Deep-learning-enhanced model reconstruction of realistic 3D rock particles by intelligent video tracking of 2D random particle projections

Rock assemblies, such as ballasts and cobbles, are typically composed of granular particles of various sizes and shapes that significantly affect their mechanical behavior. Currently, it remains challenging to generate realistic and identical three-dimensional (3D) models for rock particles with a limited number of their individual two-dimensional (2D) projections. This study proposes a novel and effective deep-learning-enhanced approach to achieve the realistic reconstruction of 3D particle models, which includes the following sequential steps: (1) automatically detecting 2D projected images of each 3D particle individually during dynamic scanning of a rock particle assembly; (2) predicting 3D particle size indexes based on 2D images for each particle; (3) generating a corresponding 3D skeleton network from 2D outlines; and (4) reconstructing the particle surface of individual particle by spatial interpolations. Sensitivity analysis and verification are finally performed on both randomized superball particles and real cobbles/ballasts. The results demonstrated that the proposed approach is robust and efficient in reconstructing 3D rock particle models and offers a rapid and low-cost practical solution for convincible numerical studies of granular materials.