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

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