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Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete

Coelho, M�rio R.F.; Sena-Cruz, Jos� M.; Neves, Lu�s A.C.; Pereira, Marta; Cortez, Paulo; Miranda, Tiago

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Authors

M�rio R.F. Coelho

Jos� M. Sena-Cruz

Marta Pereira

Paulo Cortez

Tiago Miranda



Abstract

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.

Citation

Coelho, M. R., Sena-Cruz, J. M., Neves, L. A., 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, https://doi.org/10.1016/j.conbuildmat.2016.09.048

Journal Article Type Article
Acceptance Date Sep 17, 2016
Online Publication Date Sep 23, 2016
Publication Date Nov 15, 2016
Deposit Date Feb 7, 2017
Publicly Available Date Feb 7, 2017
Journal Construction and Building Materials
Print ISSN 0950-0618
Electronic ISSN 1879-0526
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 126
DOI https://doi.org/10.1016/j.conbuildmat.2016.09.048
Keywords FRP; NSM; Bond; Guidelines; Data Mining
Public URL https://nottingham-repository.worktribe.com/output/828420
Publisher URL http://www.sciencedirect.com/science/article/pii/S095006181631488X
Contract Date Feb 7, 2017

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