Computational modelling of heterogeneity of asphalt mixtures - - PowerPoint PPT Presentation

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Computational modelling of heterogeneity of asphalt mixtures - - PowerPoint PPT Presentation

Computational modelling of heterogeneity of asphalt mixtures Daniel Castillo 31.05.2018 Transport Research Finland 2018 AC Heterogeneity Initial considerations Fine Aggregate Matrix (FAM) AC: Mixture and compaction of bitumen,


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Daniel Castillo 31.05.2018 Transport Research Finland 2018

Computational modelling

  • f heterogeneity of

asphalt mixtures

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AC Heterogeneity

  • AC: Mixture and compaction of

bitumen, aggregates and air voids. Heavily used in construction of road infrastructure, enduring traffic loadings and environmental conditions.

  • Differences among AC constitutive

phases’ response to mechanical and environmental solicitations.

  • AC is a heterogeneous material, and

this heterogeneity may induce variability in the response.

Aggregates Air voids Fine Aggregate Matrix (FAM)

Initial considerations

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“Macro” approaches

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A random field is a n-dimensional vector of random values that exhibit spatial correlation.

Correlated random field, isotropic. Uncorrelated random normal numbers.

3.02

  • 3.23

Three dimensional random field, isotropic

CASTILLO & CARO (2014). “Effects of air voids variability on the thermo-mechanical response of asphalt mixtures”

AC Heterogeneity – “Macro”

Random fields

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AC Heterogeneity – “Macro”

Methodology

Masad et al. (2009)

500 1000 1500 2000 20 40 60 E(t) [MPa] Time [s] AV 4% AV 7% AV 10%

Field of horizontal strains (εh)

  • 3.08

3.18

εh ×10-4

Random field of Air Voids

4.43 10.07

AV [%]

Calculated Linear Viscoelastic properties

6200 11,900

Eo [MPa] Air Voids LVE Strain (εh) CASTILLO & CARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”

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AC Heterogeneity – “Macro”

Air Void content 5.9 % AV 6.6 % Average AV content 6.45 % AV 7.4 % AV 6.68 % AV 7.06 % AV 7.44 %

AC layer 1 AC layer 2 AC layer N AC layer 3

AV 8.3 %

Com putational applications

AV [%]

1.3 14.8 8.1 11.4 4.7 CASTILLO & CARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”

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1.0E-04 1.2E-04 1.4E-04 1.6E-04 1.8E-04 98 100 102 104 106 108 110

εh

x [cm]

Bottom row

x

Homogeneous layers

AC Heterogeneity – “Macro”

Com putational applications

CASTILLO & CARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”

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1.0E-04 1.2E-04 1.4E-04 1.6E-04 1.8E-04 98 100 102 104 106 108 110

εh

x [cm]

Bottom row

x

Heterogeneous layers

AC Heterogeneity – “Macro”

Response dispersion increases (approx. x8)

Com putational applications

CASTILLO & CARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”

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AC Heterogeneity – “Macro”

Heterogeneous FE model of the pavement structure

Eo [GPa]

20.7 45.9 33.3 39.6 27.0

Area of moving load application

× N

x y z

Heterogeneity in the asphalt material (3D RF)

Com putational applications

CASTILLO & AL-QADI (2018). “Importance of Heterogeneity in Asphalt Pavement Modeling”

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AC Heterogeneity – “Macro”

Com putational applications

AC layer – Top view

x y z

×

7.83 με CV 5.6% CASTILLO & AL-QADI (2018). “Importance of Heterogeneity in Asphalt Pavement Modeling”

AC layer – Bottom view

×

x y z

std(E11) [με]

0.00 9.00 3.17 6.08 0.25

7.83 με CV 6.58%

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“Micro” approaches

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AV content Aggregate fraction

AC Heterogeneity – “Micro”

Random generator of m icrostructure (MG)

CASTILLO, CARO, DARABI & MASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”

MG

2D specimen shape Gradation Random 2D asphalt concrete microstructure

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AC Heterogeneity – “Micro”

Random generator of m icrostructure (MG)

CASTILLO, CARO, DARABI & MASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”

Random 2D asphalt concrete microstructure

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AC Heterogeneity – “Micro”

Random generator of m icrostructure (MG)

t = 150 s t = 225 s t = 300 s

Pavement Analysis using Nonlinear Damage Approach

DAMAGE DENSITY, Φ

0.00 2.53 1.26 1.89 0.62 CASTILLO, CARO, DARABI & MASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”

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28.11 mm² 10 20 30 40 50 60 70 80 100 150 200 250 300 Damaged FAM area [mm²] Time [s]

NMAS 12.5 mm 4% Air voids

43.00 mm² 10 20 30 40 50 60 70 80 100 150 200 250 300 Damaged FAM area [mm²] Time [s]

NMAS 12.5 mm 7% Air voids

31.89 mm² 10 20 30 40 50 60 70 80 100 150 200 250 300 Damaged FAM area [mm²] Time [s]

NMAS 19.0 mm 4% Air voids

48.50 mm² 10 20 30 40 50 60 70 80 100 150 200 250 300 Damaged FAM area [mm²] Time [s]

NMAS 19.0 mm 7% Air voids x100 specimens x100 specimens x100 specimens x100 specimens

AC Heterogeneity – “Micro”

Random generator of m icrostructure (MG)

CASTILLO, CARO, DARABI & MASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”

x100 x100

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Materials in nature are heterogeneous. Artificial m aterials, even m ore! We need to study and include this heterogeneity as an intrinsic part of our com putational m odels.

In summary…

What did w e do?

We considered AC heterogeneity implicitly/explicitly into our computational models.

Why did w e do it?

Heterogeneity has an important, measurable effect on the variability of material response. This is particularly true for a material as highly heterogeneous as asphalt concrete. Uncertainty data on mechanical properties/response is traditionally scarce.

What can w e do w ith it?

  • Several applications come to mind. In the previous modelling studies we just generated hundreds (!) of specimens, at

random, with a degree of ‘control’ over properties that only a computer can provide. This can be seen as an alternative to complement the laboratory work, which requires resources (effort, time and materials – money). Also, traditional as well as non-traditional materials and mechanical properties can be tested (RAP , aged materials, aggregate shapes). Some sources call this “virtual laboratory”.

  • Apart from the previous modelling studies, it is possible to apply the tools when developing specifications, and for

quality control.

  • We can estimate new data on uncertainty in response, which is difficult or sometimes impossible to obtain in the

laboratory or the field.

  • The tools provide a framework for approaching the modelling of heterogeneity to any existing/new materials; they

have applicability to a variety of infrastructure and building materials.

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Bogotá Urbana-Champaign

  • Prof. Imad

Al-Qadi, PhD

  • Prof. Silvia

Caro, PhD

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College Station Doha

  • Prof. Eyad

Masad, PhD

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Modelling Heterogeneity

Current w ork

Homogeneous plate, 20 GPa

E [GPa]

12.2 26.5 19.3 22.9 15.7

NON-LOCAL EQUIVALENT STRAIN [×10-3]

0.0 8.0 4.0 6.0 2.0 Heterogeneous plate