Computational generation of multiphase asphalt nanostructures using random fields

Abstract

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.

Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks

“…As an alternative, the computer‐aided method, which demonstrates powerful feature extraction (Aljarrah et al., 2022; Martins et al., 2020), analysis (Hassanpour et al., 2019), processing capabilities (Rafiei & Adeli, 2016; Rafiei et al., 2017), and condition adaptability (Yishun Li et al., 2021), has been successfully implied to automated classification of pavement conditions (Hsieh et al., 2021), monitoring of road degradation (Van Hauwermeiren et al., 2022), cracks detection (J. Chen & He, 2022; Chao Liu & Xu, 2022; Xie et al., 2022), and multiple pavement distresses detection (Zhang et al., 2022), in which the capability of generating virtually realistic road morphology for enriching pavement surface texture datasets was proved. Unlike previous methods of regenerating artificially predetermined features, an unsupervised learning method, generative adversarial networks (GANs; Goodfellow et al., 2020), was implemented in this study.…”

Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo

“…Other works have focused on surface texture evaluation (J. Lu et al, 2023) or asphalt material microstructure (Aljarrah et al, 2022;C. Jin et al, 2021).…”

“…Li et al., 2022; Rodriguez‐Lozano et al., 2023; Siriborvornratanakul, 2023). Other works have focused on surface texture evaluation (J. Lu et al., 2023) or asphalt material microstructure (Aljarrah et al., 2022; C. Jin et al., 2021). However, bulk material properties remain the most popular indicators of pavement health.…”