TRB Paper#16-1668 Influence of MEPDG Unbound Material …docs.trb.org/prp/16-1668.pdf · TRB...

TRB Paper#16-1668

Influence of MEPDG Unbound Material Type and Material Characterization

Input Level on Pavement Performance

Ragaa Abd El-Hakim, Ph.D.

Assistant Professor, Tanta University Public Works Eng. Dept., Faculty of Engineering

Tanta University, Tanta, Egypt, PH +201008258088

E-mail: [email protected]

Sherif M. El-Badawy, Ph.D. (corresponding author)

Associate Professor, Mansoura University

Public Works Eng. Dept., Faculty of Engineering

Mansoura University, Mansoura, Egypt.

PH +201000183519


Alaa R. Gabr, Ph.D.

Assistant Professor, Mansoura University



PH +201003822251


Abdelhalim M. Azam, Ph.D.

Assistant Professor, Mansoura University



PH +201004833460


Sponsoring Committee: Standing Committee on Mineral Aggregates (AFP70)

95th Annual Meeting of the Transportation Research Board

Washington D.C., USA

January 22-26, 2012

Total Number of Words: 4728 (Text including Abstract) + 500 (2 Figures) + 1250 (5 Tables) = 6478

Original Submission Date: July 27, 2015

Re-submitted: October 28, 2015

mailto:[email protected]




Abd El-Hakim, El-Badawy, Gabr, and Azam 2

ABSTRACT 1 2

For material characterization, the Mechanistic Empirical Pavement Design Guide (MEPDG) has 3

3 input levels. Level 1 input values must be obtained through direct laboratory testing. Level 2 4

inputs are determined through correlations with other material properties. Finally, Level 3 inputs 5

are simply typical default values. The level chosen for each design input parameter, however, 6

may have a significant effect on project design, cost, performance, and reliability. In this paper, 7

the influence of the unbound granular base material type and material characterization input level 8

on pavement performance in different climatic conditions in Egypt as predicted by MEPDG for a 9

typical flexible pavement section was investigated. For this purpose, analyses were made for 10

seven types of virgin and recycled base materials at three weather stations representing different 11

climatic regions in Egypt: Alexandria, Cairo and Aswan. For the typical pavement system, level 12

1 data for the resilient modulus based on measured laboratory values (k1, k2, and k3 elastic 13

response coefficients) were used for the first set of MEPDG simulation runs. The second set of 14

computer simulation runs were conducted using level 2 data for the investigated materials which 15

are the California Bearing Ratio (CBR) values. The final set of runs, utilized default resilient 16

modulus values for the investigated materials based on the AASHTO class (level 3). MEPDG 17

predicted pavement distresses and roughness for the three input levels, seven base materials were 18

compared, and the results showed significant variation in predicted performance due to the 19

change in the input level and material type. 20

21

Key Words: Resilient Modulus, CBR, MEPDG, Performance, Climate, Input Level 22

23

24


INTRODUCTION 1 2

Resilient modulus (Mr) of the unbound materials and subgrade soils is an important engineering 3

parameter for the mechanistic and empirical pavement design methods. It is an indication of the 4

elastic behavior as well as the load carrying ability of pavement materials under cyclic traffic 5

loads. The resilient modulus depends on various factors such as deviator stress, density, and 6

moisture content. Several literature studies reported that the most influential soil index properties 7

with respect to resilient modulus were moisture content, degree of saturation, material passing 8

US sieve # 200, liquid limit, plasticity index, and density(1, 2). Thompson and LaGrow (3) 9

proposed a simple Mr predictive model for conventional flexible pavement design purposes as 10

shown in Equation 1 (3). Woolstrum (4) developed a mathematical model (Equation 2) to 11

estimate the resilient modulus from the soil group index based on the soil classification. 12

Furthermore, a range of typical resilient modulus values for different subgrade types was found 13

in the literature (5). 14

𝑀𝑟 = 4.46 + 0.098𝐶 + 0.119 𝑃𝐼 (1) 15

𝑀𝑟 = 100[𝐵0 + 𝐵1(𝐺𝐼) + 𝐵2(𝐺𝐼2) + 𝐵3(𝐺𝐼3) + 𝐵4(𝐺𝐼4)] (2) 16

Where: 17

Mr = Resilient modulus at optimum moisture content (OMC) and 95% relative 18

compaction 19

C = Less than 2 micron clay content (%) 20

PI = Plasticity index (%) 21

GI = Group index 22

B0, B1, B2, B3, B4 = regression parameters 23

Some correlation equations have been reported to estimate the resilient modulus from California 24

Bearing Ratio (CBR) and R-value. Equation 3 proposed by Heukelom and klompand and 25

Equation 4 developed by the Asphalt Institute exemplify such correlations (6, 7). 26

Mr (psi) = 1500 CBR (3) 27

Mr (psi) = A + B (R value) (4) 28

Where: 29

A = 772 to 1155 30

B = 369 to 555 31

R value = Resistance value, % 32

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been developed to update the 33

1993 American Association of State Highway and Transportation Officials (AASHTO) for 34

pavements design, which was first released in 2004 and approved by AASHTO in 2008 (8). The 35

production version of the MEPDG software is called AASHTOWare Pavement ME-Design. 36

MEPDG offers the computation of the structural responses (stresses, strains, and deflections), 37

within a pavement system, using the pavement response model JULEA multi-layer elastic 38

analysis (MLEA) or the finite element code DSC2D for flexible pavements (8, 9). The MEPDG 39

software also allows the Enhanced Integrated Climatic Model (EICM) for calculating the 40

moisture and temperature variations within the pavement structure. Then, the pavement 41

distresses (i.e., rutting, cracking, and roughness) can be predicted via empirical models from the 42

mechanistically computed strains and deformations. 43


In the MEPDG software, there are three hierarchical input levels for the materials and traffic 1

based on the importance of the project and the availability of data (8, 9). For the unbound 2

material characterization, the resilient modulus is the major parameter required to express the 3

material strength. For level 1 input, resilient modulus should be determined through laboratory 4

testing to get the k1, k2, and k3regression constants by the nonlinear optimization of the universal 5

model shown in Equation 5 using the testing results (8). 6

𝑀𝑟 = 𝑘1𝑝𝑎 (𝜃

𝑝𝑎)

𝑘2

(𝜏𝑜𝑐𝑡

𝑝𝑎+ 1)

𝑘3

(5) 7

Where: 8

Mr= Resilient modulus (psi) 9

= Bulk stress =σ1+σ2+σ3 10

σ1 = Major principal stress 11

σ2 = Intermediate principal stress 12

σ3 =Minor principal stress 13

τoct= Octahedral shear stress =√(𝜎1−𝜎2)2+(𝜎1−𝜎3)2+(𝜎2−𝜎3)2

3 14

pa=Atmospheric pressure = 101 kPa (14.7 psi) 15

k1, k2, k3= Regression constants 16

Hossain et al. (10) concluded that the universal model was the best irrespective of aggregate type 17

that satisfied the MEPDG requirement for the value of coefficient of correlation (R2). They also 18

recommended that the universal model should be used for level 1 in the MEPDG. 19

20

Level 2 is the case where resilient modulus can be determined through the correlations with 21

some other material properties e.g., CBR or R-value. MEPDG uses Equation 6 to compute Mr 22

from the CBR (9). 23

Mr = 2555(CBR)0.64 (6) 24

In level 3, typical default values of layer modulus as a function of soil classification can be 25

chosen from the database of the highway agencies or the default values implemented in MEPDG 26

software. Khazanovich et al. (11) found that the typical values for resilient modulus for subgrade 27

soils in the MEPDG level 3 were reasonable but that soil with the same soil classification may 28

have a wide range of modulus values. Consequently every state started building an experimental 29

database for the pavement materials such as Virginia, Louisiana, Hampshire, New Jersey and 30

Minnesota (5, 11-14). 31

32

For the unbound material characterization level 1 data, MEPDG uses the finite element code 33

DSC2D for the computations of the critical stresses, strains, and deformations (15). Whereas for 34

levels 2 and 3, it uses the MLEA code JULEA for the computations of the critical stresses, 35

strains, and deformations. 36

37


Number of researchers focused on the influence of the input level of the material (binder, asphalt 1

mix), climate, and traffic characterization on MEPDG predicted performance (16-20). These 2

studies reported significant variations in pavement performance based on the level of input. 3

Limited research studies focused on the influence of the unbound material input level on the 4

pavement performance, For example, Hossain et al. compared between the three levels of inputs 5

in the MEPDG for limestone and sandstone aggregate in Oklahoma (10). The authors concluded 6

that level 1 provides the highest reliability level for design however level 2 gives an intermediate 7

reliability level and level 3 yielded the lowest reliability level. Yang and Wu confirmed this 8

observation regarding to level 3 (13). 9

10

This study evaluates the influence of changing the input level and the material type of only the 11

unbound materials on the MEPDG predicted pavement performance at three different climatic 12

locations in Egypt. 13

14

MATERIALS 15

Seven different base course materials were investigated in this study. The investigated materials 16

were three virgin aggregates and four recycled Construction and Demolition (C&D) waste 17

materials with different proportions. Five materials (A, B, C, D, and E) were from Australia and 18

tested in Australia, while the materials, F, and G, were from Egypt and tested in Egypt. Material 19

A was quartzite virgin aggregates sourced from Boral quarry located at Para Hills, South 20

Australia. While materials B and C were C&D (100% Recycled Concrete Aggregates, RCA), 21

which were sourced from two different companies in South Australia, named Adelaide resource 22

recovery and ResourceCo. The original aggregates contained in the investigated RCA were 23

extracted from the Adelaide Hills and were, generally, quartzite, dolomite, or siltstone. Materials 24

D and E were comparable products of 80% of RCA with 20% by mass of Recycled Clay 25

Masonry (RCM) from the same companies in South Australia. Materials F and G were limestone 26

aggregates from two different sources in Egypt. 27

28

TESTING PROGRAM 29

A series of laboratory tests were conducted on the Unbound Granular Materials (UGMs) to 30

evaluate their basic behavior. The testing program consisted of two main parts, routine and 31

advanced tests. All tests performed on the five materials sourced from Australia were conducted 32

according to the Australian Standards or to local Australian road authority protocols. While, the 33

two virgin aggregates sourced from Egypt were tested in line with the current requirements of 34

the AASHTO Standards. 35

36

General Engineering Properties 37

A number of tests were carried out for evaluating the base course materials. A list of the tests is 38

outlined below. 39

AASHTO classification. 40

Specific gravity and water absorption. 41

Atterberg Limits (Plasticity Index (PI), and Liquid Limit (LL)). 42


Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) for Modified 1

compactive effort. 2

Los Angeles Abrasion (LAA). 3

California Bearing Ratio (CBR). 4

Some routine tests were conducted on the recycled materials i.e., unconfined compressive 5

strength and shrinkage tests as the recycled materials contained a small amount of free lime 6

(Calcium oxide, CaO) of 0.2%. Results of these tests are available in (21, 22). 7

8

Advanced Tests 9

In order to determine the shear strength parameters and the resilient modulus characteristics for 10

the seven materials, a range of static shear strength triaxial testing and repeated load triaxial 11

testing (RLTT) were conducted. The materials were compacted at the target dry density ratio of 12

98% of the maximum dry density (MDD). The RLTT were performed using two different 13

protocols; AUSTROADS and AASHTO T307 (23, 24). AUSTROADS protocol was used for the 14

materials tested in Australia (A, B, C, D, and E) while the Egyptian materials (F and G) were 15

tested according to the AASHTO T307.In general, the AUSTROADS procedure is similar to the 16

approach specified by AASHTO standard T307 in the multistage and the preconditioning stage. 17

However, the shape of the cyclic load pulse differs between the two approaches. The shape is 18

trapezoidal for AUSTROADS, and ramping, loading, and unloading occur over a 3-second 19

period. The AASHTO protocol adopted a haversine shaped load pulse over a 1-second period. 20

Furthermore, the number of load repetitions applied in the AUSTROADS method in each stress 21

stage is 200, but AASHTO stipulates just 100 cycles. Lekarp et al. (25), quoting Hicks (26), 22

stated that for granular materials, the loading period had insignificant impact on the resilient 23

modulus, although they suggested that saturated samples having low permeability may be 24

affected if drainage becomes ineffective. 25

Specimens 200 mm high by 100 mm diameter were prepared by static compaction of two equal 26

layers at a target dry density of 98 % of MDD (Modified Proctor compaction) for Australian 27

materials as recommended by the Australian Standards. The materials tested in Egypt were 300 28

mm high by 150 mm diameter and were prepared at the same target density (modified Proctor 29

compaction) but by the compaction method specified in the AASHTO T307. AASHTO T307 30

(24) specifies that the minimum diameter of the specimen should be 5 times the Maximum 31

Aggregate Size (MAS) and the height of the specimen should be at least twice the specimen 32

diameter. As well, AUSTROADS method (23) requires the same conditions and restricts that the 33

MAS should not exceed 19mm to achieve a minimum diameter to MAS ratio of 5. Therefore, the 34

material retained on the 19 mm sieve was discarded before testing resilient modulus according to 35

the AUSTROADS RLTT protocol. In addition, previous studies (e.g., 27, 28) proved that the 36

specimen size had no significant effect on the resilient modulus test results. The impact of 37

compaction method on material performance was not investigated in this research study. 38

39

Testing Results of Base Course Materials 40

TABLE 1 presents a summary of the engineering properties of the seven investigated materials. 41

These included: the general engineering properties, shear strength parameters, and RLTT results. 42

The particle size distributions for the seven materials are also included in the table. It can be seen 43

from the table that the LAA values were higher for C&D products ranging between 37% and 44


43% compared with virgin materials. All the materials were A-1-a according to the AASHTO 1

classification system except material F, which was A-1-b. 2

3 TABLE 1. Summary of the Properties of the Seven Unbound Base Materials 4

Material A B C D E F G

Type

Quartzite

virgin

Aggregate

100% RCA 100% RCA

80%

RCA/20%

RCM

80%

RCA/20%

RCM

Limestone Limestone

Country Australia Egypt

CBR (%) 170 142 217 149 99 73 60

Passing #200 Sieve (%) 11 5 7 6 7 16.6 5

Passing #100 Sieve (%) 14 8 11 9 12 17.2 8.7

Passing #50 Sieve (%) 17 13 18 15 18 20 12.1

Passing #40 Sieve (%) 18 17 21 18 21.4 22.7 15.7

Passing #30 Sieve (%) 21 19 24 21 24 28.2 22.9

Passing #16 Sieve (%) 26 25 29 26 28 30.4 33.2

Passing #8 Sieve (%) 33 32 35 34 36 31.8 40.1

Passing #4 Sieve (%) 47 43 47 44 48 41.9 45.4

Passing 3/8 inch Sieve (%) 70 64 71 66 71.4 45.3 50.3

Passing ½ inch Sieve (%) 88 79 88 79 87.6 46.8 51.3

Passing ¾ inch Sieve (%) 96 90 95 95 99 51.1 53.5

Passing 1 inch Sieve (%) 100 100 100 100 100 57.6 63.5

Passing 1 1/2 inch Sieve (%) 100 100 100 100 100 84.3 96.2

Passing 2 inch Sieve (%) 100 100 100 100 100 100 100

Specific gravity 2.58 2.6 2.55 2.56 2.55 2.45 2.47

Water absorption 2.3 8.9 5.5 6.3 6.8 3.25 1.6

Liquid Limit, % 18 26 23 26 23 30 23

Plasticity Index, % 3.0 2.0 1.0 0 2.5 3.6 5.0

Material Type According to

AASHTO Classification A-1-a A-1-a A-1-a A-1-a A-1-a A-1-b A-1-a

MDD, pcf (t/cm3) 134.84 (2.16) 119.88 (1.92) 124.2 (1.99) 119.86 (1.92) 116.12 (1.86) 138.59 (2.22) 136.15 (2.18)

Los Angeles Abrasion (LAA), % 25 39 37 41 43 34 26

Apparent Cohesion, C, kPa 24 117 85 70 30 63 38

Friction Angle, φ, º 52 54 48 51 46 50 53

Resilient

Modulus

Data

Regression

Coefficients values

k1=1097.35

k2= 0.83

k3 = - 0.45

k1=2000

k2= 0.684

k3 = - 0.295

k1=1215.8

k2= 0.751

k3 = - 0.196

k1=1488.34

k2= 0.697

k3 = -1.164

k1=1504.79

k2= 0.877

k3 = - 2.81

k1=825.66

k2= 1.026

k3 = -0.908

k1=1198.84

k2= 0.654

k3 = - 0.059

R2 0.96 0.94 0.97 0.97 0.94 0.99 0.98

5

Virgin materials A and F had the highest fine contents of 11% and 16.6%, respectively. The 6

plasticity of fines was within the limits set by most of specifications i.e., AASHTO of 1 to 6%. 7

However, the plasticity of fines of materials A, F, and G was relatively higher than the other 8


materials. Fines with high plasticity rather than fines with low plasticity, can cause significant 1

reduction in the material stiffness and strength as stated by several researchers (29, 30, 31, 32). 2

Owing to that, virgin materials F and G, exhibited the lowest strength in terms of CBR although 3

these materials had the highest MDD. Moreover, the matric suction of material A was 4

significantly less than that of the C&D products (B, C, D, and E) (33). This explains why the 5

stiffness of C&D products was mostly higher than that of virgin aggregate. 6

Relatively high angles of internal friction, φ, were found for the seven UGMs, varying from 46 7

to 54º. The regression coefficients, k1, k2, and k3 were determined for each base course material 8

by applying the universal model (Equation 5) to the testing data. The accuracy of regression in 9

terms of the coefficient of determination, R2 was in the range of 0.94 to 0.99 indicating excellent 10

fit. 11

12

MEPDG SIMULATION RUNS

To investigate the influence of unbound materials input level on the pavement performance, 13

computer simulation runs using MEPDG were conducted for 10 years of service life. Average 14

Annual Daily Traffic (AADT) of 1500 vehicles per day with a growth rate of 2%, which is 15

equivalent to about 5,699,120 Equivalent Single Axle Loads (ESALS) at the end of the 10 years 16

design life, was used for all runs. The simulation runs were conducted using three weather 17

stations representing different climatic regions in Egypt. Theses weather stations were 18

Alexandria (costal climate), Cairo (moderately hot weather), and Aswan (Vey hot weather).A 19

summary of the mean annual air temperature, wind speed, cumulative precipitation, sunshine, 20

and relative humidity for these locations are summarized in TABLE 2. The geographical 21

locations of these weather stations are shown in FIGURE 1. More details regarding the 22

development of the climatic data files used for the MEPDG runs can be found in (34, 35). A 23

typical conventional flexible pavement system used in road construction in Egypt with the layers 24

shown in FIGURE 2 with a fixed ground water table depth of 12 m was used for all simulation 25

runs. 26

The Asphalt Concrete (AC) layers’ properties as well as the binder properties used for the 27

MEPDG runs are summarized in TABLE 3. 28

29

TABLE 2. Mean Annual Climatic Data for the Selected Weather Stations (34, 35) 30

Weather

Station

Mean Annual Air

Temperature

(oC)

Mean Annual Wind

Speed (km/h)

Mean Annual

Sunshine

(%)

Mean Annual

Cumulative

Precipitation

(mm)

Mean Annual

Relative

Humidity

(%)

Alexandria 21.80 17.22 88.78 232.00 64.69

Cairo 23.48 10.30 89.97 69.90 48.41

Aswan 24.40 8.59 96.09 9.50 27.98

31

Three input levels were investigated for the base layer. For the typical pavement system, level 1 32

data for the resilient modulus based on measured laboratory values (k1, k2, and k3 elastic 33

response coefficients) were used for the first set of MEPDG runs. The second set of computer 34

simulation runs were conducted using the laboratory measured CBR values of the base materials. 35

The final set of runs, utilized default resilient modulus values of the unbound base materials 36

based on the AASHTO class as recommended by the MEPDG. 37

http://en.wikipedia.org/wiki/Alexandria_Governorate

http://en.wikipedia.org/wiki/Cairo_Governorate

http://en.wikipedia.org/wiki/Aswan_Governorate


1

FIGURE 1. Locations of the Selected Weather Stations 2

3

4

5

6

7

FIGURE 2. Pavement System used for MEPDG Runs 8 9

TABLE 3. Properties of the Asphalt Binder and HMA Layers 10 Property AC Wearing Course AC Binder Course

Thickness, in. (cm) 2 (5) 2.4 (6)

Cumulative Retained ¾ inch Sieve 11.7 18.1

Cumulative Retained 3/8 inch Sieve 23.7 35.7

Cumulative Retained #4 inch Sieve 49.7 69.1

% Passing #200 Sieve 4.2 1.2

Initial Mix Air Voids (% Va) 7.0 8.0

Effective Binder content by Volume (% Vbeff) 11.0 10.2

Total Unit Weight, pcf (t/m3) 141.82 (2.27) 142.44 (2.28)

Penetration Grade of AC Binder 60-70 60-70

11

AC Wearing Course

AC Binder Course

A-1-a& A-1-b

A-7-6

Asphalt Layer I

Asphalt Layer II

Granular Base Course

Subgrade

5 cm

6 cm

30 cm


RESULTS AND ANALYSIS

A total of 63 MEPDG simulation runs were conducted. Each run for the level 1 analysis lasted 1

for approximately 18 hours while each level 2 or 3 run took about15 minutes to complete. 2

Despite the input level, MEPDG computes the pavement response using the same methodology. 3

However, for level 1 the stress dependent universal resilient modulus model (Equation 5) is used 4

to compute the resilient modulus as a function of the predicted stresses. MEPDG predicts rutting, 5

bottom-up fatigue cracking, top-down longitudinal cracking, low temperature cracking 6

(transverse cracking) and International Roughness Index (IRI) over the service life of the 7

pavement. All distresses were computed at a reliability level of 90%. A summary of the results of 8

the performed runs is given in TABLE 4. Results presented in the table show variation in 9

pavement performance indicators as the input level changes. The base material type and climatic 10

location also affected the predicted distresses. 11

It can be seen from the data in the table that level 1 input yielded the highest values of 12

longitudinal and alligator cracking for all types of materials. The difference in results from level 13

1 to level 2 and level 3 of both forms of fatigue cracking (alligator and longitudinal) is 14

significant. For example, for Alexandria climate, the longitudinal cracking based on level 1 15

ranged from 8.3 times that of level 2 and 7.3 times that of level 3 for material type G. For 16

material F it was 3.7 times that of level 2 and 2.6 times that of level 3. Level 1 Alligator cracking 17

in Aswan climate ranged from 2.8 times that of level 2 and 2.4 times that of level 3 for material 18

E to 1.7 times that of level 2 and 1.5 times that of level 3 for material type B. It can also be 19

concluded that fatigue cracking values for Aswan are higher than Cairo and both Aswan and 20

Cairo are higher than Alexandria. 21

The data in TABLE 4 also shows that rutting values for level 1 input ranged from 1.3 to 1.6 22

times the total rutting values of level 2 and level 3 for Cairo. Again, the total rutting values for 23

Aswan are higher than Cairo and both Aswan and Cairo are higher than Alexandria. The data in 24

the table also shows more than 20% difference in the total rutting values predicted based on level 25

1 data (the highest and lowest) compared to only less than 10% based on either level 2 or 3. This 26

means that the rutting values computed based on level 1 data are more sensitive to the material 27

type compared to the rutting values based on levels 2 and 3. However, the difference in the 28

predicted cracking was found to be more sensitive to the material type for all input levels. 29

Material G with the lowest CBR value and highest plasticity index exhibited the highest values 30

of longitudinal cracking for level 1 at all weather stations. It also exhibited the highest values for 31

alligator cracking, rutting, and IRI for Alexandria while material E exhibited higher values for 32

alligator cracking, rutting, and IRI at warmer weathers in Cairo and Aswan. The results also 33

indicate that for all practical purposes Mr input levels 2 and 3 yielded very similar performance 34

while Mr level 1 input yielded very high distresses compared to levels 2 and 3. The reason for 35

that is the MEPDG level 2 model (Equation 4) uses a maximum CBR value of 100% and the 36

CBR values of five out of the seven materials were 99% or more. Moreover for level 3, the 37

default moduli values were selected based on the AASHTO classification system, which were 38

mostly A-1-a for the investigated materials. For all values of performance indicators the hottest 39

climate (Aswan) yielded higher distresses compared to Cairo and Alexandria climates. The 40

predicted IRI values shown in TABLE 4 also show close agreement between the predictions 41

based on levels 2 and 3 while IRI values based on level 1 data are higher than those based on 42


levels 2 and 3. The IRI values for Aswan are relatively higher than Cairo and both Aswan and 1

Cairo are relatively higher than Alexandria. 2

TABLE 4. Predicted Pavement Distresses at Different Input Levels at Egypt Representative 3

Weather Stations for Investigated Materials at the End of Design Life. 4

City Input

Level

Longitudinal Cracking (m/km)

A B C D E F G

Ale

x. Level 1 1396.48 931.32 1229.75 1282.37 1591.82 991.71 1828.85

Level 2 207.73 212.89 208.58 310.33 275.30 263.54 220.33

Level 3 338.90 334.33 320.73 432.01 394.85 374.30 248.87

Ca

iro Level 1 1543.53 1435.65 1623.38 1387.19 1947.04 1169.29 1954.46

Level 2 196.08 213.72 202.45 295.57 268.57 272.28 217.07

Level 3 325.23 332.20 312.26 417.59 384.82 382.41 245.36

Asw

an

Level 1 1601.19 1225.93 1688.28 1450.60 1984.01 1284.21 2020.90

Level 2 206.87 221.94 214.57 289.63 267.97 283.43 221.15

Level 3 336.45 339.30 326.53 411.76 383.62 393.07 248.89

City Input

Level

Alligator Cracking (%)

A B C D E F G

Ale

x. Level 1 42.11 35.11 39.41 40.21 46.71 36.21 50.51

Level 2 21.84 23.05 22.89 23.5 24.18 22.95 22.15

Level 3 23.7 25.92 24.01 26.14 27.35 25.18 22.68

Ca

iro Level 1 46.31 46.31 51.71 44.51 64.11 40.31 55.41

Level 2 21.74 22.88 22.68 23.42 23.96 23.12 22

Level 3 23.84 25.9 24.04 26.31 27.29 25.6 22.54

Asw

an

Level 1 46.41 40.31 54.11 47.21 67.81 42.91 58.71

Level 2 21.75 22.85 22.66 23.52 23.98 23.24 22.08

Level 3 23.99 25.98 24.16 26.59 27.46 25.88 22.64

City Input

Level

Rutting (cm)

A B C D E F G

Ale

x. Level 1 2.94 2.59 2.83 2.83 3.03 2.76 3.28

Level 2 2.02 2.06 2.05 2.08 2.14 2.09 2.03

Level 3 2.10 2.17 2.05 2.17 2.24 2.17 2.05

Ca

iro Level 1 3.50 3.30 3.61 3.35 3.93 3.26 3.80

Level 2 2.40 2.47 2.47 2.46 2.53 2.49 2.38

Level 3 2.49 2.55 2.46 2.55 2.64 2.57 2.40

Asw

an

Level 1 3.61 3.37 3.94 3.67 4.31 3.57 4.16

Level 2 2.67 2.72 2.72 2.72 2.81 2.73 2.65

Level 3 2.75 2.81 2.70 2.82 2.91 2.82 2.67

City Input

Level

IRI (m/km)

A B C D E F G

Ale

x. Level 1 2.46 2.30 2.41 2.41 2.55 2.34 2.67

Level 2 2.00 2.04 2.04 2.03 2.06 2.03 2.00

Level 3 2.02 2.10 2.05 2.08 2.12 2.07 2.01

Ca

iro Level 1 2.63 2.61 2.76 2.57 3.05 2.49 2.85

Level 2 2.07 2.11 2.11 2.10 2.13 2.10 2.07

Level 3 2.09 2.17 2.12 2.16 2.19 2.15 2.08

Asw

an

Level 1 2.64 2.50 2.85 2.66 3.19 2.58 2.97

Level 2 2.11 2.15 2.15 2.14 2.17 2.14 2.11

Level 3 2.12 2.21 2.15 2.20 2.24 2.19 2.12

5

TABLE 5 presents the predicted rutting for each layer individually. This data show the 6

contribution of each layer to the total rutting for each of the input levels investigated at the 7


different climatic locations. The data show significantly lower rutting values in the granular base 1

layer compared to the AC and subgrade layers, for levels 2 and 3 which is rational for granular 2

materials. For level 1, the predicted base layer rutting is almost comparable to the subgrade layer 3

rutting and much higher compared to levels 2 and 3. This is not rational, as one would expect 4

lower amount of rutting in the granular base layer because of the grain to grain interlocking 5

action. Further, the contribution of the AC layer to the total rutting is the highest which may 6

indicate inferior AC layer quality. The predicted AC layer rutting based on level 1 Mr data is also 7

higher compared to the values based on levels 2 and 3 for all investigated weather conditions. It 8

should be noted that the current global calibration factors in the MEPDG were based on level 3 9

unbound material characterization. 10

TABLE 5. Rutting of Sublayers at Different Input Levels at Egypt Representative Weather 11

Stations for the Investigated Materials at the End of Design Life 12 City Input

Level

Layer Rutting of Sublayers (cm)

A B C D E F G

Ale

xa

nd

ria

Lev

el 1

AC 1.21 1.04 1.14 1.16 1.20 1.11 1.27

Base 0.87 0.73 0.86 0.86 0.98 0.82 1.10

Subgrade 0.86 0.82 0.84 0.82 0.86 0.83 0.91

Total 2.94 2.59 2.83 2.83 3.03 2.76 3.28

Lev

el 2

AC 0.83 0.82 0.83 0.85 0.89 0.85 0.84

Base 0.31 0.35 0.34 0.36 0.36 0.35 0.29

Subgrade 0.88 0.88 0.88 0.87 0.89 0.89 0.90

Total 2.02 2.06 2.05 2.08 2.14 2.09 2.03

Lev

el 3

AC 0.85 0.88 0.81 0.87 0.92 0.87 0.84

Base 0.35 0.38 0.36 0.40 0.41 0.39 0.30

Subgrade 0.90 0.91 0.88 0.90 0.91 0.91 0.91

Total 2.10 2.17 2.05 2.17 2.24 2.17 2.05

Ca

iro

Lev

el 1

AC 1.68 1.69 1.78 1.60 1.89 1.56 1.78

Base 0.95 0.74 0.96 0.93 1.17 0.87 1.10

Subgrade 0.86 0.87 0.87 0.82 0.88 0.83 0.91

Total 3.50 3.30 3.61 3.35 3.93 3.26 3.80

Lev

el 2

AC 1.23 1.24 1.25 1.22 1.28 1.24 1.23

Base 0.32 0.36 0.35 0.37 0.38 0.37 0.30

Subgrade 0.85 0.87 0.87 0.86 0.87 0.87 0.85

Total 2.40 2.47 2.47 2.46 2.53 2.49 2.38

Lev

el 3

AC 1.25 1.26 1.22 1.25 1.32 1.26 1.23

Base 0.36 0.40 0.37 0.42 0.43 0.42 0.31

Subgrade 0.88 0.89 0.87 0.88 0.89 0.89 0.86

Total 2.49 2.55 2.46 2.55 2.64 2.57 2.40

Asw

an

Lev

el 1

AC 1.78 1.78 2.09 1.89 2.22 1.85 2.10

Base 0.97 0.75 0.97 0.96 1.22 0.88 1.14

Subgrade 0.86 0.83 0.88 0.82 0.87 0.84 0.92

Total 3.61 3.37 3.94 3.67 4.31 3.57 4.16

Lev

el 2

AC 1.51 1.51 1.52 1.48 1.56 1.50 1.50

Base 0.33 0.37 0.36 0.38 0.39 0.38 0.31

Subgrade 0.83 0.84 0.84 0.86 0.86 0.85 0.85

Total 2.67 2.72 2.72 2.72 2.81 2.73 2.65

Lev

el 3

AC 1.52 1.53 1.48 1.50 1.59 1.52 1.50

Base 0.37 0.41 0.38 0.43 0.44 0.42 0.32

Subgrade 0.86 0.87 0.85 0.88 0.88 0.87 0.85

Total 2.75 2.81 2.70 2.82 2.91 2.82 2.67

13


SUMMARY AND CONCLUSIONS 1

A total of 63 MEPDG computer simulation runs were conducted using a typical pavement 2

section used in Egypt. The three hierarchal input levels for the unbound granular base layer were 3

used in this research. The MEPDG runs were conducted at three climatic conditions represented 4

by Alexandria, Cairo, and Aswan for seven material types including three virgin aggregates and 5

four recycled C&D materials with different proportions of RCM. Based on the results and 6

analyses of this research the following conclusions are drawn: 7

The input level of the unbound materials has a significant influence on the MEPDG 8

predicted performance. 9

Level 1 unbound material characterization input yielded significantly higher rutting and 10

cracking compared to levels 2 and 3. 11

Levels 2 and 3 yielded significantly lower rutting values in the granular base layer 12

compared to the AC and subgrade layers whereas for level 1, the predicted base layer 13

rutting was almost comparable to the subgrade layer rutting and much higher compared 14

to levels 2 and 3 which is not rational. 15

For all practical purposes, the MEPDG predicted performance indicators using input 16

levels 2 and 3 for the investigated base layers were relatively similar. 17

The results showed that, despite the input level for the unbound material characterization, 18

the hotter the climate, the larger the predicted distresses. 19

Materials with high plasticity index and low CBR exhibited higher longitudinal cracks for 20

all weather conditions. The highest values of all performance indicators were found for 21

level 1 in hotterweather conditions. 22

The predicted longitudinal and alligator fatigue cracking were found to be very sensitive 23

to the material type at all Mr input levels while for rutting theywere found to be 24

significant only at the level 1input. At levels 2 and 3 the difference between the 25

maximum and minimum predicted total rutting for the seven investigated materials was 26

less than 10%. 27

Finally, it is recommended to calibrate the MEPDG distress models before using level 1 28

input for unbound material characterization. 29

30

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