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 |
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Early detection of anomalies in dam performance: a methodology based on boosted regression treesSalazar González, FernandoToledo Municio, Miguel ÁngelGonzález Lopez, Jose Manuel|||0000-0003-4490-9438Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095Structural health monitoringAnomaly detectionBoosted regression treesDam monitoringDam safetyMachine learningMonitorització de salut estructuralÀrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i presesThis 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.The advances in information and communication technologies led to a general trend towards the availability of more detailed information on dam behaviour. This allows applying advanced data-based algorithms in its analysis, which has been reflected in an increasing interest in the field. However, most of the related literature is limited to the evaluation of model prediction accuracy, whereas the ulterior objective of data analysis is dam safety assessment. In this work, a machine-learning algorithm (boosted regression trees) is the core of a methodology for early detection of anomalies. It also includes a criterion to determine whether certain discrepancy between predictions and observations is normal, a procedure to compute a realistic estimate of the model accuracy, and an original approach to identify extraordinary load combinations. The performance of causal and noncausal models is assessed in terms of their ability to detect different types of anomalies, which were artificially introduced on reference time series generated with a numerical model of a 100-m-high arch dam. The final approach was implemented in an online application to visualise the results in an intuitive way to support decision making.Peer Reviewed20172017-11-0120242024-06-03journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/409147https://dx.doi.org/10.1002/stc.2012reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4091472026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| title |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| spellingShingle |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees Salazar González, Fernando 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 |
| title_short |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| title_full |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| title_fullStr |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| title_full_unstemmed |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| title_sort |
Early detection of anomalies in dam performance: a methodology based on boosted regression trees |
| dc.creator.none.fl_str_mv |
Salazar González, Fernando Toledo Municio, Miguel Ángel González Lopez, Jose Manuel|||0000-0003-4490-9438 Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095 |
| author |
Salazar González, Fernando |
| author_facet |
Salazar González, Fernando Toledo Municio, Miguel Ángel González Lopez, Jose Manuel|||0000-0003-4490-9438 Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095 |
| author_role |
author |
| author2 |
Toledo Municio, Miguel Ángel González Lopez, Jose Manuel|||0000-0003-4490-9438 Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095 |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
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 |
| topic |
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 |
| description |
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. |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017 2017-11-01 2024 2024-06-03 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 AM http://purl.org/coar/version/c_ab4af688f83e57aa |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/409147 https://dx.doi.org/10.1002/stc.2012 |
| url |
https://hdl.handle.net/2117/409147 https://dx.doi.org/10.1002/stc.2012 |
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Inglés eng |
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Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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