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Damage Assessment in Bridges Based on Measured Natural Frequencies

Autor(en):

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: International Journal of Structural Stability and Dynamics, , n. 2, v. 17
Seite(n): 1750022
DOI: 10.1142/s0219455417500225
Abstrakt:

This paper presents an identification technique for damage assessment of structures where only the information about the changes of measured natural frequencies can be directly utilized. The structural damage is characterized by a local decrease in the stiffness as represented by a scalar reduction of the material modulus. The objective of this study is to investigate the feasibility of using such a technique for identifying the structural damage in a real steel girder bridge. Numerical examples involving damaged reinforced concrete beams are first used to demonstrate the capability of the proposed computational technique, based on the nonlinear perturbation theory, to predict the exact location and severity of the damage. To experimentally validate the theory, laboratory damage detection experiments were performed on a simply supported reinforced concrete beam with various damage scenarios as the example. The results of the damage identification procedure based on the measurement of structure’s frequencies before and after occurrence of the damage show that this method can accurately locate the damage and predict the extent of damage. The method performs well even for a structure with a very serious damage as demonstrated by application of the proposed direct iteration technique to a six-span steel girder bridge. Using a limited number of measured natural frequencies, significant reduction in the stiffness of the bridge at multi-sites is detected.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1142/s0219455417500225.
  • Über diese
    Datenseite
  • Reference-ID
    10352413
  • Veröffentlicht am:
    14.08.2019
  • Geändert am:
    14.08.2019
 
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