A study on moisture susceptibility of stone matrix warm mix asphalt

Construction and Building Materials 144 (2017) 42–49

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A study on moisture susceptibility of stone matrix warm mix asphalt M. Khedmati a, A. Khodaii b,⇑, H.F. Haghshenas a a b

Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, United States Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

h i g h l i g h t s
Zycosoil can improve moisture resistance of WSMA where the siliceous aggregates used.
Influence of SaC is more than the influence of other factors on increasing TSR.
Influence of grading is more than the influence of other factors on decreasing TSR.
All first and second order plus interactive terms were statistically significant.

a r t i c l e

i n f o

Article history: Received 16 March 2016 Received in revised form 28 September 2016 Accepted 13 March 2017

Keywords: Moisture Susceptibility Grading Bitumen content Zycosoil content Sasobit content Mixing temperature Warm mix asphalt

a b s t r a c t Stone matrix asphalt (SMA) is known to have high resistance to permanent deformation and reflective cracking due to their stone structure but similar to other hot mixes it has a high energy intake during its production. Warm mix asphalt (WMA) has recently gained a lot of popularity worldwide due to environmental concerns. Moisture susceptibility as one of the drawbacks of warm mixes has been investigated by many researchers but the extent and interactive effects of different parameters affecting moisture susceptibility particularly for SMA has not yet been reported. The aim of the present study was to examine the effects of grading, bitumen content, Zycosoil content, Sasobit content and mixing temperature on moisture susceptibility of SMA warm mix asphalt (WSMA), as well as their interactions, using response surface methodology. The results indicated that the influence of Sasobit content and grading was more than the influence of other factors on tensile strength ratio -used as an index of moisture susceptibility- respectively. Also, interaction plots between factors affecting tensile strength ratio were generated that can help to understand the interactive relationship between investigated factors. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Stone matrix asphalt (SMA) is a gap-graded mix that has gained popularity world-wide. SMA has been used successfully in Europe for over 25 years to provide better rutting resistance and to resist studded tire wear [1,2]. These mixtures contain a large amount of coarse aggregate and enough fine aggregate to help fill the voids in the coarse aggregate [3]. In a SMA mix, the passing 4.75 mm sieve must be below 30 percent to ensure proper stone-on-stone contact [1] as the main source of strength [3]. Some advantages of SMA mixtures are its high rut resistance [4], high durability, and improved resistance to reflective cracking and reduced noise pollution; however drainage of binder and higher primary costs are known as its disadvantages [5]. SMA mixture typically contains a polymer in the asphalt binder or fiber ⇑ Corresponding author. E-mail addresses: [email protected] (M. Khedmati), [email protected] (A. Khodaii), [email protected] (H.F. Haghshenas). http://dx.doi.org/10.1016/j.conbuildmat.2017.03.121 0950-0618/Ó 2017 Elsevier Ltd. All rights reserved.

(cellulose or mineral) in the mixture to prevent drainage of the asphalt binder. This mixture has a surface appearance similar to that of an open graded friction course; however it has low inplace air voids similar to that of a dense graded hot mix asphalt (HMA) [3,6,7]. The use of warm mix asphalt (WMA) technology as a substitute for hot mix has been widely increased due to the concerns over global warming, air quality and fuel crisis. By lowering the viscosity of asphalt binder and/or increasing the workability of mixture using minimal heat, WMA technology allows the mixing, transporting, and paving process at significantly lower temperature compared to the conventional HMA [8]. The use of warm mix reduces energy consumption, lowers emissions and odors or greenhouse gases from plants, creating better working conditions at both the plants and the paving sites [9–12]. Researchers identified as many as fifteen different WMA technologies currently available. The most commonly used technologies are either using foaming or some chemical or organic additives [8]. These technologies facilitate reduced mixing and compacting temperature.

