Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge
A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Journal of Sensor and Actuator Networks |
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| Online Access: | https://www.mdpi.com/2224-2708/9/2/27 |
| _version_ | 1827715667224166400 |
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| author | Costas Argyris Costas Papadimitriou Panagiotis Panetsos Panos Tsopelas |
| author_facet | Costas Argyris Costas Papadimitriou Panagiotis Panetsos Panos Tsopelas |
| author_sort | Costas Argyris |
| collection | DOAJ |
| description | A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors. |
| first_indexed | 2024-03-10T19:24:10Z |
| format | Article |
| id | doaj.art-c88aa23f45fb44b68825b2a5312c4cf4 |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-10T19:24:10Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-c88aa23f45fb44b68825b2a5312c4cf42023-11-20T02:43:18ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082020-06-01922710.3390/jsan9020027Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo BridgeCostas Argyris0Costas Papadimitriou1Panagiotis Panetsos2Panos Tsopelas3Department of Mechanical Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Mechanical Engineering, University of Thessaly, 38334 Volos, GreeceCapital Maintenance Department, Egnatia Odos S.A., 57001 Thermi, GreeceDepartment of Mechanics, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, 15773 Athens, GreeceA Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors.https://www.mdpi.com/2224-2708/9/2/27Bayesian inferencemodel updatingmodal identificationstructural dynamicsbridges |
| spellingShingle | Costas Argyris Costas Papadimitriou Panagiotis Panetsos Panos Tsopelas Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge Journal of Sensor and Actuator Networks Bayesian inference model updating modal identification structural dynamics bridges |
| title | Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge |
| title_full | Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge |
| title_fullStr | Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge |
| title_full_unstemmed | Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge |
| title_short | Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge |
| title_sort | bayesian model updating using features of modal data application to the metsovo bridge |
| topic | Bayesian inference model updating modal identification structural dynamics bridges |
| url | https://www.mdpi.com/2224-2708/9/2/27 |
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