A machine learning framework for intelligent development of Ultra-High performance concrete (UHPC): From dataset cleaning to performance predicting

This study proposes a new machine learning (ML) framework, which mainly includes dataset cleaning processing as well as performance predicting, for property prediction of ultra-high performance concrete (UHPC). Firstly, the missing data in original dataset is interpolated and discussed by visualization results. Then, the existing outliers in the completed dataset are found out to improve the quality of dataset. Meanwhile, by analyzing the influence of key parameter, it not only clarifies the influence of dataset quality on model prediction results, but also proves the necessity of anomaly detection, with R2 increasing 15% and RMSE decreasing 37%. Finally, the chosen model is trained and further optimized by hyperparameter optimization, in which the loss function is significantly reduced by 68.82% for training data (R2 > 0.95) and 84.36% for testing data (R2 > 0.94). Overall, this framework can effectively improve the accuracy and generalization of UHPC predictive models, which is also suitable for other types of concrete materials.