This paper presents a solution to the multi‐site structural damage identification problem using a data‐driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single‐site damage identification, limited research effort has been devoted to identifying multi‐site damage due its complexity. Efficient features for single‐site damage identification may lose their effectiveness when multi‐site damage occurs. This paper extracts damage‐sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi‐site damage cases from single‐site damage cases and locate the single‐site damage. Furthermore, the mixing matrix columns of multi‐site damage cases exhibit distinct correlation with that of the corresponding single‐site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi‐site damage identification in data‐driven structural health monitoring without requiring multi‐site damage data as a reference. This relieves the burden from data incompleteness when using data‐based damage identification methods and pattern recognition.

中文翻译：

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多站点损伤识别的数值研究：基于约束独立分量分析的数据驱动方法

本文提出了一种采用数据驱动方法和约束独立分量分析（cICA）的多站点结构损伤识别问题的解决方案。尽管该领域的现有研究为单站点损伤识别提供了令人鼓舞的结果，但由于其复杂性，有限的研究工作一直致力于识别多站点损伤。当发生多站点损坏时，用于单站点损坏识别的有效功能可能会失去其有效性。本文从在某些激励下结构响应的ICA结果中提取了损伤敏感特征。通过执行与完整结构相同的独立组件，将响应中包含的有关结构损坏的信息压缩到混合矩阵中。因此，cICA可以显着减小特征尺寸并保留所有有价值的损坏信息。案例研究表明，混合矩阵元素用作损坏特征时，可以将多站点损坏案例与单站点损坏案例区分开，并定位单站点损坏。此外，多站点损坏案例的混合矩阵列与相应的单站点损坏案例具有明显的相关性。结果，所提出的方法可以逐步定位结构损伤。此外，本研究具有在数据驱动的结构健康监测中识别多部位损伤的潜力，而无需多部位损伤数据作为参考。当使用基于数据的损坏识别方法和模式识别时，这减轻了数据不完整的负担。