For predicting dynamic coupled extreme stresses of bridges with monitoring coupled data, this article considers monitoring extreme stress data as a time series, and takes into account its coupling generated by the fusion of non-stationarity and randomness. First, the local polynomial theory is introduced, and the local polynomial order of monitoring coupled extreme stress data is estimated with time-series analysis method. Second, based on time-series analysis results, dynamic linear trend models (DLTM) and the corresponding Bayesian probability recursive processes are given to predict dynamic coupled extreme stresses. Finally, through the illustration of monitoring coupled extreme stress data from an actual bridge, the proposed method, which is compared with the traditional Bayesian dynamic linear models, is proved to be more effective for predicting dynamic coupled extreme stresses of bridges.

中文翻译：

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基于DLTM和监测耦合数据的桥梁极端应力贝叶斯预测

为了利用监测耦合数据预测桥梁的动态耦合极限应力，本文将监测极限应力数据视为一个时间序列，并考虑其非平稳性和随机性融合产生的耦合。首先，引入局部多项式理论，采用时间序列分析方法估计监测耦合极应力数据的局部多项式阶数。其次，基于时间序列分析结果，给出动态线性趋势模型（DLTM）和相应的贝叶斯概率递归过程来预测动态耦合的极端应力。最后，通过监测来自实际桥梁的耦合极端应力数据的说明，将所提出的方法与传统的贝叶斯动态线性模型进行比较，