M. Khedmati et al. / Construction and Building Materials 144 (2017) 42–49

Mixing temperatures commonly used for most of WMA production is about 30–50 °C below the temperatures used for HMA [13– 16]. This lower temperature can well lead to an increase in moisture susceptibility due to adhesion failure as moisture may have remained in the aggregates. In the temperatures of 100–140 °C that WMA are produced, aggregates are not completely dry and hence many researchers recommended using aggregates from dry sources [8]. Researchers have reported using many different tests to evaluate moisture sensitivity of asphalt mixtures. These include boiling test, Marshall and indirect tensile tests; however some researchers and institutions prefer the indirect tensile test as they believe it can predict moisture susceptibility of the mixtures. [12,17,18]. Many researchers introduced various approaches for reducing moisture susceptibility in asphalt mixes [12,16–18]. One of the most widely adopted approaches is to use anti-stripping agents in order to increase the aggregate surface charges with the aim of increasing the bond between bitumen and aggregate particles. However, recent technologies have presented some other new materials, including nanomaterials such as Zycosoil [18,19]. Three common types of agents that are used to decrease the moisture sensitivity of asphalt mixes are hydrated lime, fatty amido-amines and fatty amines. It is believed that these create a temporary bond with aggregate surface and also they are not effective in all types of aggregates [20]. On the other hand, Zycosoil is claimed to be able to modify the aggregate surface as it is an organosilane compound that can react with soil particles and convert the hydrophilic silanol groups to hydrophobic siloxane groups and form a hydrophobic layer on the surfaces of aggregate [19,20]. This mechanism repels moisture and reduces water sensitivity of aggregate offering a permanent protection against moisture damage. Researchers showed that Zycosoil can be a good candidate to improve moisture resistance of asphalt mixture. In recent years, response surface methodology (RSM) has attracted attentions of many quality engineers in different industries. RSM is one of the designs of experiments (DOE) methods used to approximate an unknown function for which only a few values are computed [21]. RSM consists of a group of techniques used in the empirical study of the relationship between response and a number of input variables. Typically, an experimenter attempts to find the optimal setting for the input variables that maximizes (or minimizes) the predicted response. RSM is a useful method for studying the effect of several variables influencing the responses by varying them simultaneously [19,22–29]. The most extensive applications of RSM are in the particular situations where several input variables potentially influence some performance measure or quality characteristic of the process [30]. Thus performance measure or quality characteristic is called the response. The input variables are sometimes called independent variables, and they are subject of the scientist or engineer to control [31].

WSMA, as well as interactions between them, using proper methodology, namely RSM. A half fractional factorial center composite design (CCD) was selected as the design matrix since it allows the identification of first order interaction between factors and provides second order polynomial models, which can be employed to predict optimum level of these parameters. 2. Materials and mix design 2.1. Materials According to AASHTO M325-08 three grading levels of one aggregate type (i.e. siliceous), containing 20, 27.5 and 35% passing 4.75 mm sieve size were selected. These are shown in the gradation curves of Fig 1. The grading levels were named as fine, medium and coarse grading. Tables 1 and 2 show physical properties of the siliceous aggregates used in this research. Zycosoil was used as an anti- stripping agent in the mix. As the aggregate was siliceous and creates an unstable bond with bitumen against moisture attack. It is expected that high percentage of hydroxyls on the aggregate surface makes it a good candidate for Zycosoil that can permanently changes the ions on the aggregate surface creating a stronger bond between aggregate and the binder [19]. The binder used was polymer modified asphalt (PMA) with a Polymer purchased from a German supplier. The modified asphalt was prepared at 160 °C. Polymer was mixed with asphalt in a stirrer equipped with a mechanical agitator (350 rpm) for 15 min in a polymer to asphalt volume ratio of 6.5%. The physical properties of the asphalt and polymer are presented in Tables 3 and 4 respectively. Zycosoil was used as an anti-stripping agent and Sasobit as the warm additive and to stabilize the SMA, cellulose fiber was also added to the mix. The properties of Zycosoil and Sasobit were reported in Tables 5 and 6 respectively. 2.2. Mix design In SMA mix design, the optimum asphalt content is determined only based on the air voids of mixture [3,4]. In other words, in accordance with AASHTO MP8, the value of asphalt content which results in a final mixture’s air void of between 3 to 4% is considered as the optimum asphalt content. Three optimum asphalt contents of 7.1, 6.4 and 5.9 were obtained for the mixture with aggregate grades of fine grading (PPSS 4.75 mm = 35%), medium grading (PPSS 4.75 mm = 27.5%) and coarse grading (PPSS 4.75 mm = 20%), respectively. It should also be pointed out that the air voids of final mixtures are around 4% using these vales of

