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Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model

机译：利用改进的贝叶斯动态线性模型建模与预测长跨度桥梁的温度诱导应变

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Temperature-driven baseline is highly responsive to anomalous structural behavior of long-span bridges, which means that the discrepancy between the measured and forecasting temperature-induced strain (TIS) can be examined for anomalies. In this regard, it is important to guarantee the accuracy of the forecasting TIS responses for reliable assessment of structural performance. Bayesian dynamic linear model (BDLM) has shown a promising application in the field of structural health monitoring. Traditionally, BDLM is used to forecast structural responses by utilizing its trend form, seasonal form, regression form, or combination of the three forms. However, different features of time series cannot be totally captured by these forms, which would undermine the accuracy of BDLM. To improve the computational accuracy, an improved BDLM, which considers an auto-regressive (AR) component in addition to the trend, seasonal and regression components, is presented in this paper. Specifically, the AR component is able to model the component which cannot be captured by other three components. The real-time monitoring data collected from a long-span cable-stayed bridge is utilized to demonstrate the feasibility of the improved BDLM-based method. In particular, the present BDLM-based method allows for probabilistic forecasts, offering substantial information about the target TIS response, such as mean and confidence interval. Results show that the improved BDLM is capable of capturing the relationship between temperature and TIS. Compared to the AR model, multiple linear regression (MLR) model and BDLM without the AR component, the improved BDLM shows better forecasting performance in modeling and forecasting the TIS of a long-span bridge.
机译：温度驱动的基线对长跨度桥的异常结构行为高度响应，这意味着可以检查测量和预测温度诱导的菌株（TIS）之间的差异以用于异常。在这方面，重要的是保证预测TIS响应的准确性，以获得对结构性能的可靠性评估。贝叶斯动态线性模型（BDLM）在结构健康监测领域显示了有希望的应用。传统上，BDLM通过利用其趋势形式，季节性形式，回归形式或三种形式的组合来预测结构响应。然而，时间序列的不同特征不能完全被这些形式捕获，这会破坏BDLM的准确性。为了提高计算准确性，在本文中提出了一种改进的BDLM，该BDLM考虑了自动回归（AR）组件的趋势，季节性和回归组件。具体地，AR组件能够模拟不能被其他三个组件捕获的组件。利用从长跨度斜拉桥收集的实时监测数据来展示改进的基于BDLM的方法的可行性。特别地，本发明的基于BDLM的方法允许概率预测，提供关于目标TIS响应的实质性信息，例如均值和置信区间。结果表明，改进的BDLM能够捕获温度与TIS之间的关系。与AR模型相比，多个线性回归（MLR）型号和BDLM而没有AR组件，改进的BDLM在建模和预测到长跨度桥的TIS中显示了更好的预测性能。

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