Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/48236
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Tinoco, Joaquim Agostinho Barbosa | por |
dc.contributor.author | Correia, A. Gomes | por |
dc.contributor.author | Cortez, Paulo | por |
dc.contributor.author | Toll, David | por |
dc.date.accessioned | 2017-12-12T22:27:25Z | - |
dc.date.available | 2017-12-12T22:27:25Z | - |
dc.date.issued | 2017-09 | - |
dc.identifier.citation | J. Tinoco, A. Gomes Correia, P. Cortez, and D. Toll. Data-driven classification approaches for stability condition prediction of soil cutting slopes. In Proceedings of the 19th International Conference on Soil Mechanics and Geotechnical Engineering, pages 1–4, Seoul, South Korea, September 2017. | por |
dc.identifier.uri | https://hdl.handle.net/1822/48236 | - |
dc.description.abstract | For transportation infrastructures, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. In this paper we present a tool aimed at helping in management tasks related to maintenance and repair works for a particular component of these infrastructures, the slopes. For that, the high and flexible learning capabilities of artificial neural networks and support vector machines were applied in the development of a tool able to identify the stability condition of soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved results are presented and discussed, comparing both algorithms performance as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies is also carried out. These achieved results can give a valuable contribution for practical applications at network level. | por |
dc.description.sponsorship | This work was supported by FCT – “Fundação para a Ciência e a Tecnologia", within ISISE, project UID/ECI/04029/2013 as well Project Scope: UID/CEC/00319/2013 and through the post-doctoral Grant fellowship with reference SFRH/BPD/94792/2013. This work was also partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT within the scope of the project POCI-01-0145-FEDER-007633. This work has been also supported by COMPETE: POCI-010145-FEDER-007043. A special thanks goes to Network Rail that kindly make available the data (basic earthworks examination data and the Earthworks Hazard Condition scores) used in this work. | por |
dc.language.iso | eng | por |
dc.publisher | IOS Press | por |
dc.relation | info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F94792%2F2013/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147221/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | slope stability condition | por |
dc.subject | soil cutting slopes | por |
dc.subject | railway | por |
dc.subject | soft computing | por |
dc.subject | data mining | por |
dc.subject | imbalanced data | por |
dc.title | Data-driven classification approaches for stability condition prediction of soil cutting slopes | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationConferenceDate | 17 Set. - 22 Set. 2017 | por |
sdum.event.title | 19th International Conference on Soil Mechanics and Geotechnical Engineering (19th ICSMGE) | por |
sdum.event.type | conference | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 4 | por |
oaire.citationConferencePlace | Seoul, South Korea | por |
oaire.citationVolume | 2017-September | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Civil | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
sdum.conferencePublication | Proceedings of the 19th International Conference on Soil Mechanics and Geotechnical Engineering | por |
Aparece nas coleções: | ISISE - Comunicações a Conferências Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Data-driven Classification Approaches for Stability Condition Prediction of ... [Tinoco et al. 2017].pdf | 145,57 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons