PERFORMANCE EVALUATION OF COMPOSITE … · to Malaysian JKR standard specification ... Marshall Mix...

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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

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



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 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 +∑+∑+∑+=

∠==ββββ 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 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.

22 08.012.0018.0_43.094.003.5 BAABBAFlow ++−−=

214.022.128.5 BAFlow +−=



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

(a) (b)

(c)

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

Actual

Pre

dicte

d

Predicted vs. Actual

1.70

3.00

4.30

5.60

6.90

1.76 3.04 4.32 5.60 6.88

Actual

Pre

dic

ted


9.00

10.25

11.50

12.75

14.00

9.00 10.25 11.50 12.75 14.00

Actual

Pre

dic

ted


2.63

2.79

2.96

3.12

3.28

2.63 2.79 2.95 3.12 3.28



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.

(a) (b)

(c)

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

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

anosilic

a

2.69349

3.531544.36965.207656.04571

55555

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

anosilic

a

9.82631 9.82631

10.6087

10.608711.391

11.391

12.1734

12.9557

55555

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

anosilic

a

2.78842.88628

2.984153.08203

3.17991

55555



(a) (b)

(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.

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 (%)

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

tab

ility

(k

N)


4.00

4.50

5.00

5.50

6.00

1.00

1.50

2.00

2.50

3.00

2.63

2.7925

2.955

3.1175

3.28

F

low

(m

m)




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