Rapid Generation of 3D Mesoscale Concrete Models Using an Improved GJK Algorithm for Collision Detection

Efficient generation of 3D mesoscale concrete models with high aggregate volume fractions remains challenging due to the computational complexity of detecting overlaps between irregularly shaped aggregates. This study presents an efficient modeling approach utilizing an improved Gilbert–Johnson–Keerthi (GJK) algorithm for rapid collision detection between convex polyhedral aggregates. The enhanced algorithm significantly reduces computational time by approximately 20–25% compared to the classical GJK algorithm, while maintaining detection accuracy, enabling the direct generation of high-volume-fraction (50%) concrete models without requiring additional settlement procedures. The “take-and-place” method is employed to generate and place aggregates according to specified gradation and volume fraction of aggregates. The model is validated against experimental uniaxial compression tests; the simulations accurately capture the macroscopic mechanical response and failure patterns, with the peak stress showing good agreement with experimental data (relative error ≈ 7.6%). The validated model is then employed in a comprehensive parametric study to systematically investigate the influence of key mesoscale parameters, providing profound insights into the underlying failure mechanisms. The proposed approach provides an efficient solution for rapid generation of realistic 3D mesoscale concrete models, facilitating more extensive parametric studies and mechanical analyses. Future extensions may include handling more complex aggregate shapes and leveraging parallel computing for further acceleration.