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10 April 2007 Real-time damage monitoring scheme in PSC girder bridge using output-only acceleration data
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Artificial neural networks (ANNs) have been increasingly utilized for structural health monitoring (SHM) due to the advantage that it needs only a few training data to detect damage in structures. In this study, a new damage monitoring method using a set of parallel ANNs and acceleration signals is developed for alarming locations of damage in PSC girder bridges. First, theoretical backgrounds are described. The problem addressed in this paper is defined as the stochastic process. In addition, a parallel ANN-algorithm using output-only acceleration responses is newly designed for damage detection in real time. The cross-covariance of two acceleration-signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility of the proposed method is evaluated from numerical model tests on PSC beams for which accelerations were acquired before and after several damage cases.
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Jeong-Tae Kim, Jae-Hyung Park, Han-Sung Do, and Jung-Mi Lee "Real-time damage monitoring scheme in PSC girder bridge using output-only acceleration data", Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65292M (10 April 2007);

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