Key points are not available for this paper at this time.
In this study, a novel machine learning (ML) technique, eXtreme Gradient Boosting (XG Boost), was employed to train an extremely precise ML model. The developed XG Boost model was highly interpretable, filling the gap and opening black boxes in the literature. The study provides further a simple and free user interface to support the design of normal- and high-strength Blast Furnace Slag (BFS), and fly ash (FA) concrete. The compressive strength of 1030 concrete mixes containing cement (C), BFS, and FA were collected and analyzed. The baseline model tend to overfit, with R2 values of 0.996 and 0.919 for the training and testing datasets, respectively. The hyperparameters of the model have been optimized using vector multi-objective optimization to maximize the prediction capability of the model. The optimized XG Boost model exhibited a superior prediction performance with R2 of 0.992 and 0.949 for the training and testing datasets. Based on Gini indexes and SHAP values, C, FA, water, and aggregate were the most significant model parameters. According to this study, the best BFS, FA, sand, and superplasticizer contents for concrete strength optimization were 100–200, 100–200, 600–800, and 7–13 kg/m3, respectively. The SP has a negligible effect on concrete's compressive strength at low water contents (less than 180 kg/m3), but a stochastic effect at high contents. The various chemical properties of high-range water reducers may have resulted in the randomly generated response in the current study.
Khan et al. (Sat,) studied this question.