LOCAL CALIBRATION OF MEPDG RUTTING MODEL …docs.trb.org/prp/10-0710.pdf · Hoegh, Khazanovich,...

Hoegh, Khazanovich, Jensen

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LOCAL CALIBRATION OF MEPDG RUTTING MODEL FOR MNROAD TEST

SECTIONS

Kyle Hoegh

Research Assistant

University of Minnesota

Department of Civil Engineering

500 Pillsbury Drive S.E.

Minneapolis, MN 55455

Phone: 612-626-4098

Fax: 612-626-7750

E-mail: [email protected]

Lev Khazanovich

Associate Professor

University of Minnesota

Department of Civil Engineering

500 Pillsbury Drive S.E.

Minneapolis, MN 55455

Phone: 612-624-4764

Fax: 612-626-7750

E-mail: [email protected]

Maureen Jensen

Manager, Road Research

Minnesota Department of Transportation

Office of Materials and Road Research

1400 Gervais Avenue

Maplewood, MN 55109

Phone: 651-366-5507

Fax: 651-779-5616

E-Mail: [email protected]

Paper submitted 31 July 2009

in consideration for

Transportation Research Board 89th Annual Meeting, January 10-14, 2010

Words: 4156

Figures: 10

Tables: 2

Photographs: 0

Total Word Count: 7156

TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.


1

LOCAL CALIBRATION OF MEPDG RUTTING MODEL FOR MNROAD TEST

SECTIONS

ABSTRACT

The AASHTO interim Mechanistic-Empirical Pavement Design Guide (MEPDG) was

recently introduced in the United States. Many State agencies have conducted validation

and local calibration of the MEPDG performance prediction models. In this study, time

history rutting performance data for pavement sections at the Minnesota Department of

Transportation (Mn/DOT) full-scale pavement research facility (MnROAD) have been

used for an evaluation and local calibration of the MEPDG rutting model. A detailed

comparison of the predicted total rutting, asphalt layer rutting, and measured rutting is

presented. The paper discusses why a conventional MEPDG model calibration was not

found to feasible and recommends a modification of the rutting model. It was found that

the locally calibrated model greatly improved the MEPDG rutting prediction for various

pavement designs in MnROAD conditions.



2

INTRODUCTION

Rutting is an important distress that is often evaluated to determine rehabilitation and

reconstruction needs in hot mix asphalt (HMA) pavements (1). A rut is a depression in

the wheel path of an HMA pavement, caused by consolidation of the pavement layers

and/or uplift in the pavement adjacent to the rut in the form of shoving. HMA pavements

tend to rut when exposed to hot summer days due to a decrease in asphalt binder

stiffness. Minimizing HMA pavement rutting is important in preventing accidents caused

by hydroplaning (2). The Minnesota Department of Transportation considers 0.5 inch the

level of rutting at which problems become severe (3). The MnROAD database contains

comprehensive performance data for rutting.

In this paper an evaluation and calibration of the rutting performance prediction

capabilities of the recently introduced AASHTO interim Mechanistic-Empirical

Pavement Design Guide Manual of Practice (MEPDG) was conducted for 12 mainline

hot mix asphalt pavement test sections at the Minnesota Department of Transportation

(Mn/DOT) full-scale pavement research facility (MnROAD) (4).

The MEPDG and related software provide capabilities for the analysis and performance

prediction of different types of flexible and rigid pavements. The MEPDG uses

mechanistic-empirical numerical models to analyze input data for traffic, climate,

materials and proposed structure. The models estimate damage accumulation over

service life. Performance predictions are made in terms of pavement distresses. The

MEPDG models were calibrated using the national pavement performance database (4).

After the MEPDG was introduced, verification and local calibrations have been

conducted throughout the United States by various agencies (5, 6, 7, 8, 9, 10). The

research in this study was conducted as part of the Mn/DOT and Local Road Research

Boart-sponsored Local calibration of the MEPDG for Minnesota conditions titled,

“Implementation of The MEPDG for New and Rehabilitated Pavement Structures for

Design of Concrete and Asphalt Pavements in Minnesota (10).” The research approach

for this paper involved the following steps:

• Identify pavement sections with known performance data.

• Obtain the MEPDG inputs that closely represent the asphalt MnROAD sections.

• Run the MEPDG software for flexible test sections to obtain predicted rutting.

• Compare predicted and measured rutting for asphalt test sections.

• Recalibrate rutting model of the MEPDG by adjusting the parameters to reduce

error between predicted and measured performance.

Details and results of the recalibration process for the MEPDG rutting model are

presented below.



3

MNROAD RUTTING DATABASE

The information for the MnROAD mainline sections was used for the MEPDG rutting

model verification and recalibration. The mainline is a 3 ½ mile part of westbound

Interstate Highway 94 that contains 31 test sections and carries an average of 26,400

vehicles daily. All of the collected data is entered into the MnROAD database for

Mn/DOT. The MnROAD database contains information on 12 asphalt mainline sections

(Section numbers 1, 2, 3, 4, 14, 15, 16, 17, 18, 19, 21, 22) that have been subjected to

westbound traffic on I-94 from 1994 to 2003 (see Figure 1). The traffic that the truck and

passing lanes have been exposed to in the time frame of this study is approximately 5

million and 1 million ESALs, respectively.

Figure 1 MnROAD test sections with design details of the asphalt sections, updated

August 2005 (11).

The asphalt sections with various designs were subjected to the same environmental and

traffic loading. Design variables include asphalt binder grades, mix designs, air void

content, drainable layers, crown location, hot mix asphalt (HMA) thickness, base type,

and base thickness, as indicated in Table 1 (12). The aggregate sources for the HMA mix

were consistent for each section and the mix design was also held consistent throughout

the lifts for each section (no difference in base and wear courses). The subgrade is clay

with an R-value of 12.



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Table 1 Design parameters of the measured and predicted rutting sections (MnROAD 2008).

Section

Design

Life

Asphalt

PG

Grade/Type

Asphalt Mix

Design/(Blows)

Thickness

(inch)

Base

Inch /

Class

SubBase

Inch /

Class

Edge

Drains

Subgrade

R-Value

1993-

94

Mean

Air

Void

Percent

2001

Mean

Air

Void

Percent

Change

in Air

Void

%

Paving

Crown

1 5

Year

PG 58-28

(120/150)

Marshall (75) 5.9 33”

Class-

4

-- -- Clay

R-12

6.8% 5.7% -1.1% Quarter

2 Marshall (35) 6.2 4”

Class-

6

28”

Class-4

-- 4.5% 4.0% -0.5% Quarter

3 Marshall (50) 6.2 4”

Class-

5

33”

Class-3

-- 7.2% 4.6% -2.6% Quarter

4 Marshall (75) 9.1 -- -- -- 7.2% 6.0% -1.2% Quarter

14 10

Year

Marshall (75) 11.4 -- -- -- 6.0% 6.1% 0.1% Quarter

15 PG 64-22

(20)

Marshall (75) 11.4 -- -- -- 7.3% 7.1% -0.2% Quarter

16 Gyratory 8.2 28”

Class-

3

-- -- 7.8% 7.6% -0.2% Centerline

17 Marshall (75) 8.2 28”

Class-

3

-- -- 7.7% 6.4% -1.3% Centerline

18 Marshall (50) 8.1 10”

Class-

6

9”

Class-3

Yes 5.8% 5.3% -0.5% Centerline

TRB

2010 Annual M

eeting CD

-RO

MPaper revised from

original submittal.


5

Section

Design

Life

Asphalt

PG

Grade/Type

Asphalt Mix

Design/(Blows)

Thickness

(inch)

Base

Inch /

Class

SubBase

Inch /

Class

Edge

Drains

Subgrade

R-Value

1993-

94

Mean

Air

Void

Percent

2001

Mean

Air

Void

Percent

Change

in Air

Void

%

Paving

Crown

19 Marshall (35) 8.1 28”

Class-

3

-- -- 6.5% 4.6% -1.8% Centerline

20 PG 58-28

(120/150)

Marshall ( 35 ) 7.9 28”

Class-

3

-- -- -- -- -- Centerline

21 Marshall (50) 7.9 23”

Class-

5

-- -- 5.4% 4.4% -1.0% Centerline

22 Marshall (75) 7.8 18”

Class-

6

-- -- 6.4% 6.1% -0.3% Centerline

TRB

2010 Annual M

eeting CD

-RO

MPaper revised from

original submittal.


6

MnROAD rutting measurements were made approximately 3 times per year in the right

and left wheel path in both the truck and passing lanes for each section throughout the

period MnROAD has been open to traffic. Over 1300 truck and passing lane rutting

measurements from the MnROAD database were analyzed. An average of the right and

left wheel path rutting was used for the analysis. It is important to note that Section 20

failed and was rehabilitated with MicroSurfacing (1 Layer - 6 foot rut box filling) in both

lanes, July 1999, and was thus excluded from the analysis.

During the time period the rutting data for this study was collected (1994-2003),

MnROAD used 4 different methods to measure rutting over the life of the project. These

methods included measuring manually with a straightedge, using a PaveTech van

equipped with ultrasonic sensors, using a Pathways vehicle with laser sensors and 3-point

analysis, and using a newer Pathways vehicle with laser sensors and 5-point analysis.

Since the straightedge method was the only method used for the entire period, this

method was chosen to keep the collection method consistent from year to year. In this

method the rutting was measured by MnROAD staff manually using a 6-foot straightedge

with drill bits inserted underneath. By placing the six-foot straightedge on the rut, the

maximum rut depth at the specific location can be found. Figure 2 demonstrates this type

of rut depth measurement. The database used for this study was gathered by taking the

average of multiple rut depth measurements in the section. Two measurements were

taken for each section from 1994 to 1996, then 10 measurements were taken for each

section from 1997 to 2003 (3).

Figure 2 Straightedge method for measuring rut depth (3).



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Measurements of the total rutting do not reveal the level of rutting in the individual layers

of the pavement system. To address this limitation, MnROAD personnel performed

several forensic studies which involved trenching of the flexible MnROAD sections.

Trenches were cut for two-thirds of the asphalt pavement sections in the mainline in

1998, while the remaining 6 sections were trenched in 2001. Detailed information on

these studies can be found in Mulvaney and Worel (3). Both studies indicated that the

majority of rutting only occurred in the upper lifts of the hot mix asphalt, with the

granular base and subgrade mostly unaffected for the sections evaluated in this study.

The forensic evaluation provided useful additional information for MEPDG calibration.

MNROAD SECTION MEPDG PREDICTIONS

MnROAD data is an excellent source for calibration and verification of performance

prediction models. In this study, the rutting performance of the tested sections was also

simulated using the MEPDG software version 1.0. The MEPDG provides several

alternative input levels ranging from the default parameters (Level 3) to the site-specific

data (Level 1). When conducting the simulations, the research team strived to use the

available material and traffic data. Examples of those inputs are shown in Tables 2a

through 2c.

Table 2 (a) Seasonal FHWA vehicle class distribution. (b) Base material

properties. (c) Subgrade material properties.

(a)

FHWA Vehicle Class

Month Class

4

Class

5

Class

6

Class

7

Class

8

Class

9

Class

10

Class

11

Class

12

Class

13

January 0.50 0.75 0.59 0.59 0.46 0.88 0.88 0.88 0.88 0.88

February 0.59 0.79 0.71 0.71 0.50 0.93 0.93 0.92 0.92 0.92

March 0.61 0.87 0.87 0.87 0.61 1.00 1.00 0.97 0.97 0.97

April 0.86 0.93 0.92 0.92 0.73 1.03 1.03 1.10 1.10 1.10

May 1.00 0.99 0.86 0.86 0.83 1.01 1.01 0.98 0.98 0.98

June 1.16 1.06 1.15 1.15 0.98 1.12 1.12 1.00 1.00 1.00

July 1.14 1.14 1.18 1.18 1.23 1.05 1.05 1.06 1.06 1.06

August 1.22 1.37 1.15 1.15 1.47 1.08 1.08 0.94 0.94 0.94

September 1.42 1.23 1.41 1.41 1.68 1.11 1.11 1.10 1.10 1.10

October 1.26 1.05 1.29 1.29 1.11 0.99 0.99 1.08 1.08 1.08

November 0.88 0.92 0.80 0.80 0.60 0.88 0.88 0.94 0.94 0.94

December 0.53 0.86 0.73 0.73 0.49 0.94 0.94 1.07 1.07 1.07



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(b)

Gradation and Plasticity Index

Plasticity Index, PI: 1

Liquid Limit (LL) 6

Compacted Layer No

Passing #200 sieve (%): 7.5

Passing #40 22.5

Passing #4 sieve (%): 77.5

D10(in) 0.00359

D20(in) 0.1992

D30(in) 0.5682

D60(in) 1.815

D90(in) 10.9

(c)

Subgrade Resilient Modulus

at optimum moisture content

13,000 psi

Sieve Percent Passing

#200 7.5

#80 20.8

#40 22.5

#10 62.5

#4 77.5

3/8" 87.5

1/2" 92.5

3/4" 95

1" 97.5

1 ½" 100

However, properties of the asphalt layer were characterized using Level 3 based on the

asphalt binder grade. In the process of the MEPDG version 1.0 software validation, a

bug in the MEPDG software was found. A significant difference was found between the

rutting predictions from level 2 and level 3 inputs, respectively, for the same flexible

pavement and site conditions (see figure 3). The binder behavior predictions from level 2

inputs (see figure 2a) were found to be inconsistent with the input information due to a

bug in the MEPDG software which did not affect level 3 analyses. Based on this

analysis, for the purpose of this study the asphalt mix characterization was limited to

level 3 inputs (see figure 4b). It should be noted that this bug in the MEPDG software

can be easily fixed (El-Basyouny, M., personal communication), so use of levels 1 and 2

can improve the accuracy of the predictions in the future studies (13).



9

0

0.05

0.1

0.15

0.2

0.25

0.3

0 2 4 6 8 10 12

Ru

ttin

g (

in)

Years

Level 3

Level 2

Figure 3 Example case were the rutting vs. time was significantly different for level 2 and

level 3 analysis.



10

(a)

(b)

Figure 4 (a) Level 2 input parameters for asphalt binder (same as Level 1) (b) Level 3

input parameters for asphalt binder.



11

The design guide predicts the rutting due to the subgrade, base, and AC layer. The total

rutting is calculated by summing the rutting in the AC layer, base, and subgrade as shown

in equation 1:

subgradeRuttingbaseRuttingACRuttingRuttingTotal ____ ++= (1)

where Total_Rutting is the predicted surface rutting, Rutting_AC is the predicted rutting

in the asphalt layer only, Rutting_base is the predicted rutting in the base layer only, and

Rutting_subgrade is the predicted rutting in the subgrade only. The measured total

rutting values could then be compared to the predicted rutting values for design guide

simulations of those MnROAD sections with various cross-sections and material

properties, subjected to the same traffic composition (number, type, and weight of

vehicle). The development of rutting with time for both predicted and measured values

was then evaluated in this manner in the truck lane for each section individually as

discussed below.

COMPARISON OF MNROAD SECTION MEASUREMENTS WITH MEPDG

PREDICTIONS

For all the MnROAD pavement sections the predicted total rutting was greater than the

measured rutting. However, a comparison of the predicted asphalt layer rutting with the

measured rutting revealed a more complicated picture. In some sections the predicted AC

rutting was similar to the measured rutting while in some sections the predicted AC

rutting was significantly lower than measured rutting.

Figures 5a and 5b show examples of measured and predicted rutting in the HMA, base,

and subgrade for example sections where the predicted AC rutting is similar to the

measured rutting and where the predicted rutting is lower than the measured rutting,

respectively (sections 1 and 2 respectively).



12

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 2 4 6 8 10 12

Ru

ttin

g (

in)

Pavement Age (years)

Predicted Total Rutting

Measured Total Rutting

Predicted AC Rutting

Predicted Subgrade Rutting

similar measured total

rutting and predicted

AC rutting

(a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 2 4 6 8 10 12

Ru

ttin

g (

in)





Predicted Base Rutting


(b)

Figure 5 (a) Measured and predicted rutting over the pavement age for section 1. (b)

Measured and predicted rutting over the pavement age for section 2.



13

It can be observed in Figure 5a that the predicted HMA rutting is similar to the total

measured rutting for the entire 10 year period. Since the MnROAD forensic studies

showed that most of the measured rutting occurred in the HMA layer, the MEPDG

rutting model is fairly accurate in predicting rutting in the HMA layer, but the MEPDG

subgrade and base rutting models grossly overestimate rutting for the sections where the

AC rutting is similar to the measured rutting. Therefore, the base and subgrade rutting

model for these sections should be excluded from rutting prediction.

However, the previous observations do not hold true for most of the sections. For most

sections, use of only the predicted rutting in the asphalt layer would greatly under-predict

the rutting as shown in Figure 5b. However, use of the MEPDG total rutting model

described in equation 1 would cause gross over-prediction, especially at early age.

Therefore, to avoid under-prediction of the rutting model it is important to account for the

base and subgrade. At the same time, the base and subgrade rutting model should be

modified to avoid over-prediction of rutting, especially during early pavement ages.

Unfortunately, there is not a distinguishable design characteristic between the sections

where the AC rutting predictions are similar to the measured total rutting and the sections

where the AC rutting predictions are significantly lower than the measured total rutting.

As would be expected, sections with differing design parameters showed different

performance trends. However, some sections with very similar design parameters also

show the different performance trends discussed above when looking at the predicted

versus measured rutting (e.g. sections 1 and 2). Further investigation of the base and

subgrade rutting prediction at early pavement age identified a consistently unrealistic

high rutting prediction for the first month of pavement life. As can be observed from

Figure 5b after the first month of a pavement life, section 2 exhibited subgrade rutting of

0.08 inches. However, the predicted rutting after 10 years of pavement life in the

subgrade was 0.18 inches. The cumulative number of heavy trucks after one month and

ten years was 31,000 and 4.7 million, respectively. In the simulation, it was assumed that

the pavement was opened to traffic in August. Although the pavement received less than

1% of heavy truck traffic in the first month of the life of the pavement, it was predicted to

accumulate almost 50% of the subgrade rutting, making it the highest monthly rutting

accumulation during the pavement life. Furthermore, all subsequent rutting accumulation

in August was negligible. Similar observations were made in the base layer predictions.

Therefore it can be concluded that the first month subgrade and base rutting predictions

are unrealistic and should be excluded during analysis.

The MEPDG simulations were made using the nationally calibrated model. Therefore,

the MEPDG manual of practice recognizes a need for the local calibration (4). The

MEPDG software provides an option for modification of the coefficients of the

performance models to account for local conditions. However, because of the

observations presented above, a calibration of the rutting model which involves only an

adjustment of the calibration parameters was not feasible. The following modification of

the MEPDG rutting model is proposed in this study:



14

1___*_

1___*_

*_*___

subgradeRuttingsubgradeRuttingsubgradeRutting

baseRuttingbaseRuttingbaseRutting

subgradeRuttingbaseRuttingACRuttingRuttingTotal

−=

−=

++=

(2)

where, Total_Rutting is the predicted surface rutting, Rutting_AC is the predicted rutting

in the asphalt layer only, Rutting_base* is the modified predicted rutting in the base layer

only, Rutting_subgrade* is the modified predicted rutting in the subgrade only,

Rutting_base is the predicted rutting in the base layer only using the original MEPDG

predictions, Rutting_subgrade is the predicted rutting in the subgrade only using the

MEPDG original predictions. Rutting_base_1 is the predicted rutting in the base layer

after only one month, and Rutting_subgrade_1 is the predicted rutting in the subgrade

after only one month.

Figures 6a and 6b present the predicted rutting for individual layers and total rutting

using equation 2, as well as measured rutting for sections 1 and 2. Comparison of figures

5b and 6b shows that the modified equation (equation 2) improves the prediction of the

total rutting for the entire range of pavement age (especially early age) for section 2.

This trend was observed for most of the sections. As could be expected for the sections

with similar predicted AC rutting and measured total rutting, the total rutting is still

greater than the measured rutting, but a comparison of Figures 5a and 6a show that

equation 2 reduces the discrepancy between predicted total rutting and measured rutting.



15

0

0.1

0.2

0.3

0.4

0.5

0.6

0 2 4 6 8 10 12

Ru

ttin

g (

in)






(a)

(b)

Figure 6 (a) Section 1 predicted rutting using equation 2. (b) Section 2 predicted and

measured rutting versus pavement age using equation 2.



16

To further illustrate predictive improvement of equation 2 versus equation 1, consider

measured and predicted total rutting after 10 years of pavement life. Figures 7a and 7b

show the comparison of measured versus predicted rutting after 10 years of pavement

exposure using equations 1 and 2, respectively. It can be observed that the rutting

predictions using equation 2 are less biased than the predictions using equation 1.

Moreover, equation 1 greatly over-predicted the rutting (> 75% difference) for 4 sections

(sections 1, 3, 16, 17), and over-predicted rutting for all other sections except sections 19

and 21. Equation 2 only slightly under-predicts rutting for sections 19 and 21, fairly

accurately predicts rutting for the remaining sections in group B, and over-predicts the

rutting in group A to a lesser extent than equation 1. The advantages of the modified

equation are even more obvious for prediction after five years (see figure 8). Equation 1

over-predicted rutting for all of the sections (generally greatly over-predicted), while

equation 2 provided much better correspondence with the measured values. Figure 9

shows the measured versus equation 2 predicted rutting levels after 5 and 10 years of

traffic. It can be observed that equation 2 provides a reasonable estimate of the total

rutting in the truck lane for the MnROAD sections evaluated in this study.



17

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Me

asu

red

Ru

ttin

g (

in)

Predicted Rutting (in)

(a)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Me

asu

red

Ru

ttin

g (

in)


(b)

Figure 7 (a) Measured versus predicted rutting after 10 years without subtraction of the

initial base and subgrade rutting jump (using equation 1). (b) 10 year total rutting after

the correction for the initial subgrade and base rutting (using equation 2).



18

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.5 1.0 1.5

Me

asu

red

Ru

ttin

g (in

)


Equation 1 Rutting

after 5 years

Equation 2 rutting

after 5 years

Figure 8 Total rutting predictions from equations 1 and 2 vs measured rutting after 5

years.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Me

asu

red

Ru

ttin

g (

in)


Figure 9 Predicted versus measured total rutting after five and 10 years (using equation

2).



19

As discussed earlier, the rutting was measured in the passing lane as well as the truck

lane. This gives information about the effect of a different traffic mixes on rutting during

the same time period and the same climate. Figure 10 presents the comparison between

measured and predicted rutting using equation 2 for both passing lane and truck lanes

after 5 and 10 years in service. It can be observed that a reasonably good correspondence

is achieved between measured and predicted rutting despite a wide range of design

features, different traffic mixes, and different pavement ages. This suggests that the

modified rutting model provided by equation 2 is an improvement over the original

rutting model for Minnesota conditions.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Me

asu

red

Ru

ttin

g (

in)


Figure 10 Truck and passing lane predicted and measured rutting after 5 and 10 years

(using equation 2).

RECCOMENDATIONS FOR MINNESOTA CONDITIONS

Based on the results of this analysis, the following procedure is recommended for rutting

prediction:

1. Run the MEPDG version 1.0 software. Determine the rutting in each layer at the

end of the design period, and rutting in the base and subgrade layers for the first

month for the 50% reliability level.

2. Using equation 2, determine the total rutting at the end of the design period at the

50% reliability level.

3. Using the output from the design guide, find the rutting corresponding to the

specified reliability.



20

CONCLUSIONS

As specified in the manual of practice there is a need for MEPDG model calibration

based on local conditions. The time history rutting performance data available for 12

pavement test sections at the Minnesota Department of Transportation (Mn/DOT) full-

scale pavement research facility (MnROAD) allowed for a comprehensive evaluation and

calibration of the rutting performance capabilities of the MEPDG for local conditions.

The comparison of the predicted and measured rutting showed the need for a

recalibration of the MEPDG rutting model that did not involve an adjustment of the

calibration parameters as is typically recommended. This was due to the presence of two

groups of predicted rutting behavior with no distinguishable design characteristics

between them. However, forensics showed that the granular base and subgrade was

mostly unaffected for the sections evaluated in this study. Further investigation of the

subgrade and base models showed unrealistically high rutting predictions in the first

month of pavement life. A modified MEPDG model was created to account for the

forensic and predictive evaluations for the local conditions.

The locally calibrated rutting predictions were found to be less biased than the

predictions using the nationally calibrated rutting model. After applying the modified

model for comparison with data collected at a different time, and for data collected in the

passing lane it was concluded that a reasonably good correspondence is achieved. Thus a

procedure to apply the modified rutting model was developed and recommended for use

to improve the rutting prediction over the nationally calibrated model for conditions

similar to those at MnROAD.

ACKNOWLEDGEMENTS

The authors acknowledge the contributions of Dr. Mihai Marasteanu, Raul Velasquez,

and Aishwarya Vijaykumar for their work on the research project “Implementation of the

MEPDG for New and Rehabilitated Pavement Structures for Design of Concrete and

Asphalt Pavements in Minnesota.” that was the basis of this paper. The authors would

also like to acknowledge the work of Ben Worel and Tim Clyne in creating the

MnROAD rutting database that was used in this study.



21

REFERENCES

1. A. Shalaby and A. Reggin. Maximum-Likelihood Classification of Pavement

Surface Condition Data in Urban Road Networks. Transportation Research Board

Annual Meeting 2006 Paper #06-0904.

http://pubsindex.trb.org/document/view/default.asp?lbid=776588. Accessed July

2006.

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