Long-term bridge health monitoring and performance assessment based on a Bayesian approach
Long-term bridge health monitoring and performance assessment based on a Bayesian approach
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DOI：
10.1080/15732479.2018.1436572
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发表时间：
2018-03
影响因子：
3.7
通讯作者：
Chul‐Woo Kim;Yi Zhang-;Ziran Wang;Y. Oshima;T. Morita
中科院分区：
文献类型：
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作者：
Chul‐Woo Kim;Yi Zhang-;Ziran Wang;Y. Oshima;T. Morita
摘要本研究为桥梁结构的长期健康监测提供了一种损伤检测方法。采用贝叶斯回归和贝叶斯假设检验相结合的贝叶斯方法，对在役七跨在役Gerber系统钢板梁桥的结构变化进行检测。分析中同时考虑了温度和车辆重量的影响。在这项研究中使用了桥梁跨度四点的加速度响应。在桥梁健康监测中使用了三个不同时段的数据。回归分析表明，同时考虑温度和车辆重量影响的自回归外生模型性能最好。贝叶斯因子被发现是BHM中一个敏感的损伤指标。贝叶斯方法可以为桥梁结构的实时监测提供最新的信息。与传统方法相比，贝叶斯方法提供的信息更方便、更容易处理。通过将人工生成的损伤数据添加到观测数据中的案例研究，验证了该方法的适用性。
Abstract 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.