PERFORMANCE EVALUATION OF COMPOSITE …€¦ · Flakiness index (FI) BS 812: ... tracking test was...
Transcript of PERFORMANCE EVALUATION OF COMPOSITE …€¦ · Flakiness index (FI) BS 812: ... tracking test was...
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
http://www.iaeme.com/IJCIET/index.asp 621 [email protected]
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
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
http://www.iaeme.com/IJCIET/index.asp 622 [email protected]
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
Nura Bala, Madzlan Napiah and Ibrahim Kamaruddin
http://www.iaeme.com/IJCIET/index.asp 623 [email protected]
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
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
http://www.iaeme.com/IJCIET/index.asp 624 [email protected]
(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
Design-Expert® Softw are
Stability
Color points by value of
Stability:
14
9
Actual
Pre
dict
ed
Predicted vs. Actual
9.00
10.25
11.50
12.75
14.00
9.00 10.25 11.50 12.75 14.00
Design-Expert® Softw are
Flow
Color points by value of
Flow :
3.27
2.63
Actual
Pre
dict
ed
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
http://www.iaeme.com/IJCIET/index.asp 625 [email protected]
(a) (b)
(c)
Figure 5. 2D contour plot (a) Air void (b) Stability (c) Flow
(a) (b)
Design-Expert® Softw are
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
Design-Expert® Softw are
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
Design-Expert® Softw are
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
Design-Expert® Softw are
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 (%)
Design-Expert® Softw are
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
)
A: Binder content (%) B: Nanosilica (%)
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
http://www.iaeme.com/IJCIET/index.asp 626 [email protected]
(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.
REFERENCES
[1] B. Shirini and R. Imaninasab, "Performance evaluation of rubberized and SBS modified
porous asphalt mixtures," Construction and Building Materials, vol. 107, pp. 165-171,
2016.
[2] C. Sangiorgi, S. Eskandarsefat, P. Tataranni, A. Simone, V. Vignali, C. Lantieri, et al., "A
complete laboratory assessment of crumb rubber porous asphalt," Construction and
Building Materials, vol. 132, pp. 500-507, 2017.
[3] Y. Zhang, R. Luo, and R. L. Lytton, "Characterizing permanent deformation and fracture
of asphalt mixtures by using compressive dynamic modulus tests," Journal of Materials in
Civil Engineering, vol. 24, pp. 898-906, 2011.
[4] X. Lu and U. Isacsson, "Effect of ageing on bitumen chemistry and rheology,"
Construction and Building Materials, vol. 16, pp. 15-22, 2002.
[5] F. Durrieu, F. Farcas, and V. Mouillet, "The influence of UV aging of a
styrene/butadiene/styrene modified bitumen: comparison between laboratory and on site
aging," Fuel, vol. 86, pp. 1446-1451, 2007.
Design-Expert® Softw are
Flow
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
3.00
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
http://www.iaeme.com/IJCIET/index.asp 627 [email protected]
[6] H. Di Benedetto, C. De La Roche, H. Baaj, A. Pronk, and R. Lundström, "Fatigue of
bituminous mixtures," Materials and structures, vol. 37, pp. 202-216, 2004.
[7] G. Cerni, E. Bocci, F. Cardone, and A. Corradini, "Correlation Between Asphalt Mixture
Stiffness Determined Through Static and Dynamic Indirect Tensile Tests," Arabian
Journal for Science and Engineering, pp. 1-9, 2017.
[8] J. B. Sousa, J. Craus, and C. L. Monismith, "Summary report on permanent deformation
in asphalt concrete," 1991.
[9] D. Wang, L. Wang, and G. Zhou, "Fatigue of asphalt binder, mastic and mixture at low
temperature," Frontiers of Structural and Civil Engineering, vol. 6, pp. 166-175, 2012.
[10] A. E. Alvarez, A. E. Martin, and C. Estakhri, "A review of mix design and evaluation
research for permeable friction course mixtures," Construction and Building Materials,
vol. 25, pp. 1159-1166, 2011.
[11] J.-S. Chen, S.-F. CHEN, and M.-C. LIAO, "Laboratory and Field Evaluation of Porous
Asphalt Concrete," Asian Transport Studies, vol. 3, pp. 298-311, 2015.
[12] N. I. M. Yusoff, D. Mounier, G. Marc-Stéphane, M. R. Hainin, G. D. Airey, and H. Di
Benedetto, "Modelling the rheological properties of bituminous binders using the 2s2p1d
model," Construction and Building Materials, vol. 38, pp. 395-406, 2013.
[13] N. Bala, I. Kamaruddin, and M. Napiah, "The influence of polymer on rheological and
thermo oxidative aging properties of modified bitumen binders," Jurnal Teknologi, vol.
79, pp. 69-73, 2017.
[14] J. Zhu, B. Birgisson, and N. Kringos, "Polymer modification of bitumen: Advances and
challenges," European Polymer Journal, vol. 54, pp. 18-38, 2014.
