Modeling the Slump Flow of Self-Compacting Concrete Incorporating Metakaolin Using Soft Computing Techniques

The sensitivity of slump flow of self-compacting concrete containing metakaolin to its ingredient materials and mixture proportions, necessitate the use of high accuracy models to guarantee both estimation and generalization features. Therefore this paper investigates the potential of multivariate adaptive regression splines (MARS) and model tree (MT) approaches in prediction of slump flow of self-compacting concrete. Total of 117 data collected from the several published literature were used in present work. The data used in proposed models are arranged in a format of eight input parameters including cement, coarse aggregate, fine aggregate, water, metakaolin, super plasticizer, binder and maximum size of aggregates (Dmax) and one output as slump flow. To evaluate the precision of the models, a comparative study has been performed in terms of RMSE, R and MAE indices. The results of training and testing datasets of the techniques are compared with experimental results and their comparisons demonstrate that the MARS and MT models have potential to predict concrete properties with great precision. Performed sensitivity analysis to assign effective parameters on slump flow was indicating fine aggregate and metakaolin is most effective variable for modeling and prediction in this type of the self-compacting concrete using MT technique in this study