Bridge health monitoring system has produced a huge amount of monitored data (extreme stress data, etc.) in the long-term service periods; how to reasonably predict structural dynamic reliability with these data is one key problem in structural health monitoring (SHM) field. In this paper, considering the coupling, randomness, and time variation of SHM data, firstly, the coupled extreme stress data, which are considered as a time series, are decoupled into high-frequency and low-frequency data with the moving average method. Secondly, Bayesian dynamic linear models (BDLM) without priori monitoring error data (e.g., unknown monitored error variance) are built to dynamically predict the decoupled extreme stress; furthermore, the dynamic reliability of bridge members is predicted with the built BDLM and first-order second moment (FOSM) reliability method. Finally, an actual example is provided to illustrate the feasibility and application of the proposed models and methods. The research results of this paper will provide the theoretical foundations for structural reliability prediction.

中文翻译：

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基于解耦 SHM 极限应力数据和改进 BDLM 的桥梁动态可靠性预测

桥梁健康监测系统在长期使用期间产生了大量的监测数据（极端应力数据等）；如何利用这些数据合理预测结构动力可靠性是结构健康监测（SHM）领域的关键问题之一。本文考虑SHM数据的耦合性、随机性和时变性，首先将耦合的极端应力数据作为时间序列，采用移动平均法解耦为高频和低频数据。其次，建立没有先验监测误差数据（如未知监测误差方差）的贝叶斯动态线性模型（BDLM），动态预测解耦的极端应力；此外，桥梁构件的动力可靠度是用建成的 BDLM 和一阶二阶矩 (FOSM) 可靠度方法预测的。最后，通过一个实例来说明所提出的模型和方法的可行性和应用。本文的研究成果将为结构可靠性预测提供理论依据。