Prediction and Interpretability Analysis of Creep in Recycled Aggregate Concrete Using Multiple Machine Learning Models

Compared with natural aggregate concrete, the creep behavior of recycled aggregate concrete (RAC) is a key technical bottleneck restricting its engineering application. Owing to the coupled effects of multiple key factors related to recycled aggregates, RAC exhibits pronounced complexity and strong nonlinearity in its creep behavior. In this study, eight key factors were selected as input parameters, and five machine learning models were established. An online graphical user interface (GUI) prediction system based on the optimal model was developed in Python 3.9.7 using Streamlit. Furthermore, the SHapley Additive exPlanations (SHAP) method was employed to clarify the importance ranking and model-based contribution patterns of the key input factors. The results indicate that, based on four evaluation metrics, including the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error, the back-propagation neural network (BPNN) model demonstrates superior predictive performance to the other four machine learning models across both the training and test sets, with the radial basis function neural network (RBFNN) model ranking second. The maximum coefficient of determination of the conventional RAC creep prediction models was only 0.7045, and their prediction accuracy was lower than that of all five machine learning models established in this study.