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Bayesian model selection for ARX models and its application to structural health monitoring

机译：ARX模型的贝叶斯模型选择及其在结构健康监测中的应用

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摘要

A Bayesian framework for model order selection of auto-regressive exogenous (ARX) models is developed and applied to actual earthquake response data obtained by the structural health monitoring system of a high-rise building. The model orders of ARX models are selected appropriately by the Bayesian framework, and differ significantly from the optimal order estimated by AIC; in fact, in many cases AIC does not even give an optimal order. A method is also proposed for consistently selecting the same 'genuine' modes of interest from the whole set of modes corresponding to each of the identified models from a sequence of earthquake records. In the identification analysis based on building response records from 43 earthquakes over 9 years, the modal parameters of the first four modes in each horizontal direction are estimated appropriately in all cases, showing that the developed methods are effective and robust. As the estimates of natural frequency depend significantly on the response amplitude, they are compensated by an empirical correction so that the influence of the response amplitude is removed. The compensated natural frequencies are much more stable over the nine-year period studied, indicating that the building had no significant change in its global dynamic characteristics during this period.
机译：建立了用于自动回归外生（ARX）模型的模型顺序选择的贝叶斯框架，并将其应用于高层建筑结构健康监测系统获得的实际地震响应数据。 ARX模型的模型阶数是通过贝叶斯框架适当选择的，与AIC估计的最佳阶数有很大差异；实际上，在许多情况下，AIC甚至都没有给出最佳顺序。还提出了一种方法，该方法用于从与一系列地震记录中的每个已识别模型相对应的整个模式集中，一致地选择相同的“真正”感兴趣的模式。在基于9年间43次地震的建筑物响应记录的识别分析中，在所有情况下都适当地估计了每个水平方向上的前四个模态的模态参数，表明所开发的方法是有效且可靠的。由于固有频率的估计值在很大程度上取决于响应幅度，因此通过经验校正对其进行补偿，从而消除了响应幅度的影响。在研究的九年期间内，补偿后的自然频率要稳定得多，这表明该建筑物在此期间的全球动态特性没有明显变化。

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