The structure–property relationship of granular materials with different friction coefficients: Insight from machine learning. (July 2022)

• Record Type: Journal Article
• Authors: Zhang, Yibo; Zhou, Wei; Ma, Gang; Cheng, Ruilin; Chang, Xiaolin
• Is Part Of: Extreme mechanics letters. Volume 54(2022)
• Publication Date: 2022-07
• Subjects: Granular materials -- Machine learning -- Plastic deformation -- Structural features -- Structure–property relationship; Mechanics -- Periodicals; Mechanics, Applied -- Periodicals; Mechanics; Electronic journals; Periodicals; 531.05
• DOI: 10.1016/j.eml.2022.101759
• Languages: English
• ISSNs: 2352-4316

Abstract

Abstract: When granular materials are subjected to mechanical disturbance, dynamic heterogeneity can be observed. Although it has long been considered that dynamic heterogeneity is related to structure in material science, there were still few studies focusing on the structure–property of granular materials. In this study, we simulate conventional triaxial tests of polydisperse spheres using discrete element method. Different friction coefficients were used to represent different microscopic contact modes in the simulation. The machine learning (ML) model for predicting the plastic deformation of granular materials is successfully developed from particles' local structural information using the eXtreme Gradient Boosting algorithm. Besides, we focus on how the structural indicators of granular materials affect the multiple physical and mechanical properties. We further observed and explained the variation of ML predictive power in granular systems with different friction coefficients. Overall, our study presents a more intensive and innovative insight into the structure–property relationship of granular materials. Graphical abstract: Highlights: The machine learning model for predicting plastic deformation of granular materials. The structural indicators "softness" reflect multiple physical and mechanical properties of granular materials. Machine learning model accuracy increases with the increase of inter-particle friction coefficient. Contact number is identified as the most important signature in predicting plastic deformation.