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

Résumé

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 adaptativenetwork-
based fuzzy inference system (ANFIS) model developed by BILIR et al. [1] that uses three input
parameters.
Keywords: mortar; data mining; prediction; restrained shrinkage cracking.