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, 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
“Macro” approaches
AC Heterogeneity – “Macro” Random fields A random field is a n- dimensional vector of random values that exhibit spatial correlation. -3.23 3.02 Uncorrelated random Correlated random field, Three dimensional random normal numbers. isotropic. field, isotropic C ASTILLO & C ARO (2014). “Effects of air voids variability on the thermo - mechanical response of asphalt mixtures”
AC Heterogeneity – “Macro” Methodology AV [%] Random field of Air Voids 10.07 4.43 E o [MPa] Calculated Linear Viscoelastic properties 11,900 Air Voids LVE Strain ( ε h ) 6200 ε h × 10 -4 Field of horizontal strains ( ε h ) 3.18 -3.08 C ASTILLO & C ARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”
AC Heterogeneity – “Macro” Computational applications AV [%] 1.3 4.7 8.1 11.4 14.8 Air Void content 5.9 % Average AV content 6.45 % AC layer 1 AV 6.68 % AV 6.6 % AC layer 2 AV 7.4 % AV 7.44 % AC layer 3 AV 8.3 % AV 7.06 % AC layer N C ASTILLO & C ARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”
AC Heterogeneity – “Macro” Computational applications x Bottom row Homogeneous layers 1.8E-04 1.6E-04 ε h 1.4E-04 1.2E-04 1.0E-04 98 100 102 104 106 108 110 x [cm] C ASTILLO & C ARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”
AC Heterogeneity – “Macro” Computational applications x Bottom row Heterogeneous layers 1.8E-04 1.6E-04 Response dispersion increases (approx. x8 ) ε h 1.4E-04 1.2E-04 1.0E-04 98 100 102 104 106 108 110 x [cm] C ASTILLO & C ARO (2014). “Probabilistic modeling of air void variability of asphalt mixtures in flexible pavements”
AC Heterogeneity – “Macro” Computational applications E o [GPa] 45.9 39.6 33.3 27.0 × N 20.7 Heterogeneity in the asphalt material (3D RF) Area of moving load application Heterogeneous FE model of y the pavement structure x z C ASTILLO & A L -Q ADI (2018). “Importance of Heterogeneity in Asphalt Pavement Modeling ”
AC Heterogeneity – “Macro” Computational applications y × y z x x z AC layer – Top view × AC layer – Bottom view 7.83 με CV 5.6% 7.83 με CV 6.58% std(E11) [ με ] 0.00 0.25 3.17 6.08 9.00 C ASTILLO & A L -Q ADI (2018). “Importance of Heterogeneity in Asphalt Pavement Modeling ”
“Micro” approaches
AC Heterogeneity – “Micro” Random generator of microstructure (MG) 2D specimen shape Gradation Random 2D MG asphalt concrete microstructure Aggregate fraction AV content C ASTILLO , C ARO , D ARABI & M ASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”
AC Heterogeneity – “Micro” Random generator of microstructure (MG) Random 2D asphalt concrete microstructure C ASTILLO , C ARO , D ARABI & M ASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”
AC Heterogeneity – “Micro” Random generator of microstructure (MG) D AMAGE DENSITY , Φ 0.00 0.62 1.26 1.89 2.53 t = 150 s t = 225 s t = 300 s P avement A nalysis using N onlinear D amage A pproach C ASTILLO , C ARO , D ARABI & M ASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”
AC Heterogeneity – “Micro” Random generator of microstructure (MG) 80 80 x100 NMAS 12.5 mm x100 NMAS 12.5 mm 70 70 7% Air voids 4% Air voids specimens specimens Damaged FAM area [mm²] Damaged FAM area [mm²] 60 60 50 50 x100 40 40 43.00 mm² 28.11 mm² 30 30 20 20 10 10 0 0 100 150 200 250 300 100 150 200 250 300 Time [s] Time [s] 80 80 NMAS 19.0 mm x100 NMAS 19.0 mm x100 70 70 4% Air voids 7% Air voids specimens specimens Damaged FAM area [mm²] Damaged FAM area [mm²] 60 60 50 50 48.50 mm² 40 40 31.89 mm² x100 30 30 20 20 10 10 0 0 100 150 200 250 300 100 150 200 250 300 Time [s] Time [s] C ASTILLO , C ARO , D ARABI & M ASAD (2015). “Studying the effect of microstructural properties on the mechanical degradation of asphalt mixtures”
In summary… What did we do? We considered AC heterogeneity implicitly/explicitly into our computational models. Why did we 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 we do with 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 . Materials in nature are heterogeneous. Artificial materials, even more! We need to study and include this heterogeneity as an intrinsic part of our computational models.
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