Modified Low-Cycle Fatigue Estimation Using Machine Learning for Radius-Cut Coke-Shaped Metallic Damper Subjected to Cyclic Loading
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae, Jaehoon | - |
dc.contributor.author | Lee, Chang-Hwan | - |
dc.contributor.author | Park, Minjae | - |
dc.contributor.author | Alemayehu, Robel Wondimu | - |
dc.contributor.author | Ryu, Jaeho | - |
dc.contributor.author | Ju, Young K. | - |
dc.date.accessioned | 2021-08-30T07:08:14Z | - |
dc.date.available | 2021-08-30T07:08:14Z | - |
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/51364 | - |
dc.description.abstract | In this study, a coke-shaped steel damper that exhibits in-plane resistance is introduced as a passive damper. The double-coke damper presented in this study applies the concept of reduced beam sections to increase the ductility in the case of a prolonged earthquake. Multiplastic hinges are placed on each strip by setting the radius-cut section. The fatigue performance of the damper during earthquake loading is verified through a constant cyclic loading test. The results indicate that, as the number of plastic hinges inside the strip increases, the damper ductility increases, producing a stable hysteresis graph. In addition, a new equation that considers the damage index using parameters such as maximum strength and effective stiffness is proposed, and the experimental results are found to be in excellent agreement with the number of failure cycles obtained from the proposed model. By comparing the results of applying the proposed equation with the machine learning results, it is demonstrated that machine learning can be used for estimating the damper performance against the fatigue of the resistive cycle. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN SOC STEEL CONSTRUCTION-KSSC | - |
dc.subject | STEEL STRUCTURES | - |
dc.subject | STRIP DAMPER | - |
dc.subject | DAMAGE | - |
dc.subject | PERFORMANCE | - |
dc.subject | FRICTION | - |
dc.title | Modified Low-Cycle Fatigue Estimation Using Machine Learning for Radius-Cut Coke-Shaped Metallic Damper Subjected to Cyclic Loading | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ju, Young K. | - |
dc.identifier.doi | 10.1007/s13296-020-00377-7 | - |
dc.identifier.scopusid | 2-s2.0-85087733166 | - |
dc.identifier.wosid | 000546897600002 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF STEEL STRUCTURES, v.20, no.6, pp.1849 - 1858 | - |
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 | 1849 | - |
dc.citation.endPage | 1858 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002663102 | - |
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 | STEEL STRUCTURES | - |
dc.subject.keywordPlus | STRIP DAMPER | - |
dc.subject.keywordPlus | DAMAGE | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | FRICTION | - |
dc.subject.keywordAuthor | Plastic hinge | - |
dc.subject.keywordAuthor | Passive damper | - |
dc.subject.keywordAuthor | Low-cycle fatigue | - |
dc.subject.keywordAuthor | Machine learning | - |
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.