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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...

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Detalles Bibliográficos
Autores principales: Chen, Hui, Huang, Bin, Tee, Kong Fah, Lu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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