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Response pattern analysis-based structural health monitoring of cable-stayed bridges

Authors
Lee, YunwooPark, Won-JooKang, Young-JongKim, Seungjun
Issue Date
11월-2021
Publisher
JOHN WILEY & SONS LTD
Keywords
cable-stayed bridge; measurement data; pattern analysis; structural health monitoring
Citation
STRUCTURAL CONTROL & HEALTH MONITORING, v.28, no.11
Indexed
SCIE
SCOPUS
Journal Title
STRUCTURAL CONTROL & HEALTH MONITORING
Volume
28
Number
11
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/128569
DOI
10.1002/stc.2822
ISSN
1545-2255
Abstract
Measurement systems using different sensors are currently well established for the safe use of structures in principal infrastructures, such as cable-stayed bridges. However, existing practical technologies that assess structural states by analyzing monitored data are underutilized. Although technologies to identify potential damage using advanced sensors or algorithms are continuously being developed, they have not reached a stage wherein they can be confidently applied. This study presents a methodology for monitoring structural response measurement data for application in current systems. The proposed methodology estimates changes in structural properties through pattern changes in measurement data; it is simple, clear, and easily applicable. An autoregressive model is used to analyze patterns in time series data. Pattern changes are analyzed based on reference data. A procedure for monitoring pattern variations in measurement data is derived, and the influences of variables considered in the monitoring procedure are also analyzed. The applicability of the methodology is examined based on the measurement data of an in-service cable-stayed bridge. As a basis for structural health monitoring based on pattern analysis, the proposed methodology is expected to be applied in various ways, through further studies, to relate the pattern of structural responses to the state of the structures.
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College of Engineering > School of Civil, Environmental and Architectural Engineering > 1. Journal Articles

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