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  1. SSMSパブリケーション
  2. Vol.04

Structural Health Dignosis/Monitoring Using Neural Networks for Cable-Stayed Bridge Management System

http://hdl.handle.net/10173/1708
http://hdl.handle.net/10173/1708
6ed1aacb-7f83-4b65-b81f-46e8f6b8cb82
名前 / ファイル ライセンス アクション
SMS08-116.pdf SMS08-116.pdf (295.1 kB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2018-02-06
タイトル
タイトル Structural Health Dignosis/Monitoring Using Neural Networks for Cable-Stayed Bridge Management System
言語
言語 eng
キーワード
主題Scheme Other
主題 Neural Network
キーワード
主題Scheme Other
主題 Structural Health Diagnosis
キーワード
主題Scheme Other
主題 Monitoring
キーワード
主題Scheme Other
主題 Bridge Management System
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者 Lin, Chu-Chieh Jay

× Lin, Chu-Chieh Jay

Lin, Chu-Chieh Jay

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Chen, Chien-Chou

× Chen, Chien-Chou

Chen, Chien-Chou

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抄録
内容記述タイプ Abstract
内容記述 The cable-stayed bridge is generally a highly statically indeterminate structure. The structural performance of the bridge is highly sensitive to the load distribution among major components of the bridge. Therefore, the stayed cables of the cable–stayed bridge should be monitored to prevent bridge damage due to earthquake, strong wind, differential settlement, fatigue/defect of the material or loose of tension within the cables. That makes the structure health monitoring and diagnosis of the cable forces for the optimum structural performance very important in a cable-stayed bridge maintenance procedure. This study proposes a structural health diagnosis/monitoring management system for cable-stayed bridges using Neural Networks and field measurement data. The neural networks were used to 1. Analyze reversely the corresponding axial forces of all the stayed cables using sets of rotation measured from the pylon and 2. Determine the type and degree (scope) of the damaged bridge with ease and efficiency. Based on the cable force evaluated, the structural behavior including the deformation and stress state of the bridge can be traced successfully. Also, the damage state of the cable-stayed bridge can be identified using neural networks through the measured cable forces within stayed-cables. A few cases were studied and the results obtained could be applied for Cable-Stayed Bridge Management System.
書誌情報 Society for Social Management Systems Internet Journal

巻 4, 号 1, 発行日 2008-03
ISSN
収録物識別子タイプ ISSN
収録物識別子 2432-552X
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
出版者
出版者 Society for Social Management Systems
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