Hybrid output-only structural system identification using random decrement and Kalman filter
A novel hybrid output-only structural identification and damage identification method is proposed. The method is developed by integration of Kalman filtering, as a model-based technique, and random decrement, as a data-driven technique. The random decrement method extracts free vibration from the me...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/132727 |
| _version_ | 1811097694125424640 |
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| author | Ghorbani, Esmaeil Buyukozturk, Oral Cha, Young-Jin |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Ghorbani, Esmaeil Buyukozturk, Oral Cha, Young-Jin |
| author_sort | Ghorbani, Esmaeil |
| collection | MIT |
| description | A novel hybrid output-only structural identification and damage identification method is proposed. The method is developed by integration of Kalman filtering, as a model-based technique, and random decrement, as a data-driven technique. The random decrement method extracts free vibration from the measured responses of structural system under various types of loadings. The extracted free vibration is inputted to the Kalman filtering system to estimate the status of the structural system. In contrast to the traditional output-only techniques using Kalman filter, it is not required to estimate the input excitation in the damage detection process. The Kalman filter uses only the free vibration responses extracted from the random decrement. This also leads to downsizing the size of unknown state vector, which consequently decreases computational cost significantly. Since it is not required to use any parameter related to excitations in the mathematical model, the uncertainty of the physical model decreases. The proposed approach is numerically verified in three-degrees of freedom and ten-degrees of freedom systems under three different loading conditions. It is shown that the approach is robust to provide accurate estimation of states under physical changes due to structural damage assuming the input data is unknown. As another verification, the stiffness and damping matrices of a seven-story building on a shake table are estimated to show the capability of the method for damage identification of real structures. These numerical and experimental case studies demonstrate that the proposed technique is capable of detecting, localizing, and quantifying the extent of damage in a structure under a combination of any kinds of loadings. |
| first_indexed | 2024-09-23T17:03:25Z |
| format | Article |
| id | mit-1721.1/132727 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:03:25Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1327272024-05-31T20:33:52Z Hybrid output-only structural system identification using random decrement and Kalman filter Ghorbani, Esmaeil Buyukozturk, Oral Cha, Young-Jin Massachusetts Institute of Technology. Department of Civil and Environmental Engineering A novel hybrid output-only structural identification and damage identification method is proposed. The method is developed by integration of Kalman filtering, as a model-based technique, and random decrement, as a data-driven technique. The random decrement method extracts free vibration from the measured responses of structural system under various types of loadings. The extracted free vibration is inputted to the Kalman filtering system to estimate the status of the structural system. In contrast to the traditional output-only techniques using Kalman filter, it is not required to estimate the input excitation in the damage detection process. The Kalman filter uses only the free vibration responses extracted from the random decrement. This also leads to downsizing the size of unknown state vector, which consequently decreases computational cost significantly. Since it is not required to use any parameter related to excitations in the mathematical model, the uncertainty of the physical model decreases. The proposed approach is numerically verified in three-degrees of freedom and ten-degrees of freedom systems under three different loading conditions. It is shown that the approach is robust to provide accurate estimation of states under physical changes due to structural damage assuming the input data is unknown. As another verification, the stiffness and damping matrices of a seven-story building on a shake table are estimated to show the capability of the method for damage identification of real structures. These numerical and experimental case studies demonstrate that the proposed technique is capable of detecting, localizing, and quantifying the extent of damage in a structure under a combination of any kinds of loadings. 2021-10-06T13:46:59Z 2021-10-06T13:46:59Z 2020-05 2020-05 2021-10-05T17:06:25Z Article http://purl.org/eprint/type/JournalArticle 0888-3270 https://hdl.handle.net/1721.1/132727 Esmaeil Ghorbani, Oral Buyukozturk, Young-Jin Cha, Hybrid output-only structural system identification using random decrement and Kalman filter, Mechanical Systems and Signal Processing, Volume 144, 2020 en 10.1016/J.YMSSP.2020.106977 Mechanical Systems and Signal Processing Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Other repository |
| spellingShingle | Ghorbani, Esmaeil Buyukozturk, Oral Cha, Young-Jin Hybrid output-only structural system identification using random decrement and Kalman filter |
| title | Hybrid output-only structural system identification using random decrement and Kalman filter |
| title_full | Hybrid output-only structural system identification using random decrement and Kalman filter |
| title_fullStr | Hybrid output-only structural system identification using random decrement and Kalman filter |
| title_full_unstemmed | Hybrid output-only structural system identification using random decrement and Kalman filter |
| title_short | Hybrid output-only structural system identification using random decrement and Kalman filter |
| title_sort | hybrid output only structural system identification using random decrement and kalman filter |
| url | https://hdl.handle.net/1721.1/132727 |
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