Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87007
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Tinoco, Joaquim | por |
dc.contributor.author | Correia, A. Gomes | por |
dc.contributor.author | Cortez, Paulo | por |
dc.contributor.author | Toll, David | por |
dc.date.accessioned | 2023-10-20T09:56:34Z | - |
dc.date.available | 2023-10-20T09:56:34Z | - |
dc.date.issued | 2023-07-11 | - |
dc.identifier.citation | Tinoco, J.; Gomes Correia, A.; Cortez, P.; Toll, D. An Evolutionary Neural Network Approach for Slopes Stability Assessment. Appl. Sci. 2023, 13, 8084. https://doi.org/10.3390/app13148084 | por |
dc.identifier.uri | https://hdl.handle.net/1822/87007 | - |
dc.description.abstract | A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification. | por |
dc.description.sponsorship | This work was supported by FCT, the “Fundação para a Ciência e a Tecnologia”, within the Institute for Sustainability and Innovation in Structural Engineering (ISISE), project UID/ECI/04029/2013, as well as 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 (Fundo Europeu de Desenvolvimento Regional) 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 also been supported by COMPETE: POCI-01-0145-FEDER-007043. | por |
dc.description.sponsorship | This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE) under reference UIDB/04029/2020 and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020, as well as through the post-doctoral Grant fellowship with reference SFRH/BPD/94792/2013. A special thanks goes to Network Rail, who kindly made 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 | Multidisciplinary Digital Publishing Institute (MDPI) | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FECI%2F04029%2F2013/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F00319%2F2013/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/FARH/SFRH%2FBPD%2F94792%2F2013/PT | por |
dc.relation | POCI-01-0145-FEDER-007633 | por |
dc.relation | POCI-01-0145-FEDER-007043 | por |
dc.relation | LA/P/0112/2020 | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Slope stability condition | por |
dc.subject | Soft computing | por |
dc.subject | Genetic algorithms | por |
dc.subject | Imbalanced data | por |
dc.title | An evolutionary neural network approach for slopes stability assessment | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/13/14/8084 | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 20 | por |
oaire.citationIssue | 14 | por |
oaire.citationVolume | 13 | por |
dc.date.updated | 2023-07-28T12:22:47Z | - |
dc.identifier.eissn | 2076-3417 | - |
dc.identifier.doi | 10.3390/app13148084 | por |
sdum.journal | Applied Sciences | por |
oaire.version | VoR | por |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
applsci-13-08084-v2.pdf | 576,54 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons