A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Partially Supervised Learning for Data-Driven Structural Health Monitoring
The cost of labelling data by engineer inspections remains a significant issue for performance and health monitoring. In many cases, this is because the actual data annotation process is expensive (e.g. non-destructive testing) or it is simply infeasible to label all the measurements (e.g. lack of access). Often, however, it is possible to provide a small number of budget-restricted labels, to describe the measurements. In these scenarios, methods for partially supervised learning are proposed. Active learning, semi-supervised learning, and transfer learning are summarised here—demonstrated with simulated monitoring examples. Each family of algorithms is shown to significantly improve conventional methods for data-driven monitoring.
Partially Supervised Learning for Data-Driven Structural Health Monitoring
The cost of labelling data by engineer inspections remains a significant issue for performance and health monitoring. In many cases, this is because the actual data annotation process is expensive (e.g. non-destructive testing) or it is simply infeasible to label all the measurements (e.g. lack of access). Often, however, it is possible to provide a small number of budget-restricted labels, to describe the measurements. In these scenarios, methods for partially supervised learning are proposed. Active learning, semi-supervised learning, and transfer learning are summarised here—demonstrated with simulated monitoring examples. Each family of algorithms is shown to significantly improve conventional methods for data-driven monitoring.
Partially Supervised Learning for Data-Driven Structural Health Monitoring
Structural Integrity
Cury, Alexandre (editor) / Ribeiro, Diogo (editor) / Ubertini, Filippo (editor) / Todd, Michael D. (editor) / Bull, Lawrence A. (author) / Hughes, A. J. (author) / Rogers, T. J. (author) / Gardner, Paul (author) / Worden, Keith (author) / Dervilis, Nikolaos (author)
Structural Health Monitoring Based on Data Science Techniques ; Chapter: 19 ; 389-411
Structural Integrity ; 21
2021-10-24
23 pages
Article/Chapter (Book)
Electronic Resource
English
Partially supervised learning , Active learning , Semi-supervised learning , Transfer learning , Structural health monitoring , Prognostics and health management , Condition monitoring Computer Science , Data Structures and Information Theory , Artificial Intelligence , Machine Learning , Statistics, general , Engineering
| British Library Conference Proceedings | 2013