Finite element model updating with quantified uncertainties using point cloud data
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673623000072/type/journal_article |
| _version_ | 1797796847467626496 |
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| author | William Graves Ken Nahshon Kiyarash Aminfar David Lattanzi |
| author_facet | William Graves Ken Nahshon Kiyarash Aminfar David Lattanzi |
| author_sort | William Graves |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:39:15Z |
| format | Article |
| id | doaj.art-2faee4f323c5453b967537b2ebe5b611 |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-03-13T03:39:15Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-2faee4f323c5453b967537b2ebe5b6112023-06-23T10:04:55ZengCambridge University PressData-Centric Engineering2632-67362023-01-01410.1017/dce.2023.7Finite element model updating with quantified uncertainties using point cloud dataWilliam Graves0https://orcid.org/0000-0001-9094-7334Ken Nahshon1Kiyarash Aminfar2David Lattanzi3Department of Civil and Mechanical Engineering, United States Military Academy, West Point, NY, USAPlatform Integrity Department, Naval Surface Warfare Center Carderock Division, West Bethesda, MD, USASid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USASid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USAWhile 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.https://www.cambridge.org/core/product/identifier/S2632673623000072/type/journal_articleFinite element modelkrigingpoint cloudultimate strengthuncertainty quantification |
| spellingShingle | William Graves Ken Nahshon Kiyarash Aminfar David Lattanzi Finite element model updating with quantified uncertainties using point cloud data Data-Centric Engineering Finite element model kriging point cloud ultimate strength uncertainty quantification |
| title | Finite element model updating with quantified uncertainties using point cloud data |
| title_full | Finite element model updating with quantified uncertainties using point cloud data |
| title_fullStr | Finite element model updating with quantified uncertainties using point cloud data |
| title_full_unstemmed | Finite element model updating with quantified uncertainties using point cloud data |
| title_short | Finite element model updating with quantified uncertainties using point cloud data |
| title_sort | finite element model updating with quantified uncertainties using point cloud data |
| topic | Finite element model kriging point cloud ultimate strength uncertainty quantification |
| url | https://www.cambridge.org/core/product/identifier/S2632673623000072/type/journal_article |
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