Research on structural health monitoring (SHM) is nowadays evolving from SHM‐based diagnosis towards SHM‐based prognosis. The structural strain response, as a localized response, has gained growing attention for application to structural condition assessment and prognosis in that continuous strain measurement can offer information about the stress experienced by an in‐service structure and is better suited to characterize local deficiency and damage of the structure than global responses. As such, accurate forecasting of the structural strain response in real time is essential for both structural condition diagnosis and prognosis. In this paper, a Bayesian modeling approach embedding model class selection is proposed for dynamic forecasting purpose, which enables the probabilistic forecasting of structural strain response and bears a strong capability of modeling the underlying non‐stationary dynamic process. As opposed to the classical time series models, the proposed Bayesian dynamic linear model (BDLM) accommodates both stationary and non‐stationary time series data and delineates the time‐dependent structural strain response through invoking different hidden components, such as overall trend, seasonal (cyclical), and regressive components. It in turn paves an effective way for incorporating the newly observed time‐variant data into the model framework for structural response prediction. By embedding a novel model class selection paradigm into the BDLM, the proposed algorithm enables simultaneous model class selection and probabilistic forecasting of strain responses in a real‐time manner. The utility of the proposed approach and its forecasting accuracy are examined by using the real‐world monitoring data successively collected from a three‐tower cable‐stayed bridge.

中文翻译：

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利用结构健康监测数据进行结构应变响应的贝叶斯动态预测

如今，结构健康监测（SHM）的研究已从基于SHM的诊断发展为基于SHM的预后。结构应变响应作为一种局部响应，在结构状态评估和预后中的应用越来越受到关注，因为连续应变测量可以提供有关在役结构所经历的应力的信息，并且更适合于表征局部缺陷和损坏的结构要比全局的响应高。因此，实时准确预测结构应变响应对于结构状况的诊断和预后至关重要。本文针对动态预测的目的，提出了一种基于贝叶斯建模方法的嵌入模型类别选择方法，这使得能够对结构应变响应进行概率预测，并具有对潜在的非平稳动力过程进行建模的强大能力。与经典的时间序列模型相反，建议的贝叶斯动态线性模型（BDLM）容纳固定和​​非平稳时间序列数据，并通过调用不同的隐藏成分（如总体趋势，季节性（周期性）和回归成分。反过来，它为将新观察到的时变数据整合到模型框架中进行结构响应预测提供了有效的方法。通过将新颖的模型类别选择范例嵌入到BDLM中，该算法可以同时进行模型类别选择和应变响应的概率预测。