In this article, a novel algorithm is proposed for real‐time system identification using hierarchical interhealing model classes. One major difficulty in the system identification for large structures is to determine the complexity of the structural model and the number of unknown parameters. One can detect finer damages with more unknown stiffness parameters, but this may cause fluctuating or even unidentifiable results. Although Bayesian model class selection allows one to choose among some prescribed model classes, the number of possible model classes for large structures is huge. In this paper, we propose a new method using hierarchical interhealing model classes. The modeling errors of these model classes can be corrected adaptively according to the data and the results from the optimal model class. This includes not only the errors in the parameters but also the deficiencies of the parametric models. Furthermore, the model classes are established in a hierarchical manner so that the proposed strategy requires only a small number of model classes, yet being able to explore a large solution space. Consequently, the proposed algorithm can handle a large number of damage possibilities while it maintains relatively low computational cost. Two examples are presented, and the results show that the proposed algorithm can detect, locate, and quantify damages reliably and efficiently.

中文翻译：

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使用层次修复模型类进行实时系统识别

在本文中，提出了一种新的算法，该算法使用分层的修复模型类进行实时系统识别。大型结构系统识别的一个主要困难是确定结构模型的复杂性和未知参数的数量。可以用更未知的刚度参数检测出更细小的损伤，但这可能会导致波动甚至无法确定的结果。尽管贝叶斯模型类别选择允许人们在某些规定的模型类别中进行选择，但是大型结构可能使用的模型类别的数量巨大。在本文中，我们提出了一种使用层次修复模型类的新方法。这些模型类别的建模误差可以根据数据和最佳模型类别的结果进行自适应校正。这不仅包括参数错误，还包括参数模型的缺陷。此外，模型类是以分层方式建立的，因此所提出的策略仅需要少量模型类，但仍能够探索较大的解决方案空间。因此，提出的算法可以处理大量的损坏可能性，同时保持相对较低的计算成本。给出了两个例子，结果表明所提算法能够可靠，有效地检测，定位和量化损伤。提出的算法可以处理大量的损坏可能性，同时保持相对较低的计算成本。给出了两个例子，结果表明所提算法能够可靠，有效地检测，定位和量化损伤。提出的算法可以处理大量的损坏可能性，同时保持相对较低的计算成本。给出了两个例子，结果表明所提算法能够可靠，有效地检测，定位和量化损伤。