Health Monitoring Based on Dynamic Flexibility Matrix: Theoretical Models versus <i>In-Situ</i> Tests
Author(s) -
Sebastian Schommer,
Jean Mahowald,
Viet Ha Nguyen,
Danièle Waldmann,
Stefan Maas,
Arno Zürbes,
Guido De Roeck
Publication year - 2017
Publication title -
engineering
Language(s) - English
Resource type - Journals
eISSN - 1947-3931
pISSN - 1947-394X
DOI - 10.4236/eng.2017.92004
Subject(s) - flexibility method , flexibility (engineering) , stiffness , matrix (chemical analysis) , structural health monitoring , modal , stiffness matrix , structural engineering , bridge (graph theory) , computer science , engineering , mathematics , materials science , statistics , composite material , medicine
The paper focuses on damage detection of civil engineering structures and especially on concrete bridges. A method for structural health monitoring based on vibrational measurements is presented and discussed. Experimentally identified modal parameters (eigenfrequencies, mode shapes and modal masses) of bridge structures are used to calculate the inverse stiffness matrix, the so-called flexibility matrix. By monitoring of the stiffness matrix, damage can easily be detected, quantified and localized by tracking changes of its individual elements. However, based on dynamic field measurements, the acquisition of the flexibility matrix instead of the stiffness matrix is often the only choice and hence more relevant for practice. But the flexibility-based quantification and localisation of damage are often possible but more difficult, as it depends on the type of support and the location of the damage. These issues are discussed and synthetized, that is an originality of this paper and is believed useful for engineers in the damage detection of different bridge structures. First the theoretical background is briefly repeated prior to the illustration of the differences between stiffness and flexibility matrix on analytical and numerical examples. Then the flexibility-based detection is demonstrated on two true bridges with real-time measurement data and the results are promising.
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