Super learner machine-learning algorithms for compressive strength prediction of high performance concrete

Abstract

Because the proportion between the compressive strength of high-performance concrete (HPC) and its composition is highly nonlinear, more advanced regression methods are demanded to obtain better results. Super learner models, which are based on several ensemble methods including random forest regression (RFR), an adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical gradient Boosting (CatBoost), are used to solve this complicated problem. A grid search method is employed to determine the best set of hyper-parameters of each ensemble algorithm. Two super learner models, which combine all six models or select the top three effective ones as the base learners, are then proposed to develop an accurate approach to estimate the compressive strength of HPC. The results on four popular datasets show significant improvement of the proposed super learner models in terms of prediction accuracy. It also reveals that their trained models always perform better than other methods since their errors (MAE, MSE, RMSE) are always much lower and values of R2 are higher than those of the previous studies. The proposed super learner models can be used to provide a reliable tool for mixture design optimization of the HPC.