Advanced Deep Learning Methods for Vibration-based Structural Damage Identification
dc.contributor.author | Wang, Ruhua | |
dc.contributor.supervisor | Senjian An | en_US |
dc.contributor.supervisor | Ling Li | en_US |
dc.contributor.supervisor | Jun Li | en_US |
dc.date.accessioned | 2021-11-17T07:23:58Z | |
dc.date.available | 2021-11-17T07:23:58Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/86446 | |
dc.description.abstract |
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. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Advanced Deep Learning Methods for Vibration-based Structural Damage Identification | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |
curtin.contributor.orcid | Wang, Ruhua [0000-0001-5798-8118] | en_US |