3D random packing algorithm of ellipsoidal particles based on the Monte Carlo method

Changhong Chen*, Songlin Bai, Ying Huang, Lik Lam, Yao Yao, Leon M. Keer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The random packing of aggregate particles is an important factor affecting the mechanical properties of concrete at the mesoscopic scale. In the current study, a meso-mechanical pretreatment algorithm is developed to construct the random ellipsoidal aggregate model for the mesoscopic structure of fully graded concrete. The Fuller curve combined with equivalent diameter is adopted to ensure equality between the gradation and content of the random ellipsoidal aggregates and those of the actual geometric aggregates. A 'removing occupied space' method is proposed to improve the packing efficiency based on the background grids strategy. A modified search algorithm consisting of rough and fine detection for determining the overlaps is proposed to improve the optimised simulation of the meso-structure of cement-based composites. A random ellipsoidal aggregate model with different aspect ratios of ellipsoid is developed and compared with the existing algorithms to test the efficiency of the new pretreatment algorithm. The effect of the ellipsoidal shape on the random packing fraction is investigated based on the proposed pretreatment algorithm. The pretreatment algorithm proposed greatly improves the efficiency of packing and provides a powerful tool for the realisation of three-dimensional large-scale numerical meso-concrete.

Original languageEnglish (US)
Pages (from-to)343-355
Number of pages13
JournalMagazine of Concrete Research
Issue number7
StatePublished - Apr 2021
Externally publishedYes


  • aggregates
  • compositematerials
  • finite element methods

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)


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