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ANN-Based Real-Time Damage Detection Technique Using Acceleration Signals in Beam-Type Structures  

Park, Jae-Hyung (부경대학교 해양공학과)
Lee, Yong-Hwan (한국유지관리㈜ 유지관리사업부)
Kim, Jeong-Tae (부경대학교 해양공학과)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.20, no.3, 2007 , pp. 229-237 More about this Journal
Abstract
In this study, an artificial neural network (ANN)-based damage detection algorithm using acceleration signals is developed for real-time alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed tot 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 lot potential loading Patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.
Keywords
real-time damage detection; artificial neural network; cross-covariance,; acceleration-based; beam-type structure; structural health monitoring;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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