Strength Prediction in UHPC with XGBoost Model and Shapley Algorithm Interpretation

Guanzhong Wu. Hairui Gou. Qingzhao Ren. Ran Tang. Peng Feng.

DOI: https://doi.org/10.70114/acmsr.2024.1.1.P82

Keywords:

UHPC；Machine learning；XGBoost；Strength prediction；Shapley algorithm

Abstract:

This study employs the XGBoost regression model to predict the strength of UHPC and utilizes the Shapley algorithm to interpret the model's predictions, revealing the impact of various feature parameters. The results demonstrate that the XGBoost regression model effectively fits the data and possesses strong predictive capabilities. Furthermore, the interaction between silica fume and cement significantly influences the model predictions. Additionally, using tools such as the Shapley heatmap, the study analyzes the model's characteristics and finds that only a subset of samples have Shapley values below the mean, indicating the dataset contains relatively few high-quality samples. Through the Shapley algorithm, the optimal range for silica fume quantity is determined to be between 0 and 320 kg. This research validates the effectiveness of the XGBoost regression model for predicting UHPC strength and enhances model interpretability using the Shapley algorithm.