Abstract: Strength prediction method of high performance concrete based on stacking model fusion was proposed to address the issues of large deviations and low efficiency of traditional empirical formulas for high-performance concrete strength prediction. Firstly, 1 030 sets of high-performance concrete compressive strength test data were preprocessed through data cleaning and normalization to eliminate abnormal data and the dimensional influence among data. Secondly, based on extreme gradient boosting (XGBoost), category boosting, multi-layer perceptron, and random forest (RF) algorithms, hyperparameter optimization, model training and evaluation were conducted, and the overall effect of the four base learners on strength prediction were compared and analyzed using coefficient of determination R², root mean square error and mean absolute error. Based on this, a Stacking ensemble learning model was constructed, which fuses multiple machine learning algorithms for concrete strength prediction. Finally, the model was validated using 103 sets of new data, and interpretable analysis was performed. The results show that compared to other combinations of base learners, the fusion model using XGBoost and RF significantly improves prediction accuracy and performance, and has good generalization performance. The interpretable analysis shows that the most important input feature variables are age and cement, indicating that the internal prediction logic of the model is more in line with engineering practice experience, having high rationality and reliability. The research results provide reference for further improving the accuracy of high-performance concrete strength prediction.

Key words: concrete, strength prediction model, ensemble learning, stacking algorithm, XGBoost algorithm, RF algorithm