Early detection of anomalies in dam performance: a methodology based on boosted regression trees
This is the peer reviewed version of the following article: Salazar F, Toledo MÁ, González JM, Oñate E. Early detection of anomalies in dam performance: A methodology based on boosted regression trees. Struct Control Health Monit. 2017; 24:e2012. https://doi.org/10.1002/stc.2012 , which has been pub...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/409147 |
| Acceso en línea: | https://hdl.handle.net/2117/409147 https://dx.doi.org/10.1002/stc.2012 |
| Access Level: | acceso abierto |
| Palabra clave: | Structural health monitoring Anomaly detection Boosted regression trees Dam monitoring Dam safety Machine learning Monitorització de salut estructural Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses |
| Sumario: | This is the peer reviewed version of the following article: Salazar F, Toledo MÁ, González JM, Oñate E. Early detection of anomalies in dam performance: A methodology based on boosted regression trees. Struct Control Health Monit. 2017; 24:e2012. https://doi.org/10.1002/stc.2012 , which has been published in final form at https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2012. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
|---|