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http://www.iaeme.com/IJCIET/index.asp 616 [email protected]
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
http://www.iaeme.com/IJCIET/index.asp 617 [email protected]
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
http://www.iaeme.com/IJCIET/index.asp 618 [email protected]
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
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
http://www.iaeme.com/IJCIET/index.asp 619 [email protected]
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.
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
http://www.iaeme.com/IJCIET/index.asp 620 [email protected]
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%
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
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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
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
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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.
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
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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 +−=
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
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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
Predicted vs. Actual
9.00
10.25
11.50
12.75
14.00
9.00 10.25 11.50 12.75 14.00
Actual
Pre
dic
ted
Predicted vs. Actual
2.63
2.79
2.96
3.12
3.28
2.63 2.79 2.95 3.12 3.28
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
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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
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
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(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)
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
2.63
2.7925
2.955
3.1175
3.28
F
low
(m
m)
A: Binder content (%) B: Nanosilica (%)
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
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