1.1. Objectives of the study A review of the few literatures on the influential parameters affecting moisture susceptibility of SMA that are produced using WMA technologies (WSMA) provides no clues to the existence of any interactions between important parameters. This is because in previous studies one-factor-at-a-time methodology has been used to optimize and evaluate the parameters. This methodology is very inefficient and furthermore gives absolutely no information about interactions between parameters. The aim of the present study was to examine the effects of the percent of materials passing sieve size 4.75 mm (PPSS 4.75 mm), bitumen content (BC), Zycosoil content (ZyC), Sasobit content (SaC) and mixing Temperature (T) on moisture susceptibility of

43

Fig. 1. Grading size distribution of the coarse, medium and fine aggregates.

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Table 1 Properties of the siliceous aggregate used.

3. Experimental methods

Test

Standard

Values (%)

MS – 2 Specifications (%) [31]

LA Abrasion Loss

AASHTO T96 ASTM D5821 ASTM D5821 AASHTO T182 BS – 812 AASHTO T176 AASHTO T104

19

< 30

100

–

93

90 <

97

95 <

20 75

< 25 50 <

2.90 0.40

< 12 <8

Crushed in one Face Fractured Particles in two Face and More Coating of Aggregate Flakiness Sand Equivalent Sodium Sulphate Soundness

The aim of the modified Lottman Test (AASHTO T283) [33] is to evaluate susceptibility characteristics of the mixture to water damage. The test is performed by compacting specimens to an air void level of six to eight percent. Three specimens were selected as control and tested without moisture conditioning, and three more specimens were selected to be conditioned by saturation with water (70–80% saturation level). The specimens were then tested for indirect tensile strength by loading the specimens at a constant rate (50 mm/min) and measuring the force required to break them. The tensile strength of the conditioned specimens were compared with the control specimens to determine the tensile strength ratio (TSR). The tensile strength ratio is calculated as follows:

Tensile Strength RatioðTSRÞ ¼

Table 2 Specific gravity of the siliceous aggregate used. Fraction

Standard

Specific gravity (coarse agg.) Bulk SSD Apparent Specific gravity (fine agg.) Bulk SSD Apparent Specific gravity (filler)

3.1. Indirect tensile strength test

Bulk Specific Gravity

ASTM C 127 2.585 2.601 2.626 ASTM C 128 2.587 2.603 2.632 2.619

Test

Standard

Result

Ductility at 25 °C (cm) Penetration at 25 °C, 100 g (0.1 mm) Softening point (°C) Specific gravity at 25 °C Frass Breaking Point

ASTM D113 ASTM D5 ASTM D36 ASTM D70 EN1427

14 31 57.8 1.025 -10

Table 4 Properties of polymer used in the study. Result 3

Density at 23 °C (g/cm ) Apparent Density (g/l) Elongation at Break at 23 °C Modules of Elasticity (MPa)

ð1Þ

Where: ITS dry = average indirect tensile strength of the dry subset, (kPa). ITS saturate = average indirect tensile strength of the conditioned subset, (kPa). 3.2. Design of experiments

Table 3 Properties of PMA used in the study.

Property

ITS saturate  100 ITS dry

0.924 500 % 860 62

asphalt content. To achieve the optimum drain- down of the SMA mixes, cellulose fiber equivalent to 0.35% of the total mixture’s weight was also added to the mix. Table 7 lists the average values of drain-down of different SMA determined from the drain-down test AASHTO T305-97 [32]. Most researchers are of the opinion that the traffic load is carried by the aggregate structure in WSMA, and the mastic role is to enhance the durability of the mix. It is obvious that the mechanical properties of the WSMA is dependent on its constituent materials such as aggregate and bitumen as the air void is kept constant for all the mixtures and therefore the effects of varying amounts of these two materials on the mix properties are studied in this research.

