System reduction-based approximate reanalysis method for statically indeterminate structures with high-rank modification

Li, Wenxiong; Chen, Suiyin; Huang, Huan

doi:10.1016/j.istruc.2023.06.063

Cited by 4 publications

(1 citation statement)

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“…Like other conventional model updating methods, FRF-based model updating may encounter the issue of computational efficiency due to reusing the initial numerical model in the iterative optimization mechanism. Structural reanalysis theories aim to achieve fast calculation by utilizing initial structural information once and calculating subsequent responses after structural modification (Li et al, 2023). Therefore, this paper combines structural reanalysis theories with model updating to avoid…”

Section: Smw Formulasmentioning

confidence: 99%

A two-stage model updating method for the linear parts of structures with local nonlinearities

Zhang,

Wei,

Zhai

et al. 2023

Front. Mater.

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Finite element model updating provides an important supplement for finite element modelling. However, some studies have shown that if the tested structure involves local nonlinearities due to damages, material properties and large deformation et al., it is difficult to achieve an accurate modified model using conventional model updating methods that are based on the assumption of linear structures. To address this issue, a two-stage model updating method separating the effects of local nonlinearities is proposed in this paper. Firstly, the underlying linear frequency response function is obtained by using the conditioned reverse path method. Then, combined with the Sherman-Morrison-Woodbury formula and the model updating objective function established by the frequency response function similarity metric, then structural model updating and damage detection are carried out as the second stage. Three numerical examples are given to illustrate the effectiveness of the proposed method. This method can not only accurately identify the location and quantify the extent of structural damages, but also has the advantages of not based on sensitivity, not depending on the selection of frequency points, not repeatedly calling the initial model et al. The proposed method has high computational efficiency and avoids the numerical problems often encountered by conventional frequency response function-based model updating methods.

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Section: Smw Formulasmentioning

confidence: 99%