Damaged cable detection with statistical analysis, clustering, and deep learning models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Son, Hyesook | - |
dc.contributor.author | Yoon, Chanyoung | - |
dc.contributor.author | Kim, Yejin | - |
dc.contributor.author | Jang, Yun | - |
dc.contributor.author | Tran, Linh Viet | - |
dc.contributor.author | Kim, Seung-Eock | - |
dc.contributor.author | Kim, Dong Joo | - |
dc.contributor.author | Park, Jongwoong | - |
dc.date.accessioned | 2022-02-10T06:40:26Z | - |
dc.date.available | 2022-02-10T06:40:26Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1738-1584 | - |
dc.identifier.issn | 1738-1584 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/54957 | - |
dc.description.abstract | The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 국제구조공학회 | - |
dc.title | Damaged cable detection with statistical analysis, clustering, and deep learning models | - |
dc.type | Article | - |
dc.identifier.doi | 10.12989/sss.2022.29.1.017 | - |
dc.identifier.bibliographicCitation | Smart Structures and Systems, An International Journal, v.29, no.1, pp 17 - 28 | - |
dc.identifier.kciid | ART002806535 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000744096700002 | - |
dc.identifier.scopusid | 2-s2.0-85129130992 | - |
dc.citation.endPage | 28 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 17 | - |
dc.citation.title | Smart Structures and Systems, An International Journal | - |
dc.citation.volume | 29 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | LSTM | - |
dc.subject.keywordAuthor | time series | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.relation.journalWebOfScienceCategory | Engineering, Mechanical | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.