To investigate the effects of pertinent parameters on WSMA properties, response surface methodology was employed. A central composite design was adopted in this research to study five factors at three levels. 32 experimental runs including six face centers were generated with five factors and three levels by the principle of RSM using MINITAB Release 15 [34]. The CCD design matrix employed includes different levels of factors as presented in Table 8. A quadratic polynomial regression model proposed by Montgomery [20] as shown in Eq. (2) was chosen for predicting the response variable in terms of the four independent variables chosen for study:

Y ¼ b0 þ

5 5 4 X 5 X X X bi X i þ bii X 2i þ bij X i X j i¼1

i¼1

ð2Þ

i¼1 j¼iþ1

In this equation Y is the response variables (i.e. TSR) and b0, bi, bii, and bij are constant coefficients of intercept, linear, quadratic and interactive terms, respectively, and Xi and Xj represent the five independent variables (i.e. bitumen content, grading, Zycosoil content, Sasobit content and mixing temperature). The experiments were carried out with three replicates. The statistical significance of the developed model as well as the effect of factors (linear, quadratic and interactive terms) were evaluated by analysis of variance (ANOVA). This procedure was carried out using MINITAB Release 15. 4. Results and discussion 4.1. Model fitting To test the significance of different parameters the P value which is the probability of obtaining a test variable at the least

Table 5 Properties of Zycosoil nano-material. Color Clear to pale yellow

Solid content 41%

Flash point 80 °C

Viscosity at (25 °C) 200–800 cPs

Solubility Soluble in bitumen

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M. Khedmati et al. / Construction and Building Materials 144 (2017) 42–49 Table 6 General properties of Sasobit additive. Congealing temperature (°C) ASTM D 928

Flash point (°C)

Odor

Color

Physical state

Min 100

5.34

No odor

Greyish-white

Pastilles and pills

results of the ANOVA are presented in Table 9; the low P values for the regression (P < 0.1) and the fact that the lack of fit of the model is not significant (P > 0.1) indicates the suitability of the model. According to the results of Table 10, all the first and second order terms of the independent parameters plus all interactive terms are significant at 90% confidence level. Based on the regression coefficients calculated for the response shown in Table 10 following polynomial regression model (R2 = 97.54) is proposed:

Table 7 Average values of drain-down for different SMA. SMA

Aggregate Grades

Bitumen Content (%)

Average of drain-down (%)

1 2 3

Fine Medium Coarse

7.1 6.4 5.9

0.17 0.16 0.13

extreme value as the one that was actually observed should be calculated. P-value indicates the appropriateness of rejecting the null hypothesis in a hypothesis test. Large P-value indicates high probability that the null hypothesis should be rejected. Before conducting any analyses in the above hypothesis it is recommended the alpha level (i.e. the significance of the test) should be determined. The alpha values between 0.05 and 0.1is commonly used for this parameter. If the P-value is less than the assumed alpha, the null hypothesis is rejected. P-value is calculated from the observed sample. It represents probability of incorrectly rejecting the null hypothesis (i.e. type 1 error). Table 8 lists the values of responses at each of the 32 combinations of factorial levels generated by the principles of RSM. The

TSR ¼ 379:415 þ 4:201  ðPPSSÞ þ 198:093  ðZyCÞ þ70:769  ðBCÞ þ 60:183  ðSaCÞ þ 1:583  ðTÞ 0:164  ðPPSSÞ2  924:898  ðZyCÞ2  5:489  ðBCÞ2 5:479  ðSaCÞ2  0:009  ðTÞ2 þ 1:114  ðPPSSÞ  ðZyCÞ þ0:294  ðPPSSÞ  ðBCÞ þ 0:222ðPPSSÞ  ðSaCÞ þ0:020  ðPPSSÞ  ðTÞ þ 6:016  ðZyCÞ  ðBCÞ þ4:057  ðZyCÞ  ðSaCÞ  0:270  ðZyCÞ  ðTÞ 5:398  ðBCÞ  ðSaCÞ þ 0:042  ðBCÞ  ðTÞ 0:052  ðSaCÞ  ðTÞ ð3Þ

