Long-term bridge health monitoring and performance assessment based on a Bayesian approach 机翻标题: 暂无翻译,请尝试点击翻译按钮。

来源
Structure and Infrastructure Engineering
年/卷/期
2018 / 14 / 7/9
页码
883-894
ISSN号
1573-2479
作者单位
Kyoto Univ, Dept Civil & Earth Resources Engn, Kyoto, Japan;Kyoto Univ, Dept Civil & Earth Resources Engn, Kyoto, Japan;Kyoto Univ, Dept Civil & Earth Resources Engn, Kyoto, Japan;CAESAR, Publ Works Res Inst, Tsukuba, Ibaraki, Japan;Obayashi Corp, Tokyo, Japan;
作者
Zhang, Yi;Wang, Ziran;Kim, Chul-Woo;Oshima, Yoshinobu;Morita, Tomoaki;
摘要
This study presents a damage detection approach for the long-term health monitoring of bridge structures. The Bayesian approach comprising both Bayesian regression and Bayesian hypothesis testing is proposed to detect the structural changes in an in-service seven-span steel plate girder bridge with Gerber system. Both temperature and vehicle weight effects are accounted in the analysis. The acceleration responses at four points of the bridge span are utilised in this investigation. The data covering three different time periods are used in the bridge health monitoring (BHM). Regression analyses showed that the autoregressive exogenous model considering both temperature and vehicle weight effects has the best performance. The Bayesian factor is found to be a sensitive damage indicator in the BHM. The Bayesian approach can provide updated information in the real-time monitoring of bridge structures. The information provided from the Bayesian approach is convenient and easy to handle compared to the traditional approaches. The applicability of this approach is also validated in a case study where artificially generated damage data is added to the observation data.
机翻摘要
暂无翻译结果,您可以尝试点击头部的翻译按钮。
关键词/主题词
Autoregressive model;Bayesian statistics;bridge health monitoring;damage detection;Kalman filter;long-term assessment;real bridge;
若您需要申请原文,请登录。

最新评论

暂无评论。

登录后可以发表评论

意见反馈
返回顶部