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A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique
This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126121/ https://www.ncbi.nlm.nih.gov/pubmed/34068637 http://dx.doi.org/10.3390/s21093290 |
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author | Chen, Hui Huang, Bin Tee, Kong Fah Lu, Bo |
author_facet | Chen, Hui Huang, Bin Tee, Kong Fah Lu, Bo |
author_sort | Chen, Hui |
collection | PubMed |
description | This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance. |
format | Online Article Text |
id | pubmed-8126121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81261212021-05-17 A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique Chen, Hui Huang, Bin Tee, Kong Fah Lu, Bo Sensors (Basel) Article This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance. MDPI 2021-05-10 /pmc/articles/PMC8126121/ /pubmed/34068637 http://dx.doi.org/10.3390/s21093290 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Hui Huang, Bin Tee, Kong Fah Lu, Bo A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title | A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title_full | A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title_fullStr | A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title_full_unstemmed | A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title_short | A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique |
title_sort | new stochastic model updating method based on improved cross-model cross-mode technique |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126121/ https://www.ncbi.nlm.nih.gov/pubmed/34068637 http://dx.doi.org/10.3390/s21093290 |
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