A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes
A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes
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DOI：
10.12989/sss.2015.15.2.395
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发表时间：
2015-02
影响因子：
3.5
通讯作者：
Chul‐Woo Kim;T. Morita;Y. Oshima;K. Sugiura
中科院分区：
文献类型：
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作者：
Chul‐Woo Kim;T. Morita;Y. Oshima;K. Sugiura
本研究的目的是提出一种贝叶斯方法，考虑温度和车辆重量的变化作为环境和运营因素的振动为基础的长期桥梁健康监测。贝叶斯方法包括三个步骤：第一步是利用桥梁加速度从自回归模型的系数中识别损伤敏感特征，第二步是通过贝叶斯回归对损伤敏感特征进行回归分析，以考虑环境和运营变化;以及步骤3是基于残差，观测到的和预测的损伤敏感特征之间的差异，利用95%置信区间和贝叶斯假设检验。所提出的方法的可行性研究利用监测数据记录在一年的时间内在服务的桥梁。通过研究的观察表明，考虑环境和操作变化的贝叶斯回归比不考虑环境和操作变化的贝叶斯回归得到更准确的结果。利用健康桥梁的数据进行贝叶斯假设检验，判断桥梁的损伤概率为无损伤。关键词：桥梁长期监测;贝叶斯回归;温度;车重;振动
This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the autoregressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage. Keywords: long-term bridge monitoring; Bayesian regression; temperature; vehicle weight; vibration