Efficient Supervised Machine Learning Techniques for Structural Health Monitoring
This thesis presents supervised machine learning techniques using acceleration responses recorded from a small number of sensors. Ensemble-based traditional machine learning models are developed as a multi output regression model for the damage identification of the civil engineering structures usin...
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| Format: | Thesis |
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Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89294 |
| _version_ | 1848765197731233792 |
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| author | Chencho |
| author_facet | Chencho |
| author_sort | Chencho |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis presents supervised machine learning techniques using acceleration responses recorded from a small number of sensors. Ensemble-based traditional machine learning models are developed as a multi output regression model for the damage identification of the civil engineering structures using acceleration responses and impulse response functions extracted from it. Further, to improve the damage identification performance, a LSTM auto-encoder based multi output regression model is proposed. Finally, for a large-scale bridge, a 1D-CNN based damage classifier is developed using less number of sensors than the existing study. |
| first_indexed | 2025-11-14T11:31:26Z |
| format | Thesis |
| id | curtin-20.500.11937-89294 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:31:26Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-892942024-11-12T02:05:57Z Efficient Supervised Machine Learning Techniques for Structural Health Monitoring Chencho This thesis presents supervised machine learning techniques using acceleration responses recorded from a small number of sensors. Ensemble-based traditional machine learning models are developed as a multi output regression model for the damage identification of the civil engineering structures using acceleration responses and impulse response functions extracted from it. Further, to improve the damage identification performance, a LSTM auto-encoder based multi output regression model is proposed. Finally, for a large-scale bridge, a 1D-CNN based damage classifier is developed using less number of sensors than the existing study. 2022 Thesis http://hdl.handle.net/20.500.11937/89294 Curtin University fulltext |
| spellingShingle | Chencho Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title | Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title_full | Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title_fullStr | Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title_full_unstemmed | Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title_short | Efficient Supervised Machine Learning Techniques for Structural Health Monitoring |
| title_sort | efficient supervised machine learning techniques for structural health monitoring |
| url | http://hdl.handle.net/20.500.11937/89294 |