Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/63233

TitlePrediction of restrained shrinkage crack width of slag mortar composites using data mining techniques
Author(s)Martins, Francisco F.
Camões, Aires
KeywordsData Mining
Mortar
Prediction
restrained shrinkage cracking
Issue dateNov-2019
PublisherRede Latino-Americana de Materiais
JournalRevista Matéria
CitationMartins F. F., Camões A. Prediction of Restrained Shrinkage Crack Width of Slag Mortar Composites Using Data Mining Techniques, Matária, Vol. 24, Issue 4, doi:10.1590/s1517-707620190004.0852, 2019
Abstract(s)The purpose of this study is to develop data mining models to predict restrained shrinkage crack widths of slag mortar cementitious composites. A database published by BILIR et al. [1] was used to develop these models. As a modelling tool R environment was used to apply these data mining (DM) techniques. Several algorithms were tested and analyzed using all the combinations of the input parameters. It was concluded that using one or three input parameters the artificial neural networks (ANN) models have the best performance. Nevertheless, the best forecasting capacity was obtained with the support vector machines (SVM) model using only two input parameters. Furthermore, this model has better predictive capacity than adaptative-network-based fuzzy inference system (ANFIS) model developed by BILIR et al. [1] that uses three input parameters.
TypeArticle
URIhttp://hdl.handle.net/1822/63233
DOI10.1590/s1517-707620190004.0852
ISSN1517-7076
Publisher versionhttp://www.scielo.br/scielo.php?pid=S1517-70762019000400345&script=sci_arttext
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:C-TAC - Artigos em Revistas Internacionais

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