Affiliations: Applied Mechanics and Systems Research Laboratory,
Tunisia Polytechnic School, University of Carthage, La Marsa, Tunisia | Mechanical Engineering Program, Texas A&M
University, Doha, Qatar | Laboratory of Materials, Energy and Optimization for
Sustainability, ENIT, University of El Manar, Tunis, Tunisia | Department of Civil Engineering, Salman Ben Abdellaziz
University, Al-Kharj, Saudi Arabia
Note: [] Corresponding author: S. El-Borgi, Applied Mechanics and Systems
Research Laboratory, Tunisia Polytechnic School, University of Carthage, B.P.
743, La Marsa 2078, Tunisia. Tel.: +216 98 59 30 05; Fax: +216 71 74 88 4;
E-mail: sami.elborgi@gnet.tn
Abstract: The authors recently developed a damage identification method which
combines ambient vibration measurements and a Statistical Modal Filtering
approach to predict the location and degree of damage. The method was then
validated experimentally via ambient vibration tests conducted on full-scale
reinforced concrete laboratory specimens. The main purpose of this paper is to
demonstrate the feasibility of the identification method for a real bridge. An
important challenge in this case is to overcome the absence of vibration
measurements for the structure in its undamaged state which corresponds ideally
to the reference state of the structure. The damage identification method is,
therefore, modified to adapt it to the present situation where the intact state
was not subjected to measurements. An additional refinement of the method
consists of using a genetic algorithm to improve the computational efficiency
of the damage localization method. This is particularly suited for a real case
study where the number of damage parameters becomes significant. The damage
diagnosis predictions suggest that the diagnosed bridge is damaged in four
elements among a total of 168 elements with degrees of damage varying from 6%
to 18%.
