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

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 adaptativenetwork-based fuzzy inference system (ANFIS) model developed by BILIR et al. [1] that uses three input parameters.

Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

“…These results showed that the performance of 75% training and 25% testing set was better than that of 50% training and 50% testing set in the estimation of capillary water absorption of fiber‐reinforced SCC due to having higher R 2 value as well as higher PSNR and lower MSE, RMSE, NRMSE, and MAPE scores. In the study of Martins and Camões, 78 the kNN and SVR models were also used as machine models to find the crack width of mortar containing slag. It was said that the SVR model found better crack width than the kNN model.…”