Interpretable Machine Learning Approaches for Assessing Maximum Force in Fiber-Reinforced Composites

Abstract

This paper investigates the accurate prediction of the maximum force in fiber-reinforced composites using the CatBoost machine learning algorithm. The study incorporates the Shapley additive explanations technique to enhance interpretability, revealing the significance of the impact of each variable on the output at both local and global scales. The research demonstrates that Shapley additive explanations provide valuable insights into the decision-making process of the machine learning model, identifying influential variables for specific instances and contributing to a comprehensive understanding of the overall model predictions. Notably, the alignment between the feature importance analyses from the machine learning model and Shapley additive explanations reinforces the significance of certain parameters in predicting maximum force as an interfacial property. The study advances the prediction of interfacial properties in fiber-reinforced composites and underscores the value of interpretable machine learning methods in offering insights into complex predictive models.