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International Journal of Civil Engineering and Technology (IJCIET)

Volume 8, Issue 9, September 2017, pp. 616–628, Article ID: IJCIET_08_09_070

Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=9

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

PERFORMANCE EVALUATION OF

COMPOSITE ASPHALT MIXTURE MODIFIED

WITH POLYETHYLENE AND NANOSILICA

Nura Bala, Madzlan Napiah, Ibrahim Kamaruddin

Department of Civil & Environmental Engineering,

Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar,

Perak, Malaysia

ABSTRACT

In this study, the influence of polymer and nanosilica on performance

enhancement of asphalt mixtures was investigated. Asphalt mixtures samples are

prepared with polymer nanocomposite modified bitumen incorporating polyethylene

and nanosilica at different percentages, the results were compared with control 6%

polyethylene polymer modified mixture regarding resistant to draindown, particle

loss, and rutting resistance. The study also investigates the application of Response

Surface Methodology (RSM) for the prediction of Marshal volumetric properties.

Results indicate that, both polyethylene and nanosilica has positive effect on the

performance of porous asphalt mixture, they improve rutting resistance significantly,

reduces binder draindown and particle loss of porous asphalt mixture, on the other

hand, statistical analysis based on RSM shows that a quadratic model developed

having a high degree of correlation and predicting ability can be used to predict

Marshal volumetric properties of the mixture.

Key words: Nanosilica, Polyethylene, Nanocomposite, Particle Loss, Draindown.

Cite this Article: Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin, Performance

Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica,

International Journal of Civil Engineering and Technology, 8(9), 2017, pp. 616–628.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=9

1. INTRODUCTION

Porous asphalt mixture is a permeable hot bituminous mixture characterized with a high

percentage of air voids which is mostly used in areas where precipitation level is high. Porous

asphalt mixture is different from dense graded hot mix asphalt as it provides sufficient

interconnected voids for high permeability due to predominantly graded crushed coarse

aggregate without a significant proportion of fines [1]. The most important benefit of porous

asphalt is an improvement in the safety of pavement through a reduction in risk of skidding

during wet weather condition, furthermore, porous asphalt provides a reduction in splash and

spray as well as improvement of pavement markings visibility in wet weather [2].

Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin


The applied loads, together with harsh environmental conditions cause a deterioration of

pavement which reduces the expected service life of the pavement [3-6]. Most common

pavement mode of distresses is rutting damage which is commonly happened in the form of

permanent deformation (surface rutting) and fatigue cracking failure which is initiated due to

the successive accumulation of tensile strain induced by repeated load application on the

pavement [7-9].

Despite the several benefits reported for porous asphalt, some structural and performance

disadvantages have been reported by previous researches. Porous asphalt is generally

associated with less resistance to disintegration and premature voids clogging which reduces

its structural durability [10]. In addition, porous asphalt has the relatively high cost of

construction and maintenance compared to dense graded hot mix asphalt. Required high

quality aggregates and improved bitumen which are necessary to govern the resistance of

porous asphalt against rutting and moisture damage limited the wide application of porous

asphalt mixture [2].

In response to the above mentioned challenges, previous researches indicated that

application of modified bitumen as a substitute to virgin or unmodified bitumen increases the

life service performance of porous asphalt mixtures. Chen et al.[11] after laboratory and field

evaluation reported that using polymer-modified binders instead of unmodified binder reduces

rutting and ravelling distresses of porous asphalt mixtures. Polymer materials such as

thermoplastic elastomers and plastomers are widely used to improve bitumen properties after

yielding some improvements on the modified asphalt binders characteristics [12].

It is clear that incorporating polymers as modifiers for bitumen enhances its performance

characteristics [13]. However, polymer modified bitumen is subjected to phase separation

caused by poor compatibility of polymers with bitumen [14], these consequently affects the

performance of polymer modified binders [15]. Based on that, there is a need for improving

the performance of polymer modified binders.

Recently, nanomaterial has extensively gained a great attention by pavement researchers

for the preparation of durable asphaltic mixtures with high performance due to their excellent

beneficial properties such as large surface area, excellent dispersion ability, strong absorption,

excellent stability as well as high chemical purity [16-18]. Nanomaterials have also

extensively applied in concrete performance improvements [19].

This research investigates the application of polyolefenic polymer namely polypropylene

(thermoplastic plastomer) due to its availability as daily waste and addition of nanosilica at

lower contents to form polymer nanocomposite modified mixture to mitigate the reduction in

performance properties of polymer modified binders. The main objective of this study was to

investigate and evaluate the performance properties of porous asphalt mixtures produced with

polymer nanocomposite modified binder. In addition, a model for prediction of volumetric

properties is developed using regression and response surface methodology (RSM).

2. MATERIALS

The aggregate used in this study for the preparation of porous asphalt mixture samples is

crushed granite coarse aggregate, a porous aggregate design gradation was used in accordance

to Malaysian JKR standard specification [20]. The physical properties of crushed granite

coarse aggregate are presented in Table 1.

Bitumen binder grade 80/100 penetration was used for the preparation of modified binders

blend. Polypropylene polymer in resin form was used and blended with both bitumen and

nanosilica to form a polymer nanocomposite modified blends. The physical properties of the

bitumen used are presented in Table 2.

Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica


Table 1 Aggregates physical properties

Property Standard Value Unit

Coarse aggregate

Abrasion loss ASTM DC 131 28.16 %

Flakiness index (FI) BS 812: Section 105 7.20 %

Elongation index (EI) BS 812: Part 1 44 %

Absorption of water ASTM C 127 0.46 %

Specific gravity ASTM C 127 2.65

Table 2 Physical properties of base bitumen

Property Value Unit

Penetration (25 oC, 5 s, 0.1 mm, 100g) 84 dmm

Softening point temperature 42 oC

Ductility at 25 oC, 5 cm/min >150 cm

Viscosity at 135 oC 0.64 Pa.s

Mass loss 0.06 %

The specifications of powdered inorganic nanosilica material used in this investigation are

presented in Tables 3.

Table 3 Properties of nanosilica

Physical Property Value

Appearance High dispersive white powder

Hydrophobicity Strong hydrophobicity

SiO2 content (%) (950oC, 2h) 99.8

Purity (%) > 99.9

Loss of ignition (%) ≤ 6

Surface density (g/ml) 0.15

Average Particle size (nm) 10-25

PH value 6.5-7.5

Specific surface area (m2/g) 100 ± 25

3. METHODOLOGY

3.1 Preparation of Polymer Nanocomposites

The composite nano silica/polypropylene modified binders were prepared by adding 5%

polypropylene polymer together with 1%, 2%, 3% and 4% nanosilica by weight of bitumen

binder. 80/100 penetration grade binder was first heated in an oven at a temperature of 150 °C

to achieve desirable viscosity for mixing, polypropylene was then added to the required

amount of base binder prior to the composite modification at a high shearing rate of 4000

rpm, the mixing continued until polypropylene dissolves completely on the base binder.

Different percentages of nanosilica were added gradually and sheared at a high shearing rate

of 4000 rpm for 2 hours. Mixing was done using a propeller blade laboratory bench top multi

mix high shear mixer.

3.2 Marshal Mix Design

The Mix design used for the preparation of asphalt mixture samples were based on standard

Marshall Mix design method by applying 75 blows on both cylindrical samples sides having

dimension approximately 101 mm diameter and height of 64 mm. Marshal stability and flow

are obtained according to ASTM D1559 while the bulk specific gravity of compacted mixture



was obtained according to standard specification ASTM D2726. Volumetric characteristics of

compacted asphalt mixtures were estimated on the basis of bulk specific gravity of asphalt

mixture, and consist of Void in Mineral Aggregate (VMA) and Void in Total Mix (VTM).

3.3 Particle Loss

Particle loss tests were conducted based on standard specification EN 12697-17:2017 using

Marshal compacted specimens. The compacted asphalt mixture specimens were individually

put in the Los Angeles abrasion testing machine without steel balls. Los Angeles machine was

set to rotate for 300 revolutions at a speed of 30 – 33 revolutions per minute; after the test,

loose material broken off from the surface of the test specimen was discarded. The masses of

mixture specimens before and after the test are recorded. The Particle Loss by weight of

original specimen is computed by equation 1.

100%

A

BAPL (1)

where PL is particle loss, A is initial specimen mass, B is final specimen mass

3.4 Drain down

The draindown test was conducted in accordance to standard specification ASTM D6390

using an uncompacted asphalt mixture samples. This test simulates the conditioned

experienced by asphalt mixtures at high temperatures during production, storage, transport,

and placement of asphalt mixture. Aggregates are mixed with a binder and placed in a wire

basket positioned on top of the paper plate. The basket together with asphalt sample and paper

plate are stored in an oven at 160 ºC for 1 hour. After oven storage, the basket containing the

sample is removed from the oven along with the plate. The amount of draindown is

considered to be that portion of the material that separates from the sample. Draindown of

each asphalt mixture was computed by equation 2.

100

AB

CDDraindown

(2)

where A is a mass of empty wire basket, B is a mass of wire basket and sample, C is a

mass of empty plate, D is a mass of plate with drain material.

3.5. Wheel tracking test

Wheel track is a simulative test to predict measured rut depth of asphalt mixtures, wheel

tracking test was conducted using Wessex wheel tracking machine in accordance with British

Standard specification BS 598-110. Standard 305 mm×305 mm×50 mm compacted asphalt

mixture samples prepared at optimum binder content of each mixture were tested under a

standard wheel of 200 mm diameter and 50 mm width and load of 520 N. The Wessex wheel

tracker is equipped with software which automatically records the total rut depth for number

of wheels passes within duration of 45 minute loading period. All samples were tested at a

temperature of 40°C and prior to the test, slab samples were placed in the testing temperature

for 6 hours.



4. RESULT AND DISCUSSION

4.1. Particle Loss

Figure 1 presents the particle loss results of polymer nanocomposite modified asphalt

mixtures. From the results it is clear that polymer nanocomposites have lower particle loss

than control polymer modified mixture, this indicates that polymer nanocomposites mixtures

are stronger and more resistant than control mixture. Also, it can be seen that the particle loss

decreases with increase in nanosilica content which can be considered as a positive influence

of nanosilica on the performance of polymer nanocomposite porous asphalt. This can be

attributed due to the surface nature of nanosilica which blends and increases the adhesiveness

of the mixture there by increasing the bond strength between binder and aggregate.

Figure 1 Cantabro particle loss percentage results

4.2. Draindown

One of the major challenges with porous asphalt is binder draindown due to its open-

gradation [21], to minimize the effect of draindown a maximum value of 0.3% binder drain

down was recommended for porous asphalt mixtures [22]. Figure 2 presents the draindown

results of the polymer nanocomposite modified asphalt mixtures. As shown all polymer

nanocomposites modified mixtures analyzed presents binder draindown less than the

maximum allowable requirement of 0.3%. Control polymer modified mixture presents highest

draindown of 0.38% while polymer nanocomposite containing 3%NS presents the lowest

draindown value of 0.09%, this further confirms that nanosilica content increases adhesion

and draindown reduction of nanocomposite modified asphalt mixtures.

0

5

10

15

20

25

PE0%NS PE1%NS PE2%NS PE3%NS PE4%NS

Par

ticl

e L

oss

(%

)

Mixture type

Max. 20%



Figure 2. Binder draindown result

4.3. Wheel Tracking Test

The rut depth observed during the wheel tracking test is shown in Figure 3, it can be seen that

polymer nanocomposite modified mixture performs well when compared with control

polymer modified mixture. Lower deformation rate was observed in the polymer

nanocomposite containing 3% NS, this can be attributed to the increase in viscosity which

provides a better coating of aggregate, thus resulting in the formation of the well-connected

aggregate network within the modified mixture, this makes it more resistant to deformation.

On the other hand, highest deformation within polymer nanocomposites was observed in the

mixture containing 1% NS, this can be resulted due to an insufficient amount of nanosilica,

thus failed to enhance the stiffness of the mix resulting in failure of the mixture to resist

deformation.

Figure 3. Wheel tracking rut depth result

4.4. Response Surface Methodology

Response surface methodology (RSM) is a suitable and commonly applied statistical and

mathematical technique for analyzing and developing models between one or more

0.00

0.10

0.20

0.30

0.40

PE0% NS PE1% NS PE2% NS PE3% NS PE4% NS

Dra

in d

ow

n (

%)

Mixture type

Max. 0.3%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

PE0% NS PE1% NS PE2% NS PE3% NS PE4% NS

Rutt

ing d

epth

(m

m)

Mixture type



independent variables and responses. Application of statistical modeling and optimization

techniques is useful as it is excellent in terms of its ability to deal with various constraints and

objectives and in describing the interactions among dependent variables that affect a

particular response [23, 24]. RSM can also be applied for multi-objective optimization by

setting defined desirable goals based on either the responses or the variables. An optimal

predictor quadratic model, shown in equation 2, was used to obtain the optimal conditions for

the responses [25, 26].

exxxxy jiij

k

jijjj

k

jjj

k

jo

2

11 (2)

where y is the predicted outcome; β0 is the experiment central point fixed response value,

βj and βjj are first and second order effects, βij is cross interaction effect, xi, xj are coded

factors while e is a model random error.

Central composite design (CCD) is the most common applied design method used with

RSM for statistical evaluation of the relationship between independent variables and

responses [27]. In this study, the influence of two independent variables binder content (A)

from 4% to 6% and nanosilica (B) in the range of 1% to 3% were studied at three levels based

on face-centered central composite design (FCCCD). FCCCD is a distinct case of CCD in

which α is equal to 1.0, in FCCCD the α forces the axial points to locate on the surface of the

cubic rather than on the sphere space as in CCD design which makes FCCCD design a three-

level CCD. Design Expert software version 9.0.2.0 was utilized to produce statistical analysis

and experimental designs. The independent variables are binder content and nanosilica

content, while the responses considered, are air voids in mineral aggregate, Marshal stability,

and Marshal flow. Related literature [28, 29], as well as preliminary studies, were used to

select the independent variables as well as their respective experimental ranges.

4.4.1. Statistical Analysis

A statistical analysis has been done to have a good understating of the developed model's

performance. After regression analysis has been applied, a fitted quadratic model was

developed for prediction of all the responses. Quadratic models were selected based on the

highest order polynomials in which the additional terms were significant and are not aliased

by the software. The developed model equation with the all the significant terms are shown in

equation 3 to 5, on the other hand, the model equations after reduction to exclude insignificant

terms are also shown in equations 6 to 8 respectively. The positive and negative signs before

the terms in the equations show the synergistic and antagonistic effects of the individual

variables on the responses.

Before Reduction

22 07.005.126.092.091.1189.37 BAABBAAirvoid (3)

22 74.074.232.048.579..2769.61 BAABBAStability (4)

(5)

After Reduction

205.126.024.121.1235.38 AABBAAirvoid (6)

22 08.012.0018.0_43.094.003.5 BAABBAFlow



22 74.074.285.315.2744.58 BABAStability (7)

(8)

Table 4 presents ANOVA statistical analysis summary for the developed models before

and after reduction. The coefficient of determination (R2) is used to check the degree of

correlation of the models. As seen in Table 4, air void has an R2 value of 0.98 while stability

and flow have R2 values of 0.97 and 0.82, which indicate that the models have only 2%, 3%,

and 18% correlation error. However, after model reduction which removes insignificant terms

in the model, the R2

value for air void remain the same while that of stability reduces to 0.96

and 0.78. This is because removing the insignificant terms in the model reduces the number of

data points used in the calculation of R2 value. In addition, the lack of fit error in all the

models is found to be insignificant as their values are less than 0.0001 [30]. This indicates the

higher accuracy of the models.

The 95% confidence interval (P˂0.05) is used to evaluate the significance of the response

model and all the model terms. A low P-value of ˂ 0.05 indicates that the model selected and

its terms are significant. A quadratic model selected was found suitable for predicting air

voids, stability as well as a flow having probability P-values ˂ 0.05. The significance of each

variance and the responses are evaluated using the 95% confidence interval which

corresponds to probability P-value ˂ 0.05. Therefore, for air void, stability, and flow models,

there is only 0.01% chance that a model F-value of 188.95, 53.83 and 6.58 can occur due to

noise.

For an understanding of the developed model's satisfactoriness, plots of predicted versus

actual values for the responses are plotted as shown in Figure 4. As seen all the points for the

predicted and actual responses were spread relatively very close to the line of equality, the

distribution of the points indicates a satisfactory fitting precision of the models and the

predicted and experimental results are in agreement with each other.

Table 4 Analysis of ANOVA for responses

Response Factors F -Values P-Values Adequate

Precision

R2

Before

reduction

After

reduction

Air void

Model 118.95 ˂0.0001

30.82 0.9884 0.9879

A 527.57 ˂0.0001

B 0.49 0.5082

AB 4.78 0.065

A2 49.72 0.0002

B2 0.3 0.6025

Lack of fit 1.07 0.4557

Stability

Model 53.83 ˂0.0001

18.14 0.9746 0.9634

A 3.53 0.1022

B 34.33 0.0006

AB 3.1 0.1218

A2 152.41 ˂0.0001

B2 11.18 0.0123

Lack of fit 1.76 0.2941

Flow

Model 6.58 0.0141

7.26 0.8245 0.783

A 26.23 0.0014

B 0.27 0.6197

AB 0.088 0.7753

A2 2.66 0.1467

B2 1.3 0.2922

Lack of fit 0.4 0.7608

214.022.128.5 BAFlow



(a) (b)

(c)

Figure 4. Predicted Vs Actual plot (a) Air void (b) Stability (c) Flow

The 2D contour and 3D response plots for air voids, stability, and flow models are shown

in Figure 5 and Figure 6, respectively. As seen from Figure 5a the contour lines were nearly

straight indicating there is a partial interaction between the independent variables, while in

Figure 5b and Figure 5c elliptical contour lines can be observed indicating there is a perfect

interaction between variables [28, 31], the elliptical shape contours also show that there is an

area of optimum performance within 1.5 – 2.5% nanosilica and 4 – 5.5% binder content.

From both 2D and 3D plots presented in Figure 5 and Figure 6, it can be seen that nanosilica

has a positive effect on the responses behaviors of the modified mixture by increasing the

stability of the mixtures. This enhancement can probably attributed to the high energy and

surface activity of nanosilica in the mixture.

Design-Expert® Softw are

AV

Color points by value of

AV:

6.81

1.76

Actual

Pre

dict

ed

Predicted vs. Actual

1.70

3.00

4.30

5.60

6.90

1.76 3.04 4.32 5.60 6.88


Stability


Stability:

14

9

Actual

Pre

dict

ed


9.00

10.25

11.50

12.75

14.00

9.00 10.25 11.50 12.75 14.00


Flow


Flow :

3.27

2.63

Actual

Pre

dict

ed


2.63

2.79

2.96

3.12

3.28

2.63 2.79 2.95 3.12 3.28



(a) (b)

(c)

Figure 5. 2D contour plot (a) Air void (b) Stability (c) Flow

(a) (b)


AV

Design Points

6.81

1.76

X1 = A: Binder

X2 = B: Nanosilica

4.00 4.50 5.00 5.50 6.00

1.00

1.50

2.00

2.50

3.00AV

A: Binder content

B: N

an

osilic

a

2.69349

3.531544.36965.207656.04571

55555


Stability

Design Points

14

9

X1 = A: Binder

X2 = B: Nanosilica

4.00 4.50 5.00 5.50 6.00

1.00

1.50

2.00

2.50

3.00Stability

A: Binder content

B: N

an

osilic

a

9.82631 9.82631

10.6087

10.608711.391

11.391

12.1734

12.9557

55555


Flow

Design Points

3.27

2.63

X1 = A: Binder

X2 = B: Nanosilica

4.00 4.50 5.00 5.50 6.00

1.00

1.50

2.00

2.50

3.00Flow

A: Binder content

B: N

an

osi

lica

2.78842.88628

2.984153.08203

3.17991

55555


AV

6.81

1.76

X1 = A: Binder

X2 = B: Nanosilica

4.00

4.50

5.00

5.50

6.00 1.00

1.50

2.00

2.50

3.00

1.7

3

4.3

5.6

6.9

A

ir v

oid

(%

)

A: Binder content (%) B: Nanosilica (%)


Stability

14

9

X1 = A: Binder

X2 = B: Nanosilica

4.00

4.50

5.00

5.50

6.00

1.00

1.50

2.00

2.50

3.00

9

10.25

11.5

12.75

14

S

tability (

kN

)




(c)

Figure 6. 3D response plot (a) Air void (b) Stability (c) Flow

5. CONCLUSIONS

Based on the results of this investigation on the effects of adding polyethylene and nanosilica

to modify bitumen for porous asphalt mixtures preparation, the following conclusions can be

drawn:

Draindown tests show that nanosilica has a positive influence on the modified mixture as it

shows a reduction in the draindown values of the nanocomposite modified porous asphalts.

Polymer nanocomposite modified mixtures shows a lower rate of particle loss after Cantabro

test, this indicates that nanosilica improved aggregate adhesion of the porous asphalt mixtures

by reducing the particle loss.

Based on the statistical analysis, a quadratic model with a high degree of correlation and

predicting ability was developed for the prediction of volumetric responses air voids, stability,

and flow.

Both the individual effects of binder content and nanosilica are significant in the improvement

of the mixture but the percentage of nanosilica used shows the higher influence on the

volumetric properties.

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3.27

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X1 = A: Binder

X2 = B: Nanosilica

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