Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

中文翻译：

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基于人口的桥梁结构健康监测：比较桥梁组之间的相似性指标和动态响应


桥梁是宝贵的基础设施资产，维护起来具有挑战性且成本高昂。最先进的基于数据的桥梁健康监测解决方案希望使用桥梁响应数据进行状态评估和损坏检测。基于数据的 SHM 方法在其应用中可能受到限制，因为它们需要大量数据集来有效地训练模型，并且大多数桥梁缺乏用于工作方法的可用数据。此外，收集所需的数据集以对整个桥接网络采用基于数据的方法将是昂贵且不现实的。最近，提出了一种基于人群的结构健康监测 (PBSHM) 方法，该方法旨在通过将相似的结构与其数据集汇集在一起​​来利用可用于 SHM 问题的数据。 PBSHM 方法在桥梁 SHM 中可能很有价值，可以增强“桥梁群”可用的数据集。用于评估桥梁相似性的 PBSHM 方法之前已被考虑过，并且被证明对于识别相似和不同的桥梁类型很有用。然而，之前的工作没有考虑任何数据，并且相似性度量仅使用工程判断来限定。为了使 PBSHM 方法在桥 SHM 中有用，仍然需要检查“相似”的桥是否具有相似的响应，以使迁移学习可行。本文扩展了之前的工作，并通过调查被识别为相似的桥梁是否也表现出相似的响应来提供独创性。 PBSHM 导出的相似性度量传达了结构之间的拓扑相似性，并且振型被识别为拓扑敏感的桥梁响应。 因此，对一组六座真实桥梁进行了模态测试活动，并使用操作模态分析来识别每个桥面的模态响应。模态保证准则用于评估成对桥梁的振型之间的相似性，并随后与这些桥梁之间评估的相似性度量进行比较。研究发现，相似性度量反映了相同和不同类型桥梁各自桥梁振型之间确定的相似性。这一发现的意义在于，它是验证 PBSHM 比较方法以识别可能尝试迁移学习的相似结构的重要一步。