Continuous modal parameter identification of cable-stayed bridges based on a novel improved ensemble empirical mode decomposition. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Khan, Inamullah; Shan, Deshan; Li, Qiao; Jie, He
• Is Part Of: Structure and infrastructure engineering. Volume 14:Issue 2(2018)
• Page Start: 177
• Page End: 191
• Publication Date: 2018-02-01
• Subjects: Cable-stayed bridges -- intrinsic mode function -- white noise -- modal parameter identification -- mode decomposition -- Pareto approach -- cluster analysis; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2017.1338734
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: The objective of this paper was to perform an effective and meticulous continuous modal parameter identification. Since the data obtained from the cable-stayed bridge was non-linear and time varying, and also there exists a phenomenon of mode mixing in the current decomposition techniques, such as empirical mode decomposition (EMD), which further complicates the extraction of accurate structural information, therefore, a novel improved Ensemble EMD method was proposed. This method can effectively deal with the non-linear and time varying structural behaviour and eliminate the phenomenon of mode mixing effectively, specially for cable-stayed bridges, because in this method the added white noise was selected by a pre-defined process and also the intrinsic mode function (IMF) selection was made self-adaptively, then finally Pareto technique was adopted to reconstruct the IMF. After the signal decomposition and reconstruction, Recursive Stochastic Subspace Identification was employed to carry out the continuous modal parameter identification. Sutong Yangtze Bridge, a long-span cable-stayed bridge, with main span of 1088 m was taken as a case study and the proposed method was applied. The result showed that the proposed method was effective in attaining its goals and can endows better results in real life bridge health monitoring.