Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/84786

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dc.contributor.authorMorais, Maria José Cruzpor
dc.contributor.authorSousa, Hélder S.por
dc.contributor.authorMatos, José C.por
dc.date.accessioned2023-05-30T09:31:28Z-
dc.date.issued2021-
dc.identifier.citationMorais M.J., Sousa H.S., Matos J.C. (2021). An overview of performance predictive models for railway track assets in Europe. In: Matos, J.C. et al. (eds) Lecture notes in civil engineering, 153, Springer, Cham, pp. 149-163 (18th International Probabilistic Workshop, Guimarães, Portugal, 12-14 May 2021) (doi.org/ 10.1007/978-3-030-73616-3_11).por
dc.identifier.isbn9783030736156por
dc.identifier.issn2366-2557por
dc.identifier.urihttps://hdl.handle.net/1822/84786-
dc.description.abstractA railway system degrades over time due to several factors such as aging, traffic conditions, usage, environmental conditions, natural and man-made hazards. Moreover, the lack or inadequate maintenance and restoration works may also contribute to the degradation process. In this aspect it is important to understand the performance of transportation infrastructures, the variables influencing its degradation, as well as the necessary actions to minimize the degradation process over time, improve the security of the users, minimize the environment impact as well as the associated costs. Thus, it is crucial to follow structured maintenance plans during the life cycle of the infrastructure supported by the forecasting of the degradation over time. This paper presents a brief description of the variables influencing the degradation of a rail-way system, and the way the performance of the railway track can be measured, within a probabilistic environment. The work developed in other transportation infrastructures, like roadway, is briefly presented for comparison purposes and benchmarking. It also presents an overview of the predictive models being used in railway systems, from the mechanistic to the data-driven models, where the statistical and artificial intelligence models are included.por
dc.description.sponsorshipFCT -Fundação para a Ciência e a Tecnologia(POCI-01-0145-FEDER-007633)por
dc.language.isoengpor
dc.publisherSpringer, Champor
dc.relationFCT POCI-01-0145-FEDER-007633por
dc.relationEU Shift2Rail - IN2TRACK2–826255-H2020-S2RJU-2018/H2020-S2RJU CFM-2018por
dc.rightsrestrictedAccesspor
dc.subjectPredictive modelspor
dc.subjectRailwaypor
dc.subjectPerformance indicatorspor
dc.subjectProbabilistic assessmentpor
dc.titleAn overview of performance predictive models for railway track assets in Europepor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionThe original publication is available at Springer: https://link.springer.com/chapter/10.1007/978-3-030-73616-3_11por
oaire.citationConferenceDate12 Mai. - 14 Mai. 2021por
sdum.event.title18th International Probabilistic Workshop IPW 2020por
sdum.event.typeworkshoppor
oaire.citationStartPage149por
oaire.citationEndPage163por
oaire.citationConferencePlaceGuimarães, Portugalpor
oaire.citationVolume153 LNCEpor
dc.identifier.doi10.1007/978-3-030-73616-3_11por
dc.date.embargo10000-01-01-
dc.subject.fosEngenharia e Tecnologia::Engenharia Civilpor
sdum.journalLecture Notes in Civil Engineeringpor
sdum.conferencePublication18th International Probabilistic Workshoppor
oaire.versionAMpor
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