Sequential damage detection approaches for beams using time-modal features and artificial neural networks SCIE SCOPUS

Cited 35 time in WEB OF SCIENCE Cited 41 time in Scopus
Title
Sequential damage detection approaches for beams using time-modal features and artificial neural networks
Author(s)
Park, J.-H.; Kim, J.-T.; Hong, D.-S.; Ho, D.-D.; Yi, J.-H.
KIOST Author(s)
Yi, Jin Hak(이진학)
Publication Year
2009-06
Abstract
In this study, sequential approaches for damage detection in beams using time-modal features and artificial neural networks are proposed. The scheme of the sequential approaches mainly consists of two phases: time-domain damage monitoring and modal-domain damage estimation. In the first phase, an acceleration-based neural networks (ABNN) algorithm is designed to monitor the occurrence of damage in a structure by using cross-covariance functions of acceleration signals measured from two different sensors. By using the acceleration feature, the ABNN is trained for potential damage scenarios and loading patterns which are unknown. In the second phase, a modal feature-based neural networks (MBNN) algorithm is designed to estimate the location and severity of damage in the structure by using mode shapes and modal strain energies. By using the modal feature, the MBNN is trained for potential damage scenarios. The feasibility and the practicality of the proposed methodology are evaluated from numerical tests on simply supported beams and also from laboratory tests on free-free beams. © 2008 Elsevier Ltd. All rights reserved.
ISSN
0022-460X
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/4283
DOI
10.1016/j.jsv.2008.12.023
Bibliographic Citation
Journal of Sound and Vibration, v.323, no.1-2, pp.451 - 474, 2009
Publisher
Academic Press
Type
Article
Language
English
Document Type
Article
Publisher
Academic Press
Related Researcher
Research Interests

Ocean Energy-Tidal Current Energy Converter System,Infrastructure Management-Structural Health Monitoring,Offshore Wind,해양에너지-조류발전시스템,시설물 유지관리-구조건전성 평가,해상풍력

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