A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics
Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2023.1143597/full |
| _version_ | 1827960902841794560 |
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| author | Mansureh-Sadat Nabiyan Mahdi Sharifi Hamed Ebrahimian Babak Moaveni |
| author_facet | Mansureh-Sadat Nabiyan Mahdi Sharifi Hamed Ebrahimian Babak Moaveni |
| author_sort | Mansureh-Sadat Nabiyan |
| collection | DOAJ |
| description | Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate the statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material non-linearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them. |
| first_indexed | 2024-04-09T16:17:13Z |
| format | Article |
| id | doaj.art-9b0e201ea25045a4b06cd49a13845250 |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-04-09T16:17:13Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-9b0e201ea25045a4b06cd49a138452502023-04-24T04:31:07ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622023-04-01910.3389/fbuil.2023.11435971143597A variational Bayesian inference technique for model updating of structural systems with unknown noise statisticsMansureh-Sadat Nabiyan0Mahdi Sharifi1Hamed Ebrahimian2Babak Moaveni3Department of Civil and Environmental Engineering, Tufts University, Medford, OR, United StatesGavin and Doherty Geosolutions Ltd, Dublin, IrelandDepartment of Civil and Environmental Engineering, University of Nevada, Reno, NV, United StatesDepartment of Civil and Environmental Engineering, Tufts University, Medford, OR, United StatesDynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate the statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material non-linearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them.https://www.frontiersin.org/articles/10.3389/fbuil.2023.1143597/fulladaptive Bayesian model updatingvariational Bayesian techniquenoise identificationmodel prediction errornon-stationary noise |
| spellingShingle | Mansureh-Sadat Nabiyan Mahdi Sharifi Hamed Ebrahimian Babak Moaveni A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics Frontiers in Built Environment adaptive Bayesian model updating variational Bayesian technique noise identification model prediction error non-stationary noise |
| title | A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics |
| title_full | A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics |
| title_fullStr | A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics |
| title_full_unstemmed | A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics |
| title_short | A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics |
| title_sort | variational bayesian inference technique for model updating of structural systems with unknown noise statistics |
| topic | adaptive Bayesian model updating variational Bayesian technique noise identification model prediction error non-stationary noise |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2023.1143597/full |
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