Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/50423

TitleCompressive strength prediction of CFRP confined concrete using data mining techniques
Author(s)Camões, Aires
Martins, Francisco F.
KeywordsCFRP confined concrete
data mining
artificial neural networks
support vector machines
Issue date1-Mar-2017
PublisherTechno Press
JournalComputers and Concrete
Abstract(s)During the last two decades, CFRP have been extensively used for repair and rehabilitation of existing structures as well as in new construction applications. For rehabilitation purposes CFRP are currently used to increase the load and the energy absorption capacities and also the shear strength of concrete columns. Thus, the effect of CFRP confinement on the strength and deformation capacity of concrete columns has been extensively studied. However, the majority of such studies consider empirical relationships based on correlation analysis due to the fact that until today there is no general law describing such a hugely complex phenomenon. Moreover, these studies have been focused on the performance of circular cross section columns and the data available for square or rectangular cross sections are still scarce. Therefore, the existing relationships may not be sufficiently accurate to provide satisfactory results. That is why intelligent models with the ability to learn from examples can and must be tested, trying to evaluate their accuracy for composite compressive strength prediction. In this study the forecasting of wrapped CFRP confined concrete strength was carried out using different Data Mining techniques to predict CFRP confined concrete compressive strength taking into account the specimens' cross section: circular or rectangular.Based on the results obtained, CFRP confined concrete compressive strength can be accurately predicted for circular cross sections using SVM with five and six input parameters without spending too much time. The results for rectangular sections were not as good as those obtained for circular sections. It seems that the prediction can only be obtained with reasonable accuracy for certain values of the lateral confinement coefficient due to less efficiency of lateral confinement for rectangular cross sections.
TypeArticle
URIhttps://hdl.handle.net/1822/50423
DOI10.12989/cac.2017.19.3.233
ISSN1598-8198
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:C-TAC - Artigos em Revistas Internacionais
ISISE - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
Paper_FRP_FFM_AiresCamões_Vf.pdf
  Restricted access
728,03 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID