out_3

Road Materials and Pavement Design. Volume 12 – No. 1/2011, pages 37 to 56

Modelling Flow Rutting in In-Service

Asphalt Pavements using the Mechanistic-Empirical Pavement Design Guide

Erik Oscarsson

Department of Technology and Society

Lund University, Sweden

Box 118

221 00 Lund

Sweden

[email protected]

ABSTRACT. Permanent deformation is a serious distress mechanism in asphalt pavements.

Modelling is a tool for performance prediction and deterioration assessment. This study

evaluated the Mechanistic-Empirical Pavement Design Guide (M-E PDG) model for

permanent deformation in asphalt concrete by employing the v1.003 software at level 1 and 3

on two Swedish motorway sections. Dynamic modulus data was obtained using the indirect

tensile test on field cores. Level 1 and 3 results differed heavily due to over-predicted dynamic

modulus data at level 3. However, the M-E PDG national field calibration is currently limited

to that level. Therefore, level 3 appears to be the only usable level until the predictive

dynamic modulus equation and the permanent deformation model have been further

calibrated. Previously found regional field calibration factors derived with accelerated

loading tests improved model accuracy, although national calibration was sufficient.

KEYWORDS: Modelling, Permanent Deformation, Asphalt Pavement, M-E PDG.

DOI:10.3166/RMPD.12.37-56 © 2011 Lavoisier, Paris

38 Road Materials and Pavement Design. Volume 12 – No. 1/2011

1. Introduction

Rutting due to permanent deformation is considered one of the most seriousdistress mechanisms in asphalt pavements. It causes traffic hazards by affectingvehicle steering. Further, an impervious road surface will trap water, snow and icethat cause hydroplaning and loss of friction. Longitudinal cracks often occur in deepruts where they drain free water into the underlying pavement layers, therebyincreasing the deterioration rate. The factors affecting permanent deformations canbe divided into traffic loading, material properties and climatic conditions.

Modelling is a valuable tool used for pavement design and residue assessment. Arecently developed model is the incremental Mechanistic-Empirical PavementDesign Guide (M-E PDG) (NCHRP, 2004), that considers the most importantdistress types such as permanent deformation in bound, unbound and subgradelayers, bottom-up and top-down fatigue cracking and transverse cracking. However,this paper only considers permanent deformation in asphalt concrete (AC) layers,which is also called flow rutting. The software simulation procedure involvesmechanistic calculation of stresses and elastic strains in each sublayer by using theelastic multilayer subprogram JULEA, after which the plastic strains are assessedwith an empirical constitutive equation. The plastic strains in each increment arethen summed up into total permanent deformation (NCHRP, 2004). Someadvantages of the M-E PDG are its mechanistic-empirical framework, itscomprehensive calibration using LTPP data and its user-friendly software.

The M-E PDG concept is currently being implemented in the U.S. and couldprove useful in Sweden as well. Oscarsson (2007) verified the M-E PDG permanentdeformations model for asphalt concrete with data from an accelerated loading testusing Heavy Vehicle Simulator (HVS) applied on two representative Swedishasphalt pavements. The model was considered applicable and regional fieldcalibration factors were derived.

The next step, i.e. the objective of this paper, was to validate the M-E PDGpermanent deformations model using in-service asphalt pavements, which is moredifficult. There are more contributing factors and larger variations to considercompared to accelerated loading tests in which traffic and climate often are keptconstant for simplicity reasons. However, if reliable field data can be produced,simulations using in-service roads have the advantage of being more realistic thanusing accelerated loading tests.

This study is a qualitative field analysis based mainly on measured input datarather than a quantitative regional calibration effort. Two asphalt pavement sectionson the Swedish motorway E6 were modelled using the M-E PDG software v1.003.For asphalt materials, results from using input data accuracy level 1 and 3 werecompared, since they appear to produce different results (Azari el al., 2008), whichmay be caused by over-prediction of the dynamic modulus at level 3 as shown byprevious studies (Dongré et al., 2005; Azari et al., 2007 and 2008; Zeghal et al.,

Modelling Flow Rutting using M-E PDG 39

2008). Further, previously found regional field calibration factors using acceleratedloading testing data on typical Swedish asphalt pavements derived by Oscarsson(2007) was evaluated.

2. Methods and materials

The M-E PDG permanent deformation modelling was carried out using asphaltmaterials data at both level 1 and 3 as they represent the highest and lowest inputdata accuracy levels. Analysis using level 2 asphalt materials data was not carriedout as it is merely a mix of level 1 and 3 elements. All other data were constantlyinput at the highest possible level with respect to the available field sectiondocumentation that were collected by the Swedish National Road Administration(SNRA, 1994) and the Swedish National Road & Transport Research InstituteWiman et al., 1997). Thereby, the possible model result difference using level 1 and3 asphalt materials input data was isolated. First, the model was evaluated using theU.S. national calibration option, which means that regional calibration factors werenot used. Secondly, the previously found regional field calibration factors Er1=0.3,Er2=1.5 and Er3=0.7 derived by Oscarsson (2007) using accelerated loading testingdata from typical Swedish asphalt pavements were employed and evaluated. Further,level 1 measured and level 3 predicted dynamic modulus data were compared usingthe M-E PDG software output data.

2.1. Structure and materials

Permanent deformation modelling was carried out on two test sections of the E6motorway at Fastarp-Heberg, Sweden. The sections were selected due to theirsignificant amount of surface rutting. The relative field measurement error wasthereby minimized. The study focused on specific transversal sections rather thanmean values over distances. In practice, both field measurement and field coring forspecimen production were carried out in two specific transversal sections. Therefore,the measured rut depth and material properties in a specific section are notnecessarily representative for all sections on the distance.

The semi-rigid pavement (CBÖ) section 9:22, presented in Figure 1, wasconstructed with a 42 mm Stone Mastic Asphalt (SMA) wear layer, 60 mm ABTdense-graded asphalt concrete binder layer, 255 mm cement stabilized base layer, 65mm unbound base layer, 640 mm crushed mineral aggregate sub base layer, 400 mmcoarse-grained subgrade layer and a semi-infinite layer of fine-grained subgrade.The flexible pavement (GBÖ) section 10:7 was constructed with a 35 mm SMAwear layer, 187 mm AG dense-graded asphalt base layer, 88 mm unbound baselayer, 704 mm crushed mineral aggregate sub base layer, 400 mm coarse-grainedsubgrade layer and a semi-infinite layer of fine-grained subgrade. Further details onthe E6 Fastarp-Heberg test sections are provided by Wiman et al. (1997).


Figure 1. The layer structure of the test sections

2.1.1. Asphalt concrete layers

The properties of the asphalt concrete and its components according to SNRA(1994) and Wiman et al. (1997) are presented in Table 1. Poisson’s ratio functionsfor asphalt materials were assessed using the level 1 function within the M-E PDGsoftware limits.

The asphalt concrete dynamic compression modulus data used as M-E PDGinput were derived using the indirect tensile test (IDT). The standard uniaxialdynamic modulus test using laboratory compacted specimens was not feasible as noadditional asphalt mix was available. Further, 150 mm thick field cores could not beextracted from all layers due to limited layer thicknesses. Therefore, alternativetesting methods were considered. Kim et al. (2004) developed the IDT dynamicmodulus test and compared it with the uniaxial test. The comparison was carried outusing gyratory compacted specimens comprising 12 different asphalt mix types. Thedata was used in a statistical analysis since they showed some variation. The unequalvariance t-test was performed for each mixture at five reduced frequencies. Theresults showed that the data sets were statistically different in only 10% of the tests.Therefore, IDT was considered applicable as a substitute for the uniaxial test. In thisstudy, the uniaxial test using gyratory compacted H150xD100 mm specimens wasreplaced with the IDT test using H40xD150 mm field-cored specimens.


Table 1. Properties of the asphalt concrete layers and its components

Mix nameSMA

wear course

ABT asphalt

binder course

AG asphalt

base course

Mix typeStone mastic

asphaltDense-graded

hot mix asphaltDense-gradedasphalt base

Max. aggregate size 16 mm 16 mm 22 mm

Cumulative retained mass on

19.05 mm (3/4 in) sieve3% 3% 16%

Cumulative retained mass on

9.53 mm (3/8 in) sieve58% 36% 42%

Cumulative mass retained on

4.75 mm (#4) sieve74% 57% 57%

Mass passing 0.075 mm (#200)

sieve8% 9% 5%

Bitumen softening point at 13000 P 47 °C 47 °C 38 °C

Bitumen absolute viscosity at 60 °C

(140 °F)1750 P 1750 P 601 P

Bitumen kinematic viscosity at

135 °C (275 °F)377 cS 377 cS 202 cS

Bitumen specific gravity at 25 °C

(77 °F)1.02 t/m3 1.02 t/m3 1.02 t/m3

Bitumen penetration at 25 °C

(77 °F)85 (70/100)

dmm85 (70/100)

dmm190(160/220)

dmm

Bitumen Brookfield viscosity N/A N/A N/A

Mix reference temperature 21.1 °C 21.1 °C 21.1 °C

Mix effective binder content 6.2% 5.8% 4.2%

Mix air voids 4.8% 4.5% 5.2%

Mix total unit weight 2.31 t/m3 2.32 t/m3 2.32 t/m3

Mix thermal conductivity 0.35 mJ/h-m-°C0.35 mJ/h-m-

°C0.35 mJ/h-m-°C

Mix heat capacity 0.18 mJ/kg-°C 0.18 mJ/kg-°C 0.18 mJ/kg-°C

Four 150 mm full-depth field cores were obtained from each of the semi-rigidand flexible test sections. One specimen was cut from the upper portion of eachlayer in each core. This resulted in four specimens per layer type. The stone masticasphalt (SMA) wearing layer of the flexible section cores was rejected due to anaverage thickness of less than 35 mm. Instead, the material test results from thetheoretically identical semi-rigid section SMA specimens were used for bothsections. The two sections were subjected to the same traffic load and climate


because they were located less than 1.5 km apart on a motorway with no junctionsbetween (Wiman et al., 1997). In addition, falling weight deflectometer data frommeasurements carried out on the unbound base layer surfaces were similar. Finally,both SMA layers were produced according to a single specification. However, thesections differed regarding the asphalt binder and cement-treated base layers in thesemi-rigid section compared to the thick asphalt base layer in the flexible section.Nonetheless, the differences were considered small enough to allow characterizationof both SMA layers using semi-rigid section SMA specimens only.

Three replicate specimens from each layer type were subjected to indirect tensilehaversine loading cycles at the temperatures -10 °C, 5 °C, 20 °C and 35 °C. At eachtemperature, a frequency sweep was carried out using the frequencies 25, 20, 10, 5,2, 1, 0.5, 0.2, 0.1 and 0.01 Hz. At each frequency, ten preconditioning pulses wereapplied, followed by ten test pulses. In order to minimize the risk of excessivedeformation and invalid test results, no 0.01 Hz test at 35 °C was carried out.Further, a few invalid data points were removed because of their Poisson’s ratiosexceeding 0.5, which indicates specimen damage (Kim et al., 2004). Thetemperature accuracy was ±0.5 °C measured with a thermometer integrated in adummy specimen. The strain level range was 40-60 microstrain in order to receive aclear response signal while remaining within the linear viscoelastic range.

The dynamic modulus data was idealized using the sigmoidal function andrecalculated with respect to compaction method, aging and damage. The M-E PDGrequires data up to 55 °C (131 °F) although only data up to 35 °C (95 °F) wereavailable. Therefore, the sigmoidal function was used for extrapolation. Thisprocedure should not introduce any significant error due to the validity of the time-temperature superposition principle. Further, the pavement surface temperature atthe test site is above 35 °C less than 0.02% of the time (Oscarsson and Said, 2010).

The M-E PDG is based on dynamic modulus data using gyratory compacted,short-term aged specimens (NCHRP, 2004), while the test specimens were coredfrom 12 years old in-service pavement layers. In order to consider compactiondifferences, long-term aging and damage effects, the AG asphalt base dynamicmodulus data were compared with the corresponding IDT data derived using equallycomposed specimens produced according to M-E PDG specifications by Oscarsson(2007). At each temperature and frequency, the dynamic modulus ratio|E*field specimens|/|E*laboratory specimens| was calculated. The compaction differences, long-term aging and damage effects appears to reduce dynamic modulus when low andincrease it when high, as can be seen in Figure 2. The ratios derived with AG asphaltbase specimens were then used to adjust the ABT binder and SMA wear layerdynamic modulus data accordingly, as presented in Table 2. It should be noted thatthe methodology assumes that the dynamic modulus of all layers are equallyaffected by compaction differences, long-term aging and damage effects, regardlessof depth, air void content and binder properties. Figure 3 illustrates the range of testdata and the sigmoidal function for each mix type.


0,6

0,8

1

1,2

1,4

1,6

0 2000 4000 6000 8000 10000 12000 14000 16000

IE*labl [MPa]

IE* f

ield

l /

IE* l

abl

[MP

a]

Figure 2. Dynamic modulus ratio

Table 2. The adjusted dynamic modulus input data [MPa]

f [Hz]

Layer T [°C] 0.1 0.5 1 5 10 25

-10 7523 9647 10459 12049 12607 13236

5 2205 3879 4759 6984 7946 11485

20 397 762 1024 2004 2620 3619

35 128 181 217 360 463 662

SMAwearinglayer

55 75 85 90 108 118 135

-10 7907 9617 10259 11513 11954 12455

5 1870 3236 3969 5884 6743 7853

20 376 677 886 1652 2133 2922

35 167 250 308 531 687 979

ABTbinderlayer

55 106 139 160 241 298 405

-10 8003 10079 10934 12752 13449 14282

5 2369 3727 4439 6327 7208 8399

20 599 1020 1283 2143 2637 3413

35 212 330 407 681 857 1161

AGssphaltbaselayer

55 98 129 148 215 259 336


Figure 3. Dynamic modulus test data and their sigmoidal functions

2.1.2. Cement stabilized base layer

The compressive yield stress of the cement stabilized base layer in the semi-rigidsection was stronger than planned (Wiman et al., 1997). The M-E PDG resilientmodulus input was derived at level 2 (NCHRP, 2004) using Equation [1]:

[1]

The compressive strength fc’=18.3 MPa reported by Wiman et al. (1997) wasgeometrically adjusted from Kango cube (Andersson, 1987) results to AASHTO(2007) T22-07 standard results using Swedish standards (2005) S 137207conversion factors. Equation [1] resulted in a reasonable resilient modulus of 17400MPa (2530 ksi), which is 26% higher than the default value. Default minimumresilient modulus and modulus of rupture were also adjusted accordingly, i.e. 869MPa (126 ksi) and 5661 kPa (821 psi), respectively.

2.1.3. Unbound layers

For both pavements, the unbound base layer, the crushed mineral aggregate subbase layer and most of the coarse-grained subgrade layer were joined in a single1054 mm (41.5 in) A-1-a layer according to the AASHTO (2007) M145-91 soilclassification. As the thick pavement sections reached the software’s maximumallowed number of sublayers (NCHRP, 2004), a part of the coarse-grained materials,i.e. 51 mm (2.0 in) for the semi-rigid section and 138 mm (5.4 in) for the flexiblesection, had to be merged with the semi-infinite A-5 layer characterizing the fine-grained subgrade. The necessary simplification should not significantly affectresults, as doubling the error only resulted in 0.003 mm and 0.03 mm extra rut depthin 10 years for the semi-rigid and the flexible section, respectively. The unboundmaterials were characterized using the M-E PDG representative default values atlevel 3 due to a lack of measured data. Consequently, a Poisson’s ratio of 0.35 and acoefficient of lateral pressure of 0.5 were used for both pavements. The default

10

100

1000

10000

100000

-10 -8 -6 -4 -2 0 2 4 6 8 10

log fred [Hz]

lE*l

[M

Pa

]

SMA measured data

SMA sigmoidal function

ABT measured data

ABT sigmoidal function

AG measured data

AG sigmoidal function

'57000 cfE


software resilient modulus was 248 MPa (36 ksi) and 83 MPa (12 ksi) for the A-1-aand A-5 materials, respectively (NCHRP, 2004).

2.2. Traffic

Traffic properties were input at level 1. The number of vehicles was assessedusing pneumatic tubes or inductive loops mounted on the road surface, while theheavy axle load spectrum was characterized using Weigh-In-Motion (WIM) datafrom SNRA (2008a) as described by Winnerholt (2004). The available one-weekearly summer WIM measurements were carried out in 2004 near Kungsbacka and2005-2007 near Löddeköpinge. These locations are 95 km north and 130 km southfrom test sections, respectively. The heavy vehicle load spectra in Kungsbacka andLöddeköpinge were considered similar enough to allow interpolation of data.

The number of heavy vehicles was directly measured in the southbound rightlane using pneumatic tubes or inductive loops by SNRA (2008b). However, as theequipment erroneously included light vehicles with long axle spacing, the resultswere adjusted using the more detailed WIM data. The resulting initial two-wayheavy vehicle average annual daily truck traffic (AADTT) of 1559 heavy vehiclesper day and the mean compound growth was 5.20%. The mean operational speedcalculated using WIM data was found to be constant at 88.7 km/h (55.1 mph), aspresented in Table 3.

Table 3. General traffic input data

Initial two-way heavy vehicle traffic 1559 vehicles/day

No. of lanes in design direction 2

Percent trucks in design direction 50%

Percent trucks in design lane 100%

Operational speed 88.7 km/h (55.1 mph)

Compound growth 5.20%

M-E PDG uses the assumption of constant axle load spectrum from year to year,over the week and throughout the day (NCHRP, 2004). In this study, the axle loaddistributions were also assumed constant throughout each year since each WIMmeasurement covered only one week. The assumption appears reasonable asLu et al. (2002) reported very small seasonal load spectra variation in Californiadespite the addition of agricultural transports during harvest time. Therefore, defaultmonthly adjustment factors were used. The distribution of heavy vehiclesthroughout the day was assessed using the WIM data presented in Figure 4.


Figure 4. Distribution of heavy vehicles throughout the day

The heavy vehicles of the interpolated WIM data were classified according to theFWHA vehicle classification (NCHRP, 2004). Separation of certain infrequentvehicle types was difficult and offered limited benefits. Therefore, class 4 vehicleswere absorbed by classes 5 and 6 while class 10 vehicles joined class 12. Thedistribution of heavy vehicles into vehicle classes is shown in Table 4, where alsothe average number of axles per truck is defined.

Table 4. The class distribution of heavy vehicles and number of axles per truck

Number of axles per truckVehicle class

Distribution of

heavy vehicles Single Tandem Tridem

4 0.00% 2 0 0

5 19.1% 2 0 0

6 11.3% 1.7 0.65 0

7 0.5% 0.76 0.64 0.66

8 6.3% 2 1 0

9 27.0% 1.96 0.09 0.96

10 0.00% 1.33 3.34 0

11 10.1% 3.56 0.45 0

12 11.1% 2.12 1.24 0.47

13 14.6% 2.23 2.09 0.25

Axle load distribution factors for each vehicle class based on WIM data arepresented in Figure 5. The typical single axle range was 25-100 kN, 50-200 kN fortandem axles and 50-250 kN for tridem axles. Quad axles were not used since thefew WIM recorded quad axles were approximated as tridem axles.

0%

1%

2%

3%

4%

5%

6%

7%

0 6 12 18 24

Hour of the day

Ho

url

y d

istr

ibu

tio

n


Figure 5. Axle load distribution factors for single axles (1A), tandem axles (2A) and

tridem axles (3A) in each vehicle class (VC)

The used axle configuration data presented in Table 5 were default values(NCHRP, 2004). Assessment of the mean wheel location was based on the designlane width and the average axle width. The lateral wander average standarddeviation was estimated using the lane width and results by Buiter et al. (1989).

Table 5. Axle configuration and lateral traffic wander input data

Average axle width (edge-to-edge outside dimensions) 2.59 m (8.5 ft)

Dual tire spacing 0.30 m (12 in)

Tire pressure 828 kPa (120 psi)

Tandem axle spacing 1.31 m (51.6 in)

Tridem axle spacing 1.25 m (49.2 in)

Quad axle spacing 1.25 m (49.2 in)

Mean wheel location from the lane marking [in] 0.46 m (18 in)

Traffic wander standard deviation [in] 0.29 m (11.4 in)

Design lane width [ft] 3.50 m (11.5 ft)

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350

Axle load [kN]

Acc

um

ula

ted

dis

trib

uti

on

1A, VC 51A, VC 61A, VC 71A, VC 81A, VC 91A, VC 111A, VC 121A, VC 132A, VC 62A, VC 72A, VC 82A, VC 92A, VC 112A, VC 122A, VC 133A, VC 73A, VC 93A, VC 123A, VC 13


2.3. Climate

An integrated climatic model (icm) file at input accuracy level 1 was createdusing hourly climatic data (hcd) from the Swedish Meteorological and HydrologicalInstitute (SMHI, 2008) and the Swedish Road Administration (SNRA, 2008c). Theclimatic data time interval comprised January 2003 to February 2006,i.e. 38 months, as compared with the M-E PDG minimum requirement of 24 months(NCHRP, 2004).

The ground water depth, calculated as the mean value of the nearby Halmstadand Oskarström measurements (SGU, 2008), was constant throughout the year. Thedaily solar radiation maximum and sunrise/sunset times were modelled as a functionof latitude (NCHRP, 2004). Air temperature, wind speed and relative humidity wereprovided by hourly on-site measurements (SNRA, 2008c). Precipitation wasapproximated by accumulated 6 h data from Halmstad distributed hourly accordingto Helsingborg 1 h data. The sunshine percent was approximated using Växjö dataas that was the only nearby and complete sunshine data set (SMHI, 2008). Asummary of the climate input data is provided in Table 6.

Table 6. Climate input data summary

Longitude, latitude 12.46E, 56.47N

Elevation 18 m (60 ft)

Annual water table depth 2.2 m (7.2 ft)

Mean annual temperature 7.2 °C (45 °F)

Number of freezing degree days 420

Annual rainfall 806 mm (31.7 in)

Average relative humidity 81%

Average wind speed 3.5 m/s (7.7 mph)

Solar radiation minimum 825 W/m2 (261 BTU/ft2-h)

Solar radiation maximum 10700 W/m2 (3380 BTU/ft2-h)

2.4. Field measurement

The transversal surface profile was measured basically twice per year in Apriland October from 1997 to 2006 using a PRIMAL profilometer (Wiman et al., 2009).Figure 6 shows the total rut depth data derived from the measured profile data usingthe wire line method. M-E PDG rut depth results can be compared directly to thesemeasurements as they were calibrated using LTPP data derived with the same


method (NCHRP, 2004). However, during winter Swedish asphalt pavementsexhibit considerable wear rutting due to studded tires as reported by Wiman et al.

(2009), while M-E PDG show virtually no rutting increment. Therefore, the total rutdepth data was adjusted for wear rutting using additional laser profilometermeasurement data (Wiman et al., 2009), which resulted in the rut depths presentedin Figure 7. Again, it should be noted that the specific sections selected for thisstudy are not necessarily representative for the layer structures and materials.Comparisons between observation and model results were not carried out after aspecific time. Instead, the comparison was carried out using mean values of allobservation points in order to account for measurement variation.

Figure 6. Total observed rut depth in

the semi-rigid and flexible test

sections

Figure 7. Observed rut depth in the

semi-rigid and flexible test sections

adjusted for wear rutting

3. Results

3.1. Simulation using the national calibration option

Simulations using the national calibration option at level 1, presented inFigures 8 and 9, showed a 135% and 38% mean over-prediction in total rut depth forthe semi-rigid and flexible sections, respectively. At level 3, the model over-predicted the semi-rigid section by 18%, while the flexible section was under-predicted by 22% as illustrated in Figures 10 and 11. Level 3 results appeared tocorrespond better to observation than level 1 results.

It was also noted that the modelled contribution from unbound base andsubgrade layers compared to the total observed deformation was very high evenafter ten years of service. At level 1 it was 43% and 57%, and at level 3 it was 39%and 46% for the semi-rigid and flexible sections, respectively. In Sweden, thatshould be regarded as high considering the thick layers of asphalt concrete coveringthe high quality crushed rock material layers (Wiman et al., 2009).

0

2

4

6

8

10

12

14

16

18

20

1996-01-01 2000-01-01 2004-01-01 2008-01-01

Date

Ru

t d

ep

th [

mm

]

Semi-rigid section

Flexible section

0

2

4

6

8

10

12

14

16

18

20

1996-01-01 2000-01-01 2004-01-01 2008-01-01

Date

Ru

t d

ep

th [

mm

]

Semi-rigid section

Flexible section


Figure 8. Rut depth in the semi-rigid

test section using national calibration

at level 1

Figure 9. Rut depth in the flexible test

section using national calibration at

level 1



at level 3

Figure 11. Rut depth in the flexible


at level 3

3.2. Simulation using previously found calibration factors

Permanent deformation modelling at level 1 for the field sections usingcalibration factors derived by accelerated testing suffered from heavy over-predictions by 245% and 82% for the semi-rigid and flexible sections as presented inFigures 12 and 13. At level 3, the model over-predicted the semi-rigid section by47%, while the flexible section was under-predicted by 9% as illustrated inFigures 14 and 15. Again, level 3 results appeared to correspond better toobservation than level 1 results. The previously noted considerable contribution ofmodelled permanent deformations from unbound base and subgrade layers toobserved total rut depth was, as expected, not affected by use of regional fieldcalibration factors for asphalt concrete layers.



test section using calibration constants

from HVS test at level 1

Figure 13. Rut depth in the flexible test

section using calibration constants from

HVS test at level 1


test section using calibration constants

from HVS test at level 3

Figure 15. Rut depth in flexible test

section using calibration constants from

HVS test at level 3

3.3. Comparison of predicted and measured dynamic modulus data

Comparison of level 1 measured and level 3 predicted dynamic modulus datashowed heavy dynamic modulus over estimations by the predictive equation, asillustrated in Figures 16 and 17. Dynamic modulus data, derived with the M-E PDGsoftware, is presented monthly during a normally tempered year, i.e. at the 3rdtemperature quintile. All dynamic modulus values are relatively high due to the highloading frequency at motorway speed. The ratio of |E*level 3|/|E*level 1| in the semi-rigid section ranged from 1.95 to 3.05 with a mean value of 2.36, while the flexiblesection ratio ranged from 1.95 to 3.52 with a mean value of 2.61. The results weresimilar in all sublayers that were located at different depths in the pavement.


Figure 16. Comparison of M-E PDG

predicted and measured dynamic

modulus data in AC sublayers at

different depths in the semi rigid

pavement section

Figure 17. Comparison of M-E PDG

predicted and measured dynamic

modulus data in AC sublayers at

different depths in the flexible avement

section

4. Discussion

The difference in permanent deformation results using material input data atlevel 1 and 3 can only be explained by material properties, as traffic and climatewere input at level 1 in both cases. The main difference between level 1 and 3material input data is the use of the M-E PDG predictive dynamic modulus equation,which is sometimes referred to as the Witczak predictive equation. In this study, thepredicted dynamic modulus was generally found to be more than twice the measureddynamic modulus. However, the results of this paper relied on a number ofassumptions. First, all dynamic modulus data of the long-term aged, field compactedand possibly damaged specimens were recalculated according to the dynamicmodulus derived by Oscarsson (2007) using IDT with gyratory compacted AGasphalt base specimens. Second, the dynamic modulus data derived with the IDTwas assumed similar to that of the uniaxial test, as statistically shown by Kim et al.

(2004). Thirdly, the number gap-graded mixes included in the M-E PDG calibrationwas very limited (NCHRP, 2004). Therefore, NCHRP (2004) recommends that theSMA materials should primarily be characterized at input level 1 only. Therelevance of comparison between level 1 and 3 dynamic modulus is thereby limitedfor SMA layers, i.e. the three first sublayers in Figure 16 and 17. However, both theABT and AG materials show the same behaviour.

Previous studies have reported that the predictive dynamic modulus equationtends to over-predict at low values, i.e. at high temperature and/or low loading


frequency. Dongré et al. (2005) found that although the model results werereasonable at low temperatures, it should not be used at high temperatures, i.e. whenthe dynamic modulus is below 689 MPa (100 ksi). Using a variety of binders andcompaction methods, Azari et al. (2007) also concluded that high moduluspredictions are reasonable but that at low modulus values the results tend to be over-predicted as illustrated in Figure 18, which can be compared with the results of thisstudy in Figures 16 and 17. Based on a wide selection of mix types, Zeghal andElHussein (2008) found that the predictive model under-predicted at lowtemperatures and over-predicted at high temperatures, especially at low loadingfrequency. In order to minimize the difference between level 1 and 3 permanentdeformation results, this study recommends that the dynamic modulus predictionmodel should be further refined, especially at high temperature and low loadingfrequency.

Figure 18. Comparison of measured and NCHRP 1-37A–predicted |E*| values for

various binders (Azari et al., 2007)

The M-E PDG permanent deformations model results appeared to produce betterpredictions at level 3 than at level 1 when the opposite should theoretically be true.However, as Azari et al. (2008) suggested, the model is indeed nationally calibratedusing material input data at level 3 according to the M-E PDG documentation inAppendix EE (NCHRP, 2004). As previously discussed, the predictive dynamicmodulus equation used for level 3 calibration has been found to over-predict at hightemperature and low loading frequency, which are the conditions under whichpermanent deformations typically occur. Therefore, there is a considerable risk thatthe level 3 permanent deformation model calibration is based on over-predicteddynamic modulus data. If so, the nationally calibrated model must be over-sensitive


in order to produce results corresponding to field observations in the long-termpavement performance (LTPP) database. That would result in over-prediction whenusing measured dynamic modulus input data at level 1, which explains the results ofAzari et al. (2008) and this study. Further, it appears logical to use the model onlyup to the accuracy level it is calibrated for. Therefore, this study recommends use ofmaterial input data at level 3 only, until further calibration efforts have been carriedout.

The regional calibration factors Er1=0.3, Er2=1.5 and Er3=0.7 derived byOscarsson (2007) somewhat increased the modelled permanent deformations.Level 1 analysis generally resulted in over-predictions, while level 3 correspondedfairly well with observations. The modelled contributions from unbound base andsubgrade to total observed permanent deformation were larger than expected in therobust test sections. The contribution of the M-E PDG permanent deformationmodels to total rut depth is calibrated using a trench study comprising only sevenMnRoad pavement sections. The well-known need for further calibration based ontrench data (NCHRP, 2004; NCHRP, 2006; Oscarsson, 2007) is thereforeemphasized.

5. Conclusions

Based on material testing and modelling permanent deformations in a semi-rigidand a flexible in service pavement section using the M-E PDG software v1.003, thefollowing conclusions were drawn:

– According to this study, material data for permanent deformation modellingshould only be input at level 3 until calibration at higher levels has been carried out.

– Before performing further calibration, the predictive dynamic modulusequation used at level 2 and 3 should be refined in order to reduce model resultdifferences between the three input levels.

– Use of the regional field calibration factors Er1=0.3, Er2=1.5 and Er3=0.7,previously found by accelerated testing, yielded reasonable results, as did nationalcalibration.

– The modelled distribution of permanent deformation between layers should befurther calibrated using additional trench studies.

Acknowledgements

The financial support provided by the Swedish National Road Administrationand the Development Fund of the Swedish Construction Industry is gratefullyacknowledged. Further, the project relied on supervision and technical support byDr. Safwat Said at the Swedish National Road and Transport Research Institute,Dr. Monica Berntman at Lund University and Dr. Richard Nilsson at SkanskaTeknik.


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Received: 25 May 2009Accepted: 30 July 2010

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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