An improved stochastic subspace modal identification method considering uncertainty quantification. (May 2023)

• Record Type: Journal Article
• Authors: Zhou, Kang; Zhi, Lun-Hai; Wang, Jing-Feng; Hong, Xu; Xu, Kang; Shu, Zhen-Ru
• Is Part Of: Structures. Volume 51(2023)
• Page Start: 1083
• Page End: 1094
• Publication Date: 2023-05
• Subjects: Modal parameter identification -- Stochastic subspace identification -- Uncertainty quantification -- Civil structure -- Supertall building; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2023.03.101
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: This paper develops a novel stochastic subspace modal identification method in which the uncertainty quantification of structural modal parameters is considered. On the basis of a 4-degree-of-freedom (4DOF) simulation model, the detailed procedure of the proposed approach is first demonstrated. Then, by conducting a series of numerical simulations on the 4DOF model, the accuracy and effectiveness of the proposed method are verified for the cases of structural responses containing high-level noise and non-stationary properties. Furthermore, the proposed approach is applied to field measurements on a 260-m-high supertall building under ambient excitations, and the estimated modal parameters are further compared with the results determined by the forced vibration test performed on the building. The good agreement between these two sets of estimated modal parameters verifies the applicability and effectiveness of the developed modal identification method for field measurements. The objective of this study is to provide an efficient instrument for the accurate modal identification of civil structures.