Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring
This thesis focuses on developing novel data analytics and damage detection methods that are applicable to the condition assessment of civil engineering structures subjected to operational and environmental condition changes, nonlinearity and/or measurement noise. Comprehensive numerical and experim...
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| Format: | Thesis |
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Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89064 |
| _version_ | 1848765152423313408 |
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| author | Peng, Zhen |
| author_facet | Peng, Zhen |
| author_sort | Peng, Zhen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis focuses on developing novel data analytics and damage detection methods that are applicable to the condition assessment of civil engineering structures subjected to operational and environmental condition changes, nonlinearity and/or measurement noise. Comprehensive numerical and experimental studies validate the effectiveness and performance of using the proposed approaches for practical structural health monitoring applications. |
| first_indexed | 2025-11-14T11:30:42Z |
| format | Thesis |
| id | curtin-20.500.11937-89064 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:30:42Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-890642024-07-31T05:45:58Z Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring Peng, Zhen This thesis focuses on developing novel data analytics and damage detection methods that are applicable to the condition assessment of civil engineering structures subjected to operational and environmental condition changes, nonlinearity and/or measurement noise. Comprehensive numerical and experimental studies validate the effectiveness and performance of using the proposed approaches for practical structural health monitoring applications. 2022 Thesis http://hdl.handle.net/20.500.11937/89064 Curtin University fulltext |
| spellingShingle | Peng, Zhen Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title_full | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title_fullStr | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title_full_unstemmed | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title_short | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring |
| title_sort | novel data analytics for developing sensitive and reliable damage indicators in structural health monitoring |
| url | http://hdl.handle.net/20.500.11937/89064 |