Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques
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) techni...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2019
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| Texto completo: | http://hdl.handle.net/1822/63233 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/63233 |
| Resumo: | 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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