Predicting the impact of adding metakaolin on the splitting strength of concrete using ensemble ML classification and symbolic regression techniques –a comparative study

Abstract

The mechanical characteristics of concrete are crucial factors in structural design standards especially in concrete technology. Employing reliable prediction models for concrete’s mechanical properties can reduce the number of necessary laboratory trials, checks and experiments to obtain valuable representative design data, thus saving both time and resources. Metakaolin (MK) is commonly utilized as a supplementary replacement for Portland cement in sustainable concrete production due to its technical and environmental benefits towards net-zero goals of the United Nations Sustainable Development Goals (UNSDGs). In this research work, 204 data entries from concrete mixes produced with the addition of metakaolin (MK) were collected and analyzed using eight (8) ensemble machine learning tools and one (1) symbolic regression technique. The application of multiple machine learning protocols such as the ensemble group and the symbolic regression techniques have not been presented in any previous research work on the modeling of splitting tensile strength of MK mixed concrete. The data was partitioned and applied according to standard conditions. Lastly, some selected performance evaluation indices were used to test the models’ accuracy in predicting the splitting strength (Fsp) of the studied MK-mixed concrete. At the end, results show that the k-nearest neighbor (KNN) outperformed the other techniques in the ensemble group with the following indices; SSE of 4% and 1%, MAE of 0.1 and 0.2 MPa, MSE of 0, RMSE of 0.1 and 0.2 MPa, Error of 0.04% and 0.04%, Accuracy of 0.96 and 0.96 and R2 of 0.98 and 0.98 for the training and validation models, respectively. This is followed closely by the support vector machine (SVM) with the following indices; SSE of 7% and 3%, MAE of 0.2 and 0.2 MPa, MSE of 0.0 and 0.1 MPa, RMSE of 0.2 and 0.3 MPa, Error of 0.05% and 0.06%, Accuracy of 0.95 and 0.94, and R2 of 0.96 and 0.95, for the training and validation models, respectively. The third model in the superiority rank is the CN2 with the following performance indices; SSE of 15% and 4%, MAE of 0.2 and 0.2 MPa, MSE of 0.1 and 0.1 MPa, RMSE of 0.3 and 0.3 MPa, Error of 0.08% and 0.07%, Accuracy of 0.92 and 0.93 and R2 of 0.92 and 0.93, for the training and validation models, respectively. These models outperformed the models utilized on the MK-mixed concrete found in the literature, therefore are the better decisive modes for the prediction of the splitting strength (Fsp) of the studied MK-mixed concrete with 204 mix data entries. Conversely, the NB and SGD produced unacceptable model performances, however, this is true for the modeled database collected for the MK-mixed Fsp. The RSM model also produced superior performance with an accuracy of over 95% and adequate precision of more than 27. Overall, the KNN, SVM, CN2 and RSM have shown to possess the potential to predict the MK-mixed Fsp for structural concrete designs and production.