Advanced Deep Learning Methods for Vibration-based Structural Damage Identification
Vibration-based damage identification has been a challenging task in structural health monitoring. The main difficulty lies on the reliable correlation between the measured vibration characteristics and the damage states of structures. However, the measured vibration signals are often high-dimensio...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/86446 |
| _version_ | 1848764824550375424 |
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| author | Wang, Ruhua |
| author_facet | Wang, Ruhua |
| author_sort | Wang, Ruhua |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Vibration-based damage identification has been a challenging task in structural health monitoring. The main difficulty lies on the reliable correlation between the measured vibration characteristics and the damage states of structures. However, the measured vibration signals are often high-dimensional and noise-contaminated, and sometimes in multiple scales or have multiple physical meanings. In this thesis, we propose advanced deep learning models for effective and efficient structural damage identification. |
| first_indexed | 2025-11-14T11:25:30Z |
| format | Thesis |
| id | curtin-20.500.11937-86446 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:25:30Z |
| publishDate | 2021 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-864462023-11-21T00:45:49Z Advanced Deep Learning Methods for Vibration-based Structural Damage Identification Wang, Ruhua Vibration-based damage identification has been a challenging task in structural health monitoring. The main difficulty lies on the reliable correlation between the measured vibration characteristics and the damage states of structures. However, the measured vibration signals are often high-dimensional and noise-contaminated, and sometimes in multiple scales or have multiple physical meanings. In this thesis, we propose advanced deep learning models for effective and efficient structural damage identification. 2021 Thesis http://hdl.handle.net/20.500.11937/86446 Curtin University fulltext |
| spellingShingle | Wang, Ruhua Advanced Deep Learning Methods for Vibration-based Structural Damage Identification |
| title | Advanced Deep Learning Methods for Vibration-based Structural
Damage Identification |
| title_full | Advanced Deep Learning Methods for Vibration-based Structural
Damage Identification |
| title_fullStr | Advanced Deep Learning Methods for Vibration-based Structural
Damage Identification |
| title_full_unstemmed | Advanced Deep Learning Methods for Vibration-based Structural
Damage Identification |
| title_short | Advanced Deep Learning Methods for Vibration-based Structural
Damage Identification |
| title_sort | advanced deep learning methods for vibration-based structural
damage identification |
| url | http://hdl.handle.net/20.500.11937/86446 |