Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/23707

TitlePrediction of compressive strength of concrete containing fly ash using data mining techniques
Other titlesTechniki zgłȩbiania danych w prognozowaniu wytrzymałości na ściskanie betonu z dodatkiem popiołu lotnego
Author(s)Martins, Francisco F.
Camões, Aires
KeywordsConcrete strength
Fly ash
Data mining
Artificial neural networks
Support vector machines
Issue dateFeb-2013
PublisherStowarzyszenie Producentow Cementu
JournalCement Wapno Beton
Abstract(s)The concrete compressive strength is the most used mechanical property in the design of concrete structures. Therefore, the use of rational models to its prediction, to simulate the effects of its different constituents and its properties can play an important role in the achievement of the safety-economy required. Models to forecast the concrete compressive strength have already been presented before by some researchers. However, the comparison of different rational models and the application of models to predict the importance of the different constituents in the concrete behaviour have not yet been approached. Therefore, developing these models will be necessary namely to take into account the quality, i.e. the activity, of the most used mineral addition in concrete: fly ash. This study compared different Data Mining techniques to predict the compressive strength of fly ash concrete along time. The presented models are able to learn the complex relationships between several variables like the uniaxial compressive strength, the different concrete compounds and its mix design, the different properties of the fly ash used and the relative influence of its.
TypeArticle
URIhttp://hdl.handle.net/1822/23707
ISSN1425-8129
Publisher versionhttp://www.cementwapnobeton.pl/index.php
Peer-Reviewedyes
AccessOpen access
Appears in Collections:C-TAC - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
FFM&AC_VF.pdfdocumento principal327,32 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