Highway bridges are large and expensive structures. Failure in such a structure causes a huge impact on human life and the economy. Many studies show that approximately 250,000 of the more than existing 570,000 highway bridges in the United States are deficient and in need of rehabilitation. It is necessary to know the condition of bridges in order to prevent an abrupt failure. Visual investigation is normally used to monitor bridge structures but is not sufficient. Sophisticated procedures like x-ray, acoustic emission, and magnetic resonance can provide great detail and a reliable investigation, but those procedures are expensive and time consuming. An alternative method for monitoring bridge structures is damage detection using system identification methods. These methods are in a group of nondestructive damage detection techniques. System identification is the process of matching a mathematical model to an existing structure. This is based on the fact that when structure is damaged, its characteristic response is also changed. This research studies the possibility of using system identification methods to detect damage in bridge structures using the information of the change in structural characteristics. A three-dimensional bridge finite element model was used as the mathematical model. Eigen properties (eigenvectors and eigenvalues) and Ritz vectors are used as structural characteristics. This research proposes a screening algorithm for finding the damage by reducing the number of design variables and adjusting the amount of perturbation during the optimization as well as using a relative error as an objective function. This research also studies the sensitivity of major parameters that affect the damage detection using the system identification method. The research yields a damage detection tool that successfully identifies location and extent of simulated damage in bridge structures.
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