Incorporating the heterogeneity of asphalt mixtures in flexible pavements subjected to moisture diffusion

Caro, Silvia; Castillo, Daniel; Masad, Eyad

doi:10.1080/10298436.2015.1007236

Cited by 17 publications

(3 citation statements)

References 19 publications

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“…An example of such a parameter is the air void distribution within asphalt mixtures. Several studies have implemented RF to model heterogeneity in pavement materials (Caro et al., 2015; Lea & Harvey, 2015; Manrique‐Sanchez et al., 2020). Combining stochastic modeling with FE simulations offers the advantage of studying the effects of material properties on the mechanical response of materials without the need to build complex FE models.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Computational generation of multiphase asphalt nanostructures using random fields

Aljarrah

Karaki

Masad

et al. 2022

Computer aided Civil Eng

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“…They predicted the crack growth path using cohesive elements and calculated the tensile strength and density of the strain energy for the modeled asphalt samples. Some other researchers have focused on analyzing the heterogeneity of asphalt mixtures from different aspects such as X‐ray computed tomography, aggregate image system, microstructural heterogeneity, and stochastic random fields (RF) 26–32 . For example, Castillo et al 26 simulated the asphalt mixture as heterogeneous material by using microstructure generator (MG) and FEM.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity effect on fracture parameters of a multilayer asphalt pavement structure containing a top‐down crack and subjected to moving traffic loading

Aliha

Ziari

Fard

et al. 2021

Fatigue Fract Eng Mat Struct

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A part of the multilayer pavement structure containing top‐down crack in the hot mix asphalt (HMA) layer and subjected to loading of a moving vehicle is simulated using the finite element method. Aggregate distribution and packing algorithm was used for modeling the HMA layer around the top‐down crack as a heterogeneous two‐phase (aggregate/fine aggregate matrix [FAM]) medium. By considering the heterogeneity aspects of the asphalt mixture, the influence of traffic loading location, aggregate distribution, crack length, thickness layer, and mechanical properties of layers and components were investigated on the fracture parameters. The results showed that for different locations of vehicle wheels relative to the top‐down crack, the magnitude and sign of the fracture parameters in heterogeneous modeling were significantly different from the similar values of the homogeneous simulation. The stress intensity factors and especially the T‐stress were significantly sensitive to the crack tip location that can be placed either inside the stiffer aggregate or the softer FAM part.

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“…A study conducted by Mehrez et al addressed the stochastic modeling and propagation of creep compliance (D(t)) within a probabilistic framework coupled with finite element analysis to provide a spectral representation and statistical analysis of strain response which can be used for reliability-based analysis of pavement performance ( 13 ). Other research has addressed the modeling of the uncertainty in material properties of AC pavements in relation to randomized air void fields and the incorporation of the random distribution of these voids into macro-mechanical modeling of flexible pavements using finite element methods ( 15 ). Other researchers incorporated uncertainty resulting from heterogeneity in AC mixtures using stochastic techniques to simulate the response of AC layers subjected to moisture diffusion and mechanical loading ( 16 ).…”

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confidence: 99%

Development of Probabilistic Viscoelastic Continuum Damage Model for Asphalt Concrete

Kassem

Chehab

Najjar

2019

Transportation Research Record

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The main objective of this paper is to develop a realistic probabilistic framework for characterization of different types of asphalt concrete using advanced material modeling. The adopted methodology builds on and enhances a viscoelastic continuum damage (VECD) material model by utilizing a suite of associated experimental testing protocols and incorporating the uncertainties associated with the different material properties. The modeled uncertainties address the variabilities and errors associated with the linear viscoelastic (LVE) functions achieved from the complex modulus test and damage characteristic curves obtained from constant crosshead rate testing. A probabilistic scheme using First Order approximations and Monte Carlo simulations is developed to characterize the inherent uncertainty of each of the LVE functions over the time domain of their mastercurves. For damage characteristic curves, the uncertainty in normalized pseudostiffness increases as the level of damage becomes larger. The uncertainties of LVE properties are propagated along with those of C versus stress curves to yield a probabilistic viscoelastic continuum damage model (P-VECD). The P-VECD not only predicts the average viscoelastic response to a given loading input, but it can also provide its distribution, which is essential for a reliability-based pavement design.

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Incorporating the heterogeneity of asphalt mixtures in flexible pavements subjected to moisture diffusion

Cited by 17 publications

References 19 publications

Computational generation of multiphase asphalt nanostructures using random fields

Computational generation of multiphase asphalt nanostructures using random fields

Heterogeneity effect on fracture parameters of a multilayer asphalt pavement structure containing a top‐down crack and subjected to moving traffic loading

Development of Probabilistic Viscoelastic Continuum Damage Model for Asphalt Concrete

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