Utilizing DEM and interpretable ML algorithms to examine particle size distribution's role in small-strain shear modulus of gap-graded granular mixtures

Abstract

This study merges macroscale and microscale analyses with interpretable machine learning (ML) algorithm to explore the effect of particle size distribution (PSD) on the small-strain shear modulus (G0) of gap-graded granular mixtures. For this purpose, a database containing 610 groups of data samples was generated by conducting numerical triaxial compression simulations using the discrete element method (DEM). The simulations were carried out using the well-recognized DEM program PFC3D. The influence of isotropic confining pressure (p′), void ratio (e), non-plastic fines content (FC), particle size ratio (Rd), particle size ratio of coarse particles (Dmax/Dmin) and particle size ratio of fine particles (dmax/dmin) on the G0 were discussed in detail. Furthermore, the eXtreme Gradient Boosting (XGBoost) algorithm was utilized, and it was enhanced through a hyperparameter tuning process known as Bayesian optimization (BO) to improve the predictive accuracy of G0 model. Finally, the predictive outcomes of the BO-XGBoost model were interpreted using the SHapley Additive exPlanations (SHAP) method, addressing the technical gap associated with the lack of interpretability in ML models. Meanwhile, the local and global feature variable importance in nonlinear models was also elucidated to provide intuitive insight for the user. The analysis demonstrates that the outcomes from both macroscale and microscale assessments correlate well with the findings obtained through the BO-XGBoost-SHAP approach, facilitating the incorporation of ML model predictions into decision-making processes.