Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges. (18th February 2020)

• Record Type: Journal Article
• Authors: Wen, Peng; Khan, Inamullah; Jie, He; Qiaofeng, Chen; Shiyu, Yang
• Other Names: Kaewunruen Sakdirat Academic Editor.
• Is Part Of: Shock and vibration. Volume 2020(2020)
• Publication Date: 2020-02-18
• Subjects: Shock (Mechanics) -- Periodicals; Vibration -- Periodicals; 534.5
• DOI: 10.1155/2020/2040216
• Languages: English
• ISSNs: 1070-9622

Abstract

Abstract : Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters.