Machine Learning-based Study on Rheological Properties of UHPC by SHAP and PDP Analysis
ZHOU Xiangxu1, DUAN Feng1,*, ZHU Bo2
1 College of Materials Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China 2 Shaanxi Concreate Applied Materials Co., Ltd., Xi’an 710086, China
Abstract: In this study, machine learning methods combined with interpretable tools SHAP (Shapley Additive exPlanations) and PDP (Partial Depen-dence Plot) were used to accurately predict the rheology of ultra-high performance concrete (UHPC). By collecting a large amount of UHPC rheological parameter data and input variables including water consumption, mineral admixture content and admixture content, four kinds of machine learning prediction models were constructed. The optimal models were selected by R2, MAE, RMSE and other evaluation indicators, and SHAP and PDP analysis were carried out. The experimental results show that the optimal machine learning model combined with SHAP and PDP can effectively predict the rheology of UHPC. Moreover, the interpretability of the method helps to better understand the prediction process and results of the model, and provides a basis for further optimizing the mix ratio of UHPC.
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2026, Vol. 40 
