This study extended previous research by predicting the slump flow and time to 500 mm flow using various factors of high-fluidity concrete with multiple machine learners. The prediction results for unknown data showed a coefficient of determination R2 of 0.73 for slump flow and 0.71 for 500 mm flow arrival time, both with high accuracy over 70%. Permutation Feature Importance and Partial Dependence Plot visualized and evaluated the feature importance and influence of various factors on flow characteristics through machine learning.