Table 8 Central composite design arrangement and responses for mixtures. Run

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1 2 3 4 5

Factors

Responses

PPSS1 4.75 mm (%)

ZyC2 (%)

BC3 (%)

SaC4 (%)

T5 (°C)

TSR (%)

20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 20.0 35.0 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5

0.0 0.0 0.2 0.2 0.0 0.0 0.2 0.2 0.0 0.0 0.2 0.2 0.0 0.0 0.2 0.2 0.1 0.1 0.0 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

5.5 5.5 5.5 5.5 7.5 7.5 7.5 7.5 5.5 5.5 5.5 5.5 7.5 7.5 7.5 7.5 6.5 6.5 6.5 6.5 5.5 7.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 1.5 1.5 1.5 1.5 1.5 1.5 0.5 2.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5

140 100 100 140 100 140 140 100 100 140 140 100 140 100 100 140 120 120 120 120 120 120 120 120 100 140 120 120 120 120 120 120

34 20 45 40 50 56 60 57 63 59 64 67 51 54 67 77 77 78 71 83 76 86 72 90 81 84 87 87 88 87 88 88

Percent Materials Passing Sieve Size 4.75 mm: Low Level = 20%, Mean Level = 27.5% and High Level = 35%. Zycosoil Content: Low Level = 0%, Mean Level = 1.5% and High Level = 3%. Bitumen Content: Low Level = 5.5%, Mean Level = 6.5% and High Level = 7.5%. Sasobit Content: Low Level = 0.5%, Mean Level = 1.5% and High Level = 2.5%. Mixing Temperature: Low Level = 100 °C, Mean Level = 120 °C and High Level = 140 °C.

ITS

dry

(kPa)

233 201 290 260 320 343 371 350 388 360 400 434 327 336 450 510 501 520 468 570 495 598 477 700 563 577 630 620 657 612 650 660

ITS

saturated

80 40 131 104 160 192 223 200 244 213 259 291 170 183 305 395 386 406 336 477 380 514 347 630 459 486 552 540 580 534 570 582

(kPa)

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M. Khedmati et al. / Construction and Building Materials 144 (2017) 42–49

Table 9 ANOVA for TSR values.

Total Regression Residual error Lack of fit (model error) Pure error (Replicate error) R2 1 2 3

DF1

SS2

MS3

F-values

P-values

31 20 11 6 5 97.54

10245.9 10243.9 2.1 0.4 1.6 –

– 512.19 0.19 0.07 0.33 –

– 2728.97 0.22 – –

– 0.000 0.955 – –

DF: degrees of freedom; SS: sum of squares; MS: mean square.

Table 10 Values of regression coefficients calculated. P-value

T-value

Standard error

Regression coefficient

Constant

Independent factor

0.000

-28.098

13.5032

-379.415

Linear PPSS 4.75 mm ZyC BC SaC T

0.000 0.000 0.000 0.000 0.000

13.994 16.638 19.248 45.056 9.263

0.3002 11.9060 3.6767 1.3357 0.1709

4.201 198.093 70.769 60.183 1.583

Quadratic PPSS 4.75 mm ZyC BC SaC T

0.000 0.000 0.000 0.000 0.000

-33.490 -33.490 -19.875 -19.840 -13.575

0.0049 27.6173 0.2762 0.2762 0.0007

-0.164 -924.898 -5.489 -5.479 -0.009

0.000 0.000 0.000 0.000 0.000 0.030 0.000 0.000 0.000 0.000

7.713 20.383 15.370 28.314 5.555 3.745 -4.981 -49.837 7.690 -9.656

0.1444 0.0144 0.0144 0.0007 1.0831 1.0831 0.0542 0.1083 0.0054 0.0054

1.114 0.294 0.222 0.02 6.016 4.057 -0.27 -5.398 0.042 -0.052

Interactive PPSS 4.75 mm PPSS 4.75 mm PPSS 4.75 mm PPSS 4.75 mm ZyC -BC ZyC - SaC ZyC - T BC- SaC BC- T SaC-T

