Two-stage Bayesian system identification using Gaussian discrepancy model
System identification aims at updating the model parameters (e.g., mass, stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and m...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/143231 |
| _version_ | 1824454672690184192 |
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| author | Zhang, Feng-Liang Au, Siu-Kui Ni, Yan-Chun |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Zhang, Feng-Liang Au, Siu-Kui Ni, Yan-Chun |
| author_sort | Zhang, Feng-Liang |
| collection | NTU |
| description | System identification aims at updating the model parameters (e.g., mass, stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes' rule without using a heuristic model (often based on classical statistics) that transfers the information from Stage I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this paper investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure. |
| first_indexed | 2025-02-19T03:26:02Z |
| format | Journal Article |
| id | ntu-10356/143231 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:26:02Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1432312020-08-14T03:19:32Z Two-stage Bayesian system identification using Gaussian discrepancy model Zhang, Feng-Liang Au, Siu-Kui Ni, Yan-Chun School of Civil and Environmental Engineering Institute of Catastrophe Risk Management (ICRM) Engineering::Civil engineering System Identification Two-stage Formulation System identification aims at updating the model parameters (e.g., mass, stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes' rule without using a heuristic model (often based on classical statistics) that transfers the information from Stage I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this paper investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure. Nanyang Technological University Accepted version This paper is funded by the National Natural Science Foundation of China (Grant No.: 51878484; F.-L.Z.), Natural Science Foundation of Shenzhen (Grant No.: JCYJ20190806143618723; F.-L.Z.), a start-up grant SUG/4 (S.-K.A.) from Nanyang Technological University, Singapore, and Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No.: 2019 EEEVL0401; Y.-C.N.). The financial support is gratefully acknowledged. 2020-08-14T03:19:32Z 2020-08-14T03:19:32Z 2020 Journal Article Zhang, F.-L., Au, S.-K., & Ni, Y.-C. (2020). Two-stage Bayesian system identification using Gaussian discrepancy. Structural Health Monitoring. doi: 10.1177/1475921720933523 1741-3168 https://hdl.handle.net/10356/143231 10.1177/1475921720933523 en 51878484 JCYJ20190806143618723 M4082398 2019 EEEVL0401 Structural Health Monitoring © 2020 SAGE Publications. All rights reserved. This paper was published in Structural Health Monitoring and is made available with permission of SAGE Publications. application/pdf |
| spellingShingle | Engineering::Civil engineering System Identification Two-stage Formulation Zhang, Feng-Liang Au, Siu-Kui Ni, Yan-Chun Two-stage Bayesian system identification using Gaussian discrepancy model |
| title | Two-stage Bayesian system identification using Gaussian discrepancy model |
| title_full | Two-stage Bayesian system identification using Gaussian discrepancy model |
| title_fullStr | Two-stage Bayesian system identification using Gaussian discrepancy model |
| title_full_unstemmed | Two-stage Bayesian system identification using Gaussian discrepancy model |
| title_short | Two-stage Bayesian system identification using Gaussian discrepancy model |
| title_sort | two stage bayesian system identification using gaussian discrepancy model |
| topic | Engineering::Civil engineering System Identification Two-stage Formulation |
| url | https://hdl.handle.net/10356/143231 |
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