A Study on the Long-Term Measurement Data Analysis of Existing Cable Stayed Bridge Using ARX Model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Yunwoo | - |
dc.contributor.author | Jang, Minseo | - |
dc.contributor.author | Kim, Seungjun | - |
dc.contributor.author | Kang, Young-Jong | - |
dc.date.accessioned | 2021-08-30T07:07:25Z | - |
dc.date.available | 2021-08-30T07:07:25Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 1598-2351 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/51354 | - |
dc.description.abstract | Various sensors have been installed in cable-stayed bridges to monitor the behavior of structures and external conditions. These sensors alert the administrator to take appropriate action when an abnormal signal is detected. Although inherent meaningful information about the history of structural responses in long-term accumulated measurement data are available, the methodology for utilizing such data in the long-term point of view has not yet been established. Structural response is determined by the mechanical principle of external loads and the structural system characteristics. Assuming that structural responses have a certain pattern in a constant condition, the state of the structure can be estimated to have changed or not through an analysis of the pattern variation of the measured data. This study utilizes the temperature and displacement data of a cable-stayed bridge to analyze the pattern variation of the measurement data. An autoregressive model is used to define the pattern of the time series data. A pattern model is then constructed with the data adopted as a reference for comparison. The compared data are applied to the pattern model to simulate the data reflecting the reference data pattern. Subsequently, the simulated data are compared with the actual data, and the pattern difference is computed through the error discriminant index. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN SOC STEEL CONSTRUCTION-KSSC | - |
dc.subject | STRUCTURAL DAMAGE DETECTION | - |
dc.subject | PREDICTION | - |
dc.subject | SERIES | - |
dc.title | A Study on the Long-Term Measurement Data Analysis of Existing Cable Stayed Bridge Using ARX Model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seungjun | - |
dc.contributor.affiliatedAuthor | Kang, Young-Jong | - |
dc.identifier.doi | 10.1007/s13296-020-00376-8 | - |
dc.identifier.scopusid | 2-s2.0-85088089951 | - |
dc.identifier.wosid | 000549302200001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF STEEL STRUCTURES, v.20, no.6, pp.1871 - 1881 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF STEEL STRUCTURES | - |
dc.citation.title | INTERNATIONAL JOURNAL OF STEEL STRUCTURES | - |
dc.citation.volume | 20 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1871 | - |
dc.citation.endPage | 1881 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002669743 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Construction & Building Technology | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Construction & Building Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.subject.keywordPlus | STRUCTURAL DAMAGE DETECTION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | SERIES | - |
dc.subject.keywordAuthor | Cable-stayed bridge | - |
dc.subject.keywordAuthor | Long term | - |
dc.subject.keywordAuthor | Measurement data | - |
dc.subject.keywordAuthor | Data analysis | - |
dc.subject.keywordAuthor | Pattern analysis | - |
dc.subject.keywordAuthor | Autoregressive model | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.