– ZyC –BC –SaC –T

Where:PPSS 4.75 mm = Percentage of materials passing sieve size 4.75 mm (%);ZyC = Zycosoil content (%);BC = Bitumen content (%); SaC = Sasobit content (%);T = Mixing Temperature (°C). 4.2. Main effect plot Fig. 2 shows the ‘‘Main Effects Plot”, which is a plot of the mean value of the results obtained at each level of a factor. A main effects plot can be drawn for either the measured or predicted values of the response for each level of a factor. Main effect plots show the relative strength of the effects across factors. It should be noted that these plots are used in conjunction with an analysis of variance and design of experiments to examine differences among mean levels for one or more factors [35–37]. When different levels of a factor affect the response differently a main effect is present. These graphs plot the mean response for each factor simply connected by a straight line. Fig. 2 shows that the influence of ZyC is more than the influence of BC, SaC, grading (PPSS 4.75 mm) and temperature (T) on TSR when each parameter moves from its low level to its mid-level. After that TSR decreases when PPSS 4.75 mm, BC, ZyC, SaC and T move from their mid-level to high level. It can be observed that the influence of PPSS 4.75 mm is more than other parameters in decreasing TSR when they move from mid (medium grading) to high level (fine grading). From main effect plot these findings can be derived:

1- Although it was previously reported that in SMA mixtures with larger amount of fine material and more mastic, a better moisture resistance is observed that could be attributed to a better coating of large aggregates with mastic [23,29], the optimum grading appears to be around the mid-level of PPSS 4.75 mm (27.5 percent of materials passing sieve size 4.75 mm). Hence, the maximum TSR value was achieved at this mid-level. 2- 0.1% ZyC as an anti-stripping agent can lead to maximum TSR. 3- Fig. 2 shows that the maximum value of TSR occurs in around the mid-level of BC (6.5%) where the optimum value of bitumen content for mixture with medium grading (midlevel = 27.5%) was determined. 4- The amount of SaC around 1.5% can lead to maximum TSR. 5- Typically mixing temperature used in WMA production is in the range of 100–140 °C. Fig 2 shows the optimum value of temperature for achieving maximum TSR is around 120 °C (mid-level of temperature). 4.3. Interactive effects of parameters An interactions plot is a plot of means for each level of a factor with the level of a second factor held constant. Interaction plots are useful for judging the presence of interaction. Interaction is present when the response at a factor level depends upon the level(s) of

M. Khedmati et al. / Construction and Building Materials 144 (2017) 42–49

47

Fig. 2. Main effect plots of TSR versus PPSS 4.75 mm, ZyC, BC, SaC, T.

other factors. Parallel lines in an interaction plot indicate no interaction. The greater the departure of the lines from the parallel state, the higher the degree of interaction [34]. Fig. 3 shows that at constant BC (6.5% of the mixture) an increase in the amount of PPSS 4.75 mm from low level to mid-level results in enhancement of TSR and after that the values of TSR decrease when PPSS 4.75 mm moves from mid-level to high level. Also, it can be observed that at a constant level of PPSS 4.75 mm (i.e. 35%) when the amount of BC increase, the value of TSR initially increases to

reach its maximum (TSRmaximum) at around 80% and then decreases. It can be observed that the maximum TSR occurs around midlevel of PPSS 4.75 mm (27.5%) where the grading is also at its optimum value. Also, in around mid-level of BC (6.5%) the maximum TSR is achieved that it is the optimum bitumen content for WSMA mixtures with medium grading (PPSS 4.75 = 27.5%). Same analysis can be carried out about other parameters that affect TSR.

Fig. 3. Interaction Plots of TSR.