[15] N. Bala and I. Kamaruddin, "Physical and storage stability properties of linear low density
polyethylene at optimum content," in Engineering Challenges for Sustainable Future:
Proceedings of the 3rd International Conference on Civil, Offshore and Environmental
Engineering (ICCOEE 2016, Malaysia, 15-17 Aug 2016), 2016, p. 395.
[16] L. Singh, S. Karade, S. Bhattacharyya, M. Yousuf, and S. Ahalawat, "Beneficial role of
nanosilica in cement based materials–A review," Construction and Building Materials,
vol. 47, pp. 1069-1077, 2013.
[17] R. Li, F. Xiao, S. Amirkhanian, Z. You, and J. Huang, "Developments of nano materials
and technologies on asphalt materials–A review," Construction and Building Materials,
vol. 143, pp. 633-648, 2017.
[18] N. Bala, I. Kamaruddin, M. Napiah, and N. Danlami, "Rheological and rutting evaluation
of composite nanosilica/polyethylene modified bitumen," Proceedings of the 7th
International Conference on Key Engineering Materials (ICKEM 2017) held between 11th
to 13th March 2017, Penang Malaysia. IOP Conference Series: Materials Science and
Engineering, vol. Vol 201, 2017.
[19] M. Adamu, B. S. Mohammed, and N. Shafiq, "Effect of Mineral Filler type on Strength of
Roller Compacted Rubbercrete for Pavement Applications."
[20] J. K. R. Malaysia, "Standard specification for Road Works, section 4. Flexible Pavement;
2008. p," S4-58–S4-59.
[21] A. C. d. Vale, M. D. T. Casagrande, and J. B. Soares, "Behavior of natural fiber in stone
matrix asphalt mixtures using two design methods," Journal of Materials in Civil
Engineering, vol. 26, pp. 457-465, 2013.
[22] T. R. Herráiz, J. I. R. Herráiz, L. M. Domingo, and F. C. Domingo, "Posidonia oceanica
used as a new natural fibre to enhance the performance of asphalt mixtures," Construction
and Building Materials, vol. 102, pp. 601-612, 2016.
[23] S. Kikuchi, N. Kronprasert, and S. M. Easa, "Aggregate blending using fuzzy
optimization," Journal of Construction Engineering and Management, vol. 138, pp. 1411-
1420, 2012.
Performance Evaluation of Composite Asphalt Mixture Modified with Polyethylene and Nanosilica
http://www.iaeme.com/IJCIET/index.asp 628 [email protected]
[24] M. Soltani, T. B. Moghaddam, M. R. Karim, S. Shamshirband, and C. Sudheer, "Stiffness
performance of polyethylene terephthalate modified asphalt mixtures estimation using
support vector machine-firefly algorithm," Measurement, vol. 63, pp. 232-239, 2015.
[25] D. C. Montgomery, "Design and Analysis of Experiments, John Wiley & Sons," New
York, pp. 64-65, 2001.
[26] Andi Maal, Muh. Saleh Pallu, Nur Ali and Isran Ramli. Experimental Study the
Performance of Asphalt Concrete Which using Plastics Powder Filler in Submersed Water
Conditions. International Journal of Civil Engineering and Technology, 8(7), 2017, pp.
686–696.
[27] Ciro Caliendo and Maurizio Guida, Performance of Porous Asphalt Pavement Based on
Seemingly Unrelated Equations Approach. International Journal of Civil Engineering and
Technology, 8(5), 2017, pp. 1195–1205
[28] R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, Response surface
methodology: process and product optimization using designed experiments: John Wiley
& Sons, 2016.
[29] N. Bala, M. Napiah, and I. Kamaruddin, "Application of Response Surface Methodology
for Mix Design Otimization of Nanocomposite Modified Asphalt Mixtures," International
Journal of GEOMATE, vol. 13, pp. 237-244, 2017.
[30] A. I. Nassar, N. Thom, and T. Parry, "Optimizing the mix design of cold bitumen
emulsion mixtures using response surface methodology," Construction and Building
Materials, vol. 104, pp. 216-229, 2016.
[31] M. Bendjima, M. Merbouh and B. Glaoui, Asphalt Concrete Behavior in Frozen Area.
International Journal of Civil Engineering and Technology, 8(5), 2017, pp. 927–936.
[32] M. O. Hamzah, B. Golchin, and C. T. Tye, "Determination of the optimum binder content
of warm mix asphalt incorporating Rediset using response surface method," Construction
and Building Materials, vol. 47, pp. 1328-1336, 2013.
[33] D. C. Montgomery, Design and analysis of experiments: John Wiley & Sons, 2008.
[34] Q. Li, L. Cai, Y. Fu, H. Wang, and Y. Zou, "Fracture properties and response surface
methodology model of alkali-slag concrete under freeze–thaw cycles," Construction and
Building Materials, vol. 93, pp. 620-626, 2015.