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Computational generation of multiphase asphalt nanostructures using random fields

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journal contribution
submitted on 2023-03-16, 06:26 and posted on 2023-03-16, 06:26 authored by Mohammad Aljarrah, Ayman Karaki, Eyad Masad, Daniel Castillo, Silvia Caro, Dallas Little

This study presents a novel methodology to generate computational replicates of nanostructures of multiphase materials, such as asphalt binders, by integrating image analysis techniques with stochastic random field (RF) modeling. Image analysis techniques are used to identify and segment nanostructure images obtained by atomic force microscopy, while RF is used to model the spatial distribution of their material properties. The results of this process are images showing probable arrangements of nanostructures with stochastic material properties that replicate the experimentally obtained images. The computationally generated nanostructures are then used as inputs in a finite element model to evaluate the effect of heterogeneity on their mechanical response. The efficacy of the developed approach is demonstrated through simulations of asphalt binders’ nanostructures, which reveal novel insights regarding their nanoscale mechanical behavior and response. The FE simulations provided the link between the distribution of nanoscale properties of asphalt binders and variations in their mechanical response. The application of this methodology expands the body of knowledge beyond the deterministic analysis of asphalt binders toward probabilistic analysis and uncertainty quantification that considers their heterogeneous, multiphase structures. Consequently, the methodology can be used to design multiphase materials, such as asphaltic blends, with tailored properties and enhanced performance.

Other Information

Published in: Computer-Aided Civil and Infrastructure Engineering
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: http://dx.doi.org/10.1111/mice.12898

History

Language

  • English

Publisher

Wiley

Publication Year

  • 2022

Institution affiliated with

  • Texas A&M University at Qatar