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M. Khedmati et al. / Construction and Building Materials 144 (2017) 42–49

From a physical point of view it could be justified that the amount of BC has a major influence on moisture resistance of asphalt mixtures. In other words, an incorrect selection of BC may increase the moisture susceptibility. In fact when the amount of binder is less than optimum, the moisture may cause loss of adhesive bond between the asphalt and aggregate (a failure of the bonding of asphalt to aggregate). On the other hand when the binder is more than the optimum and relatively speaking moisture has more access to binder in comparison with aggregate/ binder interface, it has more opportunity to penetrate within the binder itself causing cohesion failure [23,29]. As mentioned earlier, WMA additives are added to mixtures to reduce binder viscosity permitting a desirable mix at a lower temperature compared with hot mixtures. The results indicate that increasing the amount of Sasobit in the mixture will cause an increase in the TSR values. However, after a specific optimum Sasobit content (1.5%) the TSR values decrease. The extra Sasobit will reduce viscosity of the binder further affecting aggregate coating and exacerbate the loss of bond between asphalt binder and aggregates. The lower mixing and compaction temperatures can result in incomplete drying of the aggregate. The obtained results indicate that increasing the mixing temperature can improve moisture resistance of WMSA possibly by decreasing moisture trapped in the aggregates. This trend can manifest itself as a positive sign of the interactive term in the mathematical model (Eq. (1)). In addition, the interactive effect of temperature and binder on moisture resistance has a positive sign in the mathematical model which shows that increasing temperature can decrease viscosity of binder and result in better coating of aggregate. However, after the specific temperature (i.e. 120 °C), the temperature has negative effect on moisture resistance of WMSA. This might be related to the interactive of Sasobit and temperature on moisture resistance of mixtures which manifest itself by negative sign in the mathematical model. In other word, the temperature and Sasobit both decrease the viscosity of binder and it leads to loss of bond between asphalt binder and aggregates. Fig. 3 indicates that an increase in the mastic amount causes an increase in TSR, this could be attributed to a better coating of large aggregate with asphalt available in the mastic. Fig. 3 also shows that an increased amount of BC over and above the optimum value can decrease its TSR value that indicates an increase in the moisture susceptibility of WSMA. As indicated in Section 2.1 the aggregate used in all mixtures was siliceous aggregate. The aggregate predominantly comprised of SiO2 that creates an unstable bond with bitumen against water attack. When such mixes are exposed to moisture, water is rapidly absorbed by the aggregate and detaches from the binder. It is believed that the high percentage of hydroxyls on the surface of siliceous aggregate makes it very suitable for Zycosoil as it permanently changes the physical properties of aggregate surface [19,38].

5. Optimization of the process response The derived models can be used for interpolation within the levels of the studied parameters to achieve the maximum (or a minimum) desired response [21,34,39]. Based on factors that were taken into consideration in this research, the aim would be to achieve a maximum for TSR. The maximum response was achieved under the following conditions: TSR = 89.99% ± 0.99, PPSS 4.75 mm = 28%, ZyC = 0.1%, BC = 6.5%, SaC = 1.9% and T = 123 °C.

