Optimized finite element analysis model based on terrestrial laser scanning data

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  • Jiangsu University of Science and Technology
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Original languageEnglish
Pages (from-to)62-71
Number of pages10
JournalComposite structures
Volume207
Early online date15 Sept 2018
Publication statusPublished - 1 Jan 2019

Abstract

The problem of the arch loading case is studied by the combination of finite element analysis (FEA) and B-Splines, which provides a highly accurate solution to the calibration problem of the FEA model. Attention is paid to the assessment of FEA by a high-accuracy two-dimensional B-Spline model using point clouds from terrestrial laser scanning (TLS) which is one of the most accurate measurement tools. In the current paper, a B-Spline-based optimized model, comparing to an idealized model, is applied in the FEA. Meanwhile, characteristics of arched nodal displacement and stress are discussed. The target function of the optimization is to reduce the displacement error. It is revealed that the B-Spline optimized model based on TLS data with high-accuracy could carry out FEA efficiently with fewer displacement errors, which is an important part of the automatic calibration of FEA. Finally, the equivalent stress in the post-computation is applied to predict the possible crack regions and carry out the damage monitoring.

Keywords

    Automatic calibration, B-Splines, Finite element analysis, Optimized model, Terrestrial laser scanning

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Optimized finite element analysis model based on terrestrial laser scanning data. / Xu, Wei; Xu, Xiangyang; Yang, Hao et al.
In: Composite structures, Vol. 207, 01.01.2019, p. 62-71.

Research output: Contribution to journalArticleResearchpeer review

Xu W, Xu X, Yang H, Neumann I. Optimized finite element analysis model based on terrestrial laser scanning data. Composite structures. 2019 Jan 1;207:62-71. Epub 2018 Sept 15. doi: 10.1016/j.compstruct.2018.09.006
Xu, Wei ; Xu, Xiangyang ; Yang, Hao et al. / Optimized finite element analysis model based on terrestrial laser scanning data. In: Composite structures. 2019 ; Vol. 207. pp. 62-71.
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