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Machine learning algorithms for the prediction of the mechanical properties of railways’ rail pads

2021-01-27Journal article Open access
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dc.contributor.authorFerreño, D.
dc.contributor.authorSainz-Aja, J. A.
dc.contributor.authorCarrascal, I. A.
dc.contributor.authorCuartas, M.
dc.contributor.authorPombo, João
dc.contributor.authorCasado, J. A.
dc.contributor.authorDiego, S.
dc.date.accessioned2021-06-23T12:26:31Z
dc.date.available2021-06-23T12:26:31Z
dc.date.issued2021-01-27
dc.description.abstractTrain operations generate high impact and fatigue loads that degrade the rail infrastructure and vehicle components. Rail pads are installed between the rails and the sleepers to damp the transmission of vibrations and noise and to provide flexibility to the track. These components play a crucial role to maximize the durability of railway assets and to minimize the maintenance costs. The non-linear mechanical response of this type of materials make it extremely difficult to estimate their mechanical properties, such as the dynamic stiffness. In this work, several machine learning algorithms were used to determine the dynamic stiffness of pads depending on their in-service conditions (temperature, frequency, axle-load and toe-load). 720 experimental tests were performed under different realistic operating conditions; this information was used for the training, validation and testing of the algorithms. It was observed that the optimal algorithm was gradient boosting for EPDM (R2 of 0.995 and mean absolute percentage error of 5.08% in test dataset), TPE (0.994 and 2.32%) and EVA (0.968 and 4.91%) pads. This algorithm was implemented in an application, developed on Microsoft .Net platform, that provides the dynamic stiffness of the pads characterized in this study as function of material, temperature, frequency, axle-load and toe-load.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFERREÑO, D.; [et al] – Machine learning algorithms for the prediction of the mechanical properties of railways’ rail pads. Journal of Physics: Conference Series (2nd International Conference on Graphene and Novel Nanomaterials (GNN) 2020). ISSN 1742-6588. Vol. 1765 (2021), pp. 1-7pt_PT
dc.identifier.doi10.1088/1742-6596/1765/1/012008pt_PT
dc.identifier.eissn1742-6596
dc.identifier.issn1742-6588
dc.identifier.urihttp://hdl.handle.net/10400.21/13471
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIOP Publishingpt_PT
dc.relation.publisherversionhttps://iopscience.iop.org/article/10.1088/1742-6596/1765/1/012008/pdfpt_PT
dc.subjectTrain operationspt_PT
dc.subjectHigh impactpt_PT
dc.subjectFatigue loadspt_PT
dc.subjectRail infrastructurept_PT
dc.subjectVehicle componentspt_PT
dc.subjectRail padspt_PT
dc.subjectRailway assetspt_PT
dc.titleMachine learning algorithms for the prediction of the mechanical properties of railways’ rail padspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJournal of Physics: Conference Seriespt_PT
oaire.citation.volume1765pt_PT
person.familyNamePombo
person.givenNameJoão
person.identifier.ciencia-id3E1A-F8C1-9B8D
person.identifier.orcid0000-0002-5877-1989
person.identifier.ridB-9140-2013
person.identifier.scopus-author-id16022941000
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication4b7bfa25-4662-4995-8146-4b45c39ffc53
relation.isAuthorOfPublication.latestForDiscovery4b7bfa25-4662-4995-8146-4b45c39ffc53

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