6. Conclusion This study examined the effects of the percent of materials passing sieve size 4.75 mm (PPSS 4.75 mm), bitumen content (BC), Zycosoil content (ZyC), Sasobit content (SaC) and mixing temperature (T) on moisture susceptibility of WSMA, as well as interactions between them, using proper methodology, namely RSM. The obtained results imply that a proper selection of aggregate grading and amount of anti-stripping agent could satisfy the minimum requirement of durability against moisture damage in WSMA. In addition, the results testify that there are some interactions between different parameters affecting TSR of the WSMA and hence a closer attention should be paid to selection of different proportions of the mix components and the aggregate grading. From main effect plot of TSR it could be concluded that the influence of ZyC is more than the influence of BC, SaC, grading (PPSS 4.75 mm) and mixing temperature (T) on increasing TSR when each parameters moves from its low level to its mid-level. However, the influence of grading (PPSS 4.75 mm) is more than the influence of BC, SaC, ZyC and T on decreasing TSR when each parameter moves from its mid-level to its high level. Finally, it can be observed that Zycosoil can improve moisture resistance of WSMA where the siliceous aggregates used. This can be related to high percentage of hydroxyls on the surface of siliceous aggregate which makes it very suitable for Zycosoil. References [1] E.R. Brown, J. Haddock, R. Mallick, T. Lynn, Development of a mixture design procedure for stone matrix asphalt (SMA), National Center for Asphalt Technology, Auburn University, Report 97–03, 1997. [2] E.R. Brown, L.A. Cooley, Designing Stone Matrix Asphalt Mixtures for RutResistant Pavements, NCHRP Reports 425, Transportation Research Board, National Research Council Washington DC, 1999. [3] E.R. Brown, Experience with stone matrix asphalt in the United States, National Center for Asphalt Technology, Auburn University, Report 93–04, 1992. [4] J.T.G. Richardson, Stone Mastic Asphalt in the UK Symposium on Stone Mastic Asphalt and thin Surfacing, London, Richter E, 1997. [5] F. Moghadas Nejad, E. Aflaki, M.A. Mohammadi, Fatigue behavior of SMA and HMA mixtures, J. Constr. Build. Mater. 24 (2010) 1158–1165. [6] J.P. Serfass, J. Samanous, Fiber-modified asphalt concrete characteristics, application and behavior, J. Assoc. Asphalt Paving Technol. 65 (1996) 193–230. [7] T. Süreyya, O. Halit, A. Atakan, Investigation of rutting performance of asphalt mixtures containing polymer modifiers, J. Constr. Build. Mater. 21 (2) (2007) 328–337. [8] Arif Chowdhury, Joe W. Button, A review of warm mix asphalt, Texas Transportation institute, Texas A&M University, Report No. SWUTC/08/ 473700-00080-1, 2008. [9] M. Stroup-Gardiner, C. Lange, Characterization of Asphalt Odors and Emissions, Proceedings of the Ninth International Conference on Asphalt Pavements, August 17–22, Copenhagen, Denmark, 2002. [10] O. Kristjansdottir, S. Muench, L. Michael, G. Burke, Assessing potential for warm-mix asphalt technology adoption, J. Transp. Res. Board 2040 (2007) 91– 99. [11] B. Prowell, G. Hurley, E. Crews, Field performance of warm-mix asphalt at national center for asphalt center, asphalt technology test track, J. Transp. Res. Board 1998 (2007) 96–102. [12] A. Khodaii, H. Kazemi Tehrani, H.F. Haghshenas, Hydrated lime effect on moisture susceptibility of warm mix asphalt, J. Constr. Build. Mater. 36 (2012) 165–170. [13] O. Kristjansdottir, S. Muench, L. Michael, G. Burke, Assessing potential for warm-mix asphalt technology adoption, J. Transp. Res. Board 2040 (2007) 91– 99. [14] B. Prowell, G. Hurley, E. Crews, Field performance of warm-mix asphalt at national center for asphalt center, Asphalt Technology Test Track, J. Transp. Res. Board (1998) (2007) 96–102. [15] G. Hurley, B. Prowell, Evaluation of SasobitÒ for use in Warm Mix Asphalt, NCAT Report 05–06, Auburn, 2005. [16] F. Xiao, V.S. Punith, S.N. Amirkhanian, Effects of non-foaming WMA additives on asphalt binders at high performance temperatures, J. Fuel (94) (2012) 144– 155. [17] A. Khodaii, H.F. Haghshenas, H. Kazemi Tehrani, Effect of grading and lime content on HMA moisture susceptibility using statistical methodology, J. Constr. Build. Mater. 34 (2012) 131–135. [18] A. Kavussi, M. Qorbani, A. Khodaii, H.F. Haghshenas, Moisture susceptibility of warm mix asphalt: a statistical analysis of the laboratory testing results, J. Constr. Build. Mater. 52 (2014) 511–517.

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