced with steel fibers. It is observed that the proposed method can identify both the presence and the location of the damage in a relatively early stage.
Dimitrios Anastasopoulos, Maure De Smedt, Lucie Vandewalle, Guido De Roeck, Edwin Reynders (2017). Damage identification using modal strains identified from operational fiber-optic Bragg grating data. Structural Health Monitoring, 17(6), pp. 1441-1459, DOI: 10.1177/1475921717744480.
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Type
Article
Year
2017
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
Structural Health Monitoring
DOI
10.1177/1475921717744480
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