Repository logo
 
Publication

Prediction of mechanical properties of rail pads under in-service conditions through machine learning algorithms

2021-01Journal article Restricted access
Simple item page
dc.contributor.authorFerreño, Diego
dc.contributor.authorSainz-Aja, Jose Adolfo
dc.contributor.authorCarrascal, Isidro
dc.contributor.authorCuartas, Miguel
dc.contributor.authorPombo, João
dc.contributor.authorCasado, José A.
dc.contributor.authorDIEGO, SORAYA
dc.date.accessioned2020-11-06T18:42:26Z
dc.date.available2020-11-06T18:42:26Z
dc.date.issued2021-01
dc.description.abstractTrain operations generate high impact and fatigue loads that degrade the rail infrastructure and the vehicle components. Rail pads are installed between the rails and the sleepers in order to damp the transmission of vibrations and noise and to provide flexibility to the track. These components play a crucial role in maximizing the durability of the railway assets and minimizing maintenance costs. Rail pads can be fabricated with different polymeric materials that exhibit non-linear mechanical behaviours, which strongly depend on the service conditions. Therefore, it is extremely difficult to estimate their mechanical properties, in particular the dynamic stiffness. In this work, several machine learning methodologies (multilinear regression, K nearest neighbours, regression tree, random forest, gradient boosting, multi-layer perceptron and support vector machine) were used to determine the dynamic stiffness of rail pads depending on their in-service conditions (temperature, frequency, axle load and toe load). 720 experimental tests, under different realistic operating conditions, were performed to produce a dataset that was then used for the training and testing of the machine learning methods. The optimal algorithm was gradient boosting for EPDM (R2 of 0.995 and mean absolute percentage error of 5.08% in the test dataset), TPE (0.994 and 2.32%) and EVA (0.968 and 4.91%) pads. This model was implemented in an application, available for the readers of this journal, developed on the Microsoft .Net platform that allows the dynamic stiffness of the pads study to be estimated as a function of the temperature, frequency, axle load and toe load.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFERRENO, Diego; [et al] – Prediction of mechanical properties of rail pads under in-service conditions through machine learning algorithms. Advances in Engineering Software. ISSN 0965-9978. Vol. 151 (2021), pp. 1-11pt_PT
dc.identifier.doi10.1016/j.advengsoft.2020.102927pt_PT
dc.identifier.issn0965-9978
dc.identifier.urihttp://hdl.handle.net/10400.21/12356
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationUIDB/50022/2020 - FCTpt_PT
dc.relation.publisherversionhttps://reader.elsevier.com/reader/sd/pii/S096599782030973X?token=49F665F4809814B265648D6892BC41A63A4A864F926B8F447AB47FDE387F3641F5F4DAC895518E52849B32F95A4EF68Ept_PT
dc.subjectRailway dynamicspt_PT
dc.subjectSleeper padspt_PT
dc.subjectMachine learningpt_PT
dc.subjectRail service conditionspt_PT
dc.subjectDynamic stiffnesspt_PT
dc.titlePrediction of mechanical properties of rail pads under in-service conditions through machine learning algorithmspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage11pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAdvances in Engineering Softwarept_PT
oaire.citation.volume151pt_PT
person.familyNameFerreño
person.familyNameSainz-Aja
person.familyNameCarrascal
person.familyNamePombo
person.familyNameDIEGO
person.givenNameDiego
person.givenNameJose Adolfo
person.givenNameIsidro
person.givenNameJoão
person.givenNameSORAYA
person.identifier.ciencia-id3E1A-F8C1-9B8D
person.identifier.orcid0000-0003-3533-1881
person.identifier.orcid0000-0003-3187-4790
person.identifier.orcid0000-0002-7045-1267
person.identifier.orcid0000-0002-5877-1989
person.identifier.orcid0000-0003-4518-7449
person.identifier.ridB-9140-2013
person.identifier.scopus-author-id57190956707
person.identifier.scopus-author-id9239704200
person.identifier.scopus-author-id16022941000
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication715a8c0e-a5a7-4a97-9f3d-9eb7bd09104c
relation.isAuthorOfPublication45b37d03-a7ab-48b7-accb-1ee53b5dca62
relation.isAuthorOfPublicationa969d61f-5919-4a88-869b-8d7b88415bd7
relation.isAuthorOfPublication4b7bfa25-4662-4995-8146-4b45c39ffc53
relation.isAuthorOfPublication7f15fd5b-eaa9-4604-95e9-f61fa4070176
relation.isAuthorOfPublication.latestForDiscoverya969d61f-5919-4a88-869b-8d7b88415bd7

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Prediction_JPombo.pdf
Size:
5.69 MB
Format:
Adobe Portable Document Format
Download

Collections

ISEL - Eng. Mecan. - Artigos