Monte Carlo simulations of the fracture resistance of an asphalt pavement layer - Publication - Bridge of Knowledge

Search

Monte Carlo simulations of the fracture resistance of an asphalt pavement layer

Abstract

The purpose of the proposed numerical model is to analyze the cracking of the wearing course in a pavement overlay, assuming a pre-existing crack that passes through the binding layer and base. The computations employed the author's simulation-based Monte Carlo material model, which describes the failure process of a Semi-Circular Bend (SCB) specimen during standard laboratory testing of asphalt concrete. A key feature of this model is the incorporation of the random nature of material parameters, allowing for the simulation of result dispersion when analyzing a sufficiently large population of samples. The proposed FEM model and obtained material data were directly applied to the numerical analysis of the pavement structure. The comprehensive computational algorithm allows for a random description of the load that induces crack propagation in the pavement wearing course, leading to the creation of a histogram that defines the range of failure load dispersion. Such supporting calculations can assist in optimizing asphalt mix design and, in the future, may allow for the estimation of pavement structure reliability.

Citations

  • 0

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Cite as

Full text

full text is not available in portal

Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
CONSTRUCTION AND BUILDING MATERIALS no. 452,
ISSN: 0950-0618
Language:
English
Publication year:
2024
Bibliographic description:
Smakosz Ł., Szydłowski C., Górski J.: Monte Carlo simulations of the fracture resistance of an asphalt pavement layer// CONSTRUCTION AND BUILDING MATERIALS -,iss. 452 (2024), s.138970-
DOI:
Digital Object Identifier (open in new tab) 10.1016/j.conbuildmat.2024.138970
Sources of funding:
  • Free publication
Verified by:
Gdańsk University of Technology

seen 27 times

Recommended for you

Meta Tags