Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/44912
Título: | Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete |
Autor(es): | Coelho, Mário Rui Freitas Sena-Cruz, José Neves, Luís A. C. Pereira, Marta Cortez, Paulo Miranda, Tiago F. S. |
Palavras-chave: | NSM Bond FRP Guidelines Data Mining |
Data: | 2016 |
Editora: | Elsevier Sci Ltd |
Revista: | Construction and Building Materials |
Citação: | Coelho, M. R. F., Sena-Cruz, J. M., Neves, L. A. C., Pereira, M., Cortez, P., & Miranda, T. (2016). Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete. Construction and Building Materials, 126, 484-495. doi: 10.1016/j.conbuildmat.2016.09.048 |
Resumo(s): | This paper presents the effectiveness of soft computing algorithms in analyzing the bond behavior of fiber reinforced polymer (FRP) systems inserted in the cover of concrete elements, commonly known as the near-surface mounted (NSM) technique. It focuses on the use of Data Mining (DM) algorithms as an alternative to the existing guidelines’ models to predict the bond strength of NSM FRP systems. To ease and spread the use of DM algorithms, a web-based tool is presented. This tool was developed to allow an easy use of the DM prediction models presented in this work, where the user simply provides the values of the input variables, the same as those used by the guidelines, in order to get the predictions. The results presented herein show that the DM based models are robust and more accurate than the guidelines’ models and can be considered as a relevant alternative to those analytical methods. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/44912 |
DOI: | 10.1016/j.conbuildmat.2016.09.048 |
ISSN: | 0950-0618 |
e-ISSN: | 1879-0526 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Manuscript_Coelho_et_al2016.pdf | 377,12 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons