Impact of Aggregates on Concrete Fracture Analysis Based on the Ergodicity oF Mesh Refinement

Authors

  • Gabriel A. Chacón Téllez Universidad de Buenos Aires, Facultad de Ingeniería & Instituto de Tecnologías y Ciencias de la Ingeniería “Hilario Fernández Long” (INTECIN), CONICET-UBA. Ciudad Autónoma de Buenos Aires, Argentina. https://orcid.org/0009-0009-8644-3008
  • Felipe López Rivarola Universidad de Buenos Aires, Facultad de Ingeniería & Instituto de Tecnologías y Ciencias de la Ingeniería “Hilario Fernández Long” (INTECIN), CONICET-UBA. Ciudad Autónoma de Buenos Aires, Argentina.
  • Daniel van Huyssteen Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg. Erlangen, Germany.
  • Paul Steinmann Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg. Erlangen, Germany.
  • Guillermo Etse Universidad de Buenos Aires, Facultad de Ingeniería & Instituto de Tecnologías y Ciencias de la Ingeniería “Hilario Fernández Long” (INTECIN), CONICET-UBA. Ciudad Autónoma de Buenos Aires, Argentina. & CONICET - Universidad Nacional de Tucumán, Facultad de Ciencias Exactas y Tecnología. San Miguel de Tucumán, Argentina.

DOI:

https://doi.org/10.70567/rmc.v2.ocsid8556

Keywords:

Refinement, Ergodicity, VEM

Abstract

This work extends a stochastic procedure for modeling brittle failure using virtual elements (VEs) and nonlinear interface elements (IEs), adapted to heterogeneous materials with inclusions such as concrete. Based on the ergodicity of randomly generated polyhedral mesh refinement, the approach simulates tortuous crack propagation in concrete with aggregates under Mode II fracture. We study the three-point bending test, systematically varying mesh density and the random arrangement of aggregates, and analyze sensitivity to constitutive parameters such as elastic modulus and fracture energy. The results show that ergodic averaging over mesh refinements and aggregate configurations accurately reproduces the structural response, and that the interaction between these two sources of randomness improves the model’s predictive robustness.

Published

2025-12-15

Issue

Section

Abstracts in MECOM 2025