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dc.contributor.authorSalazar González, Fernando
dc.contributor.authorToledo Municio, Miguel Ángel
dc.contributor.authorOñate Ibáñez de Navarra, Eugenio
dc.contributor.authorSuárez Arroyo, Benjamín
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.date.accessioned2016-06-01T14:49:11Z
dc.date.available2018-08-01T00:30:40Z
dc.date.issued2016-07
dc.identifier.citationSalazar, F., Toledo, M. A., Oñate, E., Suarez, B. Interpretation of dam deformation and leakage with boosted regression trees. "Engineering structures", Juliol 2016, vol. 119, p. 230-251.
dc.identifier.issn0141-0296
dc.identifier.urihttp://hdl.handle.net/2117/87614
dc.description.abstractPredictive models are essential in dam safety assessment. They have been traditionally based on simple statistical tools such as the hydrostatic-season-time (HST) model. These tools are well known to have limitations in terms of accuracy and reliability. In the recent years, the examples of application of machine learning and related techniques are becoming more frequent as an alternative to HST. While they proved to feature higher flexibility and prediction accuracy, they are also more difficult to interpret. As a consequence, the vast majority of the research is limited to prediction accuracy estimation. In this work, one of the most popular machine learning techniques (boosted regression trees), was applied to model 8 radial displacements and 4 leakage flows at La Baells Dam. The possibilities of model interpretation were explored: the relative influence of each predictor was computed, and the partial dependence plots were obtained. Both results were analysed to draw conclusions on dam response to environmental variables, and its evolution over time. The results show that this technique can efficiently identify dam performance changes with higher flexibility and reliability than simple regression models.
dc.format.extent22 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
dc.subject.lcshDam safety
dc.subject.otherMachine learning
dc.subject.otherDam safety
dc.subject.otherDam monitoring
dc.subject.otherBoosted regression trees
dc.titleInterpretation of dam deformation and leakage with boosted regression trees
dc.typeArticle
dc.subject.lemacPreses (Enginyeria) -- Mesures de seguretat
dc.contributor.groupUniversitat Politècnica de Catalunya. GMNE - Grup de Mètodes Numèrics en Enginyeria
dc.contributor.groupUniversitat Politècnica de Catalunya. RMEE - Grup de Resistència de Materials i Estructures en l'Enginyeria
dc.identifier.doi10.1016/j.engstruct.2016.04.012
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0141029616301237
dc.rights.accessOpen Access
local.identifier.drac18544980
dc.description.versionPostprint (author's final draft)
local.citation.authorSalazar, F.; Toledo, M. A.; Oñate, E.; Suarez, B.
local.citation.publicationNameEngineering structures
local.citation.volume119
local.citation.startingPage230
local.citation.endingPage251


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