Large-scale Pavement Crack Evaluation and Prediction using a Novel Spatial Machine Learning Approach
| dc.contributor.author | Chen, Chunjiang | |
| dc.contributor.supervisor | Peng Wu | en_US |
| dc.contributor.supervisor | Yongze Song | en_US |
| dc.date.accessioned | 2025-05-09T00:33:04Z | |
| dc.date.available | 2025-05-09T00:33:04Z | |
| dc.date.issued | 2025 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/97702 | |
| dc.description.abstract |
This study introduces a geocomplexity-enhanced machine learning (GML) model that integrates spatial methodologies to uncover influencing factors of crack severity obtained from human inspection and laser scanning. These two aspects, representing existing surface crack condition, are then integrated with a risk of deterioration to develop a comprehensive crack evaluation framework. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Large-scale Pavement Crack Evaluation and Prediction using a Novel Spatial Machine Learning Approach | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Design and the Built Environment | en_US |
| curtin.accessStatus | Fulltext not available | en_US |
| curtin.faculty | Humanities | en_US |
| curtin.contributor.orcid | Chen, Chunjiang [0000-0002-9244-2257] | en_US |
| dc.date.embargoEnd | 2027-04-29 |