Prediction of Compressive Strength of Concrete Specimens Based on Interpretable Machine Learning

The aim of this paper is to explore an effective model for predicting the compressive strength of concrete using machine learning technology, as well as to interpret the model using an interpretable method, which overcomes the limitation of the unknowable prediction processes of previous machine learning models. An experimental database containing 228 samples of the compressive strength of standard cubic specimens was built in this study, and six algorithms were applied to build the predictive model. The results show that the XGBoost model has the highest prediction accuracy among all models, as the R² of the training set and testing set are 0.982 and 0.966, respectively. Further analysis was conducted on the XGBoost model to discuss its applicability. The main steps include the following: (i) obtaining key features, (ii) obtaining trends in the evolution of features, (iii) single-sample analysis, and (iv) conducting a correlation analysis to explore methods of visualizing the variations in the factors that exert influence. The interpretability analyses on the XGBoost model show that the contribution to the compressive strength by each factor is highly in line with the conventional theory. In summary, the XGBoost model proved to be effective in predicting concrete's compressive strength.