Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/66445

Registo completo
Campo DCValorIdioma
dc.contributor.authorTinoco, Joaquim Agostinho Barbosapor
dc.contributor.authorde Granrut, Mathildepor
dc.contributor.authorDias, Danielpor
dc.contributor.authorMiranda, Tiago F. S.por
dc.contributor.authorSimon, Alexandre-Gillespor
dc.date.accessioned2020-08-12T18:13:13Z-
dc.date.issued2020-
dc.identifier.issn0941-0643por
dc.identifier.urihttps://hdl.handle.net/1822/66445-
dc.description.abstractThe safety assessment of dams is a complex task that is made possible thanks to a constant monitoring of pertinent parameters. Once collected, the data are processed by statistical analysis models in order to describe the behaviour of the structure. The aim of those models is to detect early signs of abnormal behaviour so as to take corrective actions when required. Because of the uniqueness of each structure, the behavioural models need to adapt to each of these structures, and thus flexibility is required. Simultaneously, generalization capacities are sought, so a trade-off has to be found. This flexibility is even more important when the analysed phenomenon is characterized by nonlinear features. This is notably the case of the piezometric levels (PL) monitored at the rock-concrete interface of arch dams, when this interface opens. In that case, the linear models that are classically used by engineers show poor performances. Consequently, interest naturally grows for the advanced learning algorithms known as machine learning techniques. In this work, the aim was to compare the predictive performances and generalization capacities of six different data mining algorithms that are likely to be used for monitoring purposes in the particular case of the piezometry at the interface of arch dams: artificial neural networks (ANN), support vector machines (SVM), decision tree, k-nearest neighbour, random forest and multiple regression. All six are used to analyse the same time series. The interpretation of those PL permits to understand the phenomenon of the aperture of the interface, which is highly nonlinear, and of great concern in dam safety. The achieved results show that SVM and ANN stand out as the most efficient algorithms, when it comes to analysing nonlinear monitored phenomenon. Through a global sensitivity analysis, the influence of the models' attributes is measured, showing a high impact of Z (relative trough) in PL prediction.por
dc.description.sponsorshipThe authors thank the ANRT CIFRE for itsGrant (No. 0902/2016) that partly supported this work.por
dc.language.isoengpor
dc.publisherSpringerpor
dc.rightsrestrictedAccesspor
dc.subjectDam monitoringpor
dc.subjectConcrete damspor
dc.subjectPiezometric levelspor
dc.subjectData mining techniquespor
dc.titlePiezometric level prediction based on data mining techniquespor
dc.typearticle-
dc.peerreviewedyespor
oaire.citationStartPage4009por
oaire.citationEndPage4024por
oaire.citationIssue8por
oaire.citationVolume32por
dc.date.updated2020-08-12T16:52:13Z-
dc.identifier.doi10.1007/s00521-019-04392-6por
dc.date.embargo10000-01-01-
dc.subject.wosScience & Technology-
sdum.export.identifier5889-
sdum.journalNeural Computing and Applicationspor
Aparece nas coleções:ISISE - Artigos em Revistas Internacionais

Ficheiros deste registo:
Ficheiro Descrição TamanhoFormato 
Piezometric level prediction based on data mining techniques.pdf
Acesso restrito!
1,03 MBAdobe PDFVer/Abrir

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID