A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

Abstract

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.