Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sungkon | - |
dc.contributor.author | Lee, Jungwhee | - |
dc.contributor.author | Park, Min-Seok | - |
dc.contributor.author | Jo, Byung-Wan | - |
dc.date.accessioned | 2022-12-20T20:33:00Z | - |
dc.date.available | 2022-12-20T20:33:00Z | - |
dc.date.created | 2022-08-26 | - |
dc.date.issued | 2009-10 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/176065 | - |
dc.description.abstract | This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jo, Byung-Wan | - |
dc.identifier.doi | 10.3390/s91007943 | - |
dc.identifier.scopusid | 2-s2.0-70350381538 | - |
dc.identifier.wosid | 000271265800018 | - |
dc.identifier.bibliographicCitation | SENSORS, v.9, no.10, pp.7943 - 7956 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 9 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 7943 | - |
dc.citation.endPage | 7956 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordAuthor | bridge weigh-in-motion (B-WIM) | - |
dc.subject.keywordAuthor | artificial neural network (ANN) | - |
dc.subject.keywordAuthor | cable-stayed bridge | - |
dc.subject.keywordAuthor | vehicle information | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/9/10/7943 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.