Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring
| dc.contributor.author | Peng, Zhen | |
| dc.contributor.supervisor | Jun Li | en_US |
| dc.contributor.supervisor | Hong Hao | en_US |
| dc.date.accessioned | 2022-07-29T06:40:11Z | |
| dc.date.available | 2022-07-29T06:40:11Z | |
| dc.date.issued | 2022 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/89064 | |
| dc.description.abstract |
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. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Novel Data Analytics for Developing Sensitive and Reliable Damage Indicators in Structural Health Monitoring | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Civil and Mechanical Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Peng, Zhen [0000-0001-9352-9613] | en_US |