Clustering and Deep Learning Techniques for Structural Health Monitoring
This thesis proposes the development and application of clustering and deep learning techniques for improved automated modal identification, lost vibration data recovery, vibration signal denoising, and dynamic response reconstruction under operational and extreme loading conditions in the area of s...
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| Format: | Thesis |
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Curtin University
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/80611 |
| _version_ | 1848764246505029632 |
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| author | Fan, Gao |
| author_facet | Fan, Gao |
| author_sort | Fan, Gao |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis proposes the development and application of clustering and deep learning techniques for improved automated modal identification, lost vibration data recovery, vibration signal denoising, and dynamic response reconstruction under operational and extreme loading conditions in the area of structural health monitoring. The effectiveness and performances of the proposed approaches are validated by numerical and experimental studies. The outstanding results demonstrate that these proposed approaches are reliable and very promising for practical applications. |
| first_indexed | 2025-11-14T11:16:18Z |
| format | Thesis |
| id | curtin-20.500.11937-80611 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:16:18Z |
| publishDate | 2020 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-806112022-08-19T01:08:43Z Clustering and Deep Learning Techniques for Structural Health Monitoring Fan, Gao This thesis proposes the development and application of clustering and deep learning techniques for improved automated modal identification, lost vibration data recovery, vibration signal denoising, and dynamic response reconstruction under operational and extreme loading conditions in the area of structural health monitoring. The effectiveness and performances of the proposed approaches are validated by numerical and experimental studies. The outstanding results demonstrate that these proposed approaches are reliable and very promising for practical applications. 2020 Thesis http://hdl.handle.net/20.500.11937/80611 Curtin University fulltext |
| spellingShingle | Fan, Gao Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title | Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title_full | Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title_fullStr | Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title_full_unstemmed | Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title_short | Clustering and Deep Learning Techniques for Structural Health Monitoring |
| title_sort | clustering and deep learning techniques for structural health monitoring |
| url | http://hdl.handle.net/20.500.11937/80611 |