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      Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques

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          Abstract

          ABSTRACT 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.

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          A tutorial on support vector regression

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            “Support vector networks”

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Journal
                rmat
                Matéria (Rio de Janeiro)
                Matéria (Rio J.)
                Rede Latino-Americana de Materiais (Rio de Janeiro, RJ, Brazil )
                1517-7076
                2019
                : 24
                : 4
                Affiliations
                Minho Guimarães orgnameCTAC orgdiv1University of Minho orgdiv2Department of Civil Engineering Portugal
                Minho Guimarães orgnameISISE orgdiv1University of Minho orgdiv2Department of Civil Engineering Portugal
                Article
                S1517-70762019000400345
                10.1590/s1517-707620190004.0852

                This work is licensed under a Creative Commons Attribution 4.0 International License.

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                Figures: 0, Tables: 0, Equations: 0, References: 32, Pages: 0
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