Finite element model updating with quantified uncertainties using point cloud data

被引:1
作者
Graves, William [1 ]
Nahshon, Ken [2 ]
Aminfar, Kiyarash [3 ]
Lattanzi, David [3 ]
机构
[1] US Mil Acad, Dept Civil & Mech Engn, West Point, NY 10996 USA
[2] Naval Surface Warfare Ctr Carderock Div, Platform Integr Dept, West Bethesda, MD USA
[3] George Mason Univ, Sid & Reva Dewberry Dept Civil Environm & Infrastr, Fairfax, VA USA
来源
DATA-CENTRIC ENGINEERING | 2023年 / 4卷 / 04期
关键词
Finite element model; kriging; point cloud; ultimate strength; uncertainty quantification; ULTIMATE STRENGTH; LASER SCANNER; COMBINATION; PREDICTION; DEPTH;
D O I
10.1017/dce.2023.7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
While finite element (FE) modeling is widely used for ultimate strength assessments of structural systems, incorporating complex distortions and imperfections into FE models remains a challenge. Conventional methods typically rely on assumptions about the periodicity of distortions through spectral or modal methods. However, these approaches are not viable under the many realistic scenarios where these assumptions are invalid. Research efforts have consistently demonstrated the ability of point cloud data, generated through laser scanning or photogrammetry-based methods, to accurately capture structural deformations at the millimeter scale. This enables the updating of numerical models to capture the exact structural configuration and initial imperfections without the need for unrealistic assumptions. This research article investigates the use of point cloud data for updating the initial distortions in a FE model of a stiffened ship deck panel, for the purposes of ultimate strength estimation. The presented approach has the additional benefit of being able to explicitly account for measurement uncertainty in the analysis. Calculations using the updated FE models are compared against ground truth test data as well as FE models updated using standard spectral methods. The results demonstrate strength estimation that is comparable to existing approaches, with the additional advantages of uncertainty quantification and applicability to a wider range of application scenarios.
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页数:19
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