Nanostructural and Nanomechanical Properties of LDPE-Modified Binders

Aljarrah, Mohammad Fuad; Roja, K. Lakshmi; Masad, Eyad; Ouederni, Mabrouk; Ibikunle, Oluwagbemi Banji

doi:10.1061/(asce)mt.1943-5533.0004220

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Figure 16 shows the von Mises stress distribution and elastic modulus distribution for the experimentally obtained nanostructures and 25 computationally generated samples, respectively. The overall trend aligns with findings from a previous work conducted by the authors of this study (Aljarrah et al., 2022), in which they measured the bulk dynamic modulus of the same binders using a dynamic shear rheometer. This previous study showed that sample 5L produced the highest stiffness, followed by sample 3L, and then PG64‐22.…”

Section: Resultssupporting

confidence: 92%

“…The intrinsic heterogeneity of such materials at the nanoscale is translated into variations in their mechanical response and when used as part of civil works, such as road structures, their field performance, and durability. On top of that, the widely adopted addition of asphalt modifiers introduces further heterogeneity to the material due to the presence of new constituents/phases (Aljarrah et al., 2022; Roja et al., 2022). It is essential to account for the effects of these variabilities to understand their behavior and predict performance; computational tools are especially well‐suited for this task.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, assigning a single material property value to each phase of the FE model indicates the homogeneity of their material properties (Adeli & Kumar, 1995; Yu & Adeli, 1993). In reality, many materials, including asphalt binders, exhibit heterogeneity at the nanoscale (Aljarrah & Masad, 2020; Aljarrah et al., 2022; Holleran et al., 2020). This heterogeneity is demonstrated by the spatial variability of their nanostructures and material properties.…”

Section: Introductionmentioning

confidence: 99%

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Computational generation of multiphase asphalt nanostructures using random fields

Aljarrah

Karaki

Masad

et al. 2022

Computer aided Civil Eng

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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.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%