Compaction process tracking for asphalt mixture using discrete element method

Construction and Building Materials 235 (2020) 117478

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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat

Compaction process tracking for asphalt mixture using discrete element method Guoping Qian a, Kaikai Hu a, Jue Li a,⇑, Xianping Bai a, Ningyuan Li b a b

School of Traffic and Transportation Engineering, Changsha University of Science & Technology, China Department of Civil and Environmental Engineering, University of Waterloo, Canada

h i g h l i g h t s
A packing mode of binary particles was developed to determine fine size limitation.
The compaction process was tracked by air void, aggregates and asphalt mortar.
A numerical technique to simulate the laboratory compaction was proposed.

a r t i c l e

i n f o

Article history: Received 27 May 2019 Received in revised form 25 September 2019 Accepted 3 November 2019

Keywords: Asphalt mixture Compaction method Air void content Critical aggregate size Large-scale specimen Discrete element method

a b s t r a c t Although the same compaction degree is achieved in practice, asphalt mixture samples prepared by different compaction methods often have different mechanical properties. In this paper, the air void content (AV) and distribution of aggregates and asphalt mortar in the process of asphalt mixture compaction are traced to capture the meso structural change characteristics of asphalt mixture during compaction. Using the discrete element method (DEM), a numerical technique is developed to simulate the laboratory compaction by taking into account the critical aggregate size and boundary effect. First, the critical aggregate size (CAS) is determined by the 2D and 3D binary particle assembly. Second, DEM simulations of both the Marshall impact compaction (MIC) and static compaction (SC) methods are conducted by the mass-wall and servo boundary, respectively. Third, the applicability of the 2D model is demonstrated through laboratory tests and numerical calculations. Finally, the distribution of aggregates and asphalt mortar are displayed and analyzed. The results show that the variation of CAS presents linear growth approximately with the increase of coarse particle size, less affected by the boundary. The primary control sieve (PCS) is applicable to separate the coarse and fine particles in the 3D assembly, but the CAS is around 0.195 for the 2D assembly, which is obviously less than the PCS. It is verified by two compaction methods and two mixture gradations that the DEM simulation is an effective way to evaluating the compacting effects of the compaction process. By double-sided hammering, coarse aggregates are moved to accumulate more closely, thus the coordination number at the bottom increases. Although a dense specimen can be achieved by compaction method, the size distribution of particles is still uneven in horizontal direction, since the position of large size particles (>16 mm) is difficult to be changed in the compaction process. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction The compaction process of asphalt mixture is an essential link in the design and construction of asphalt pavement by ensuring a high compaction level, sufficient stiffness, and excellent antrutting performance [1]. The inadequate compaction level of materials results in a great air void content (AV), in which 1.0% increase

⇑ Corresponding author. E-mail addresses: [email protected] (K. Hu), [email protected] (J. Li). https://doi.org/10.1016/j.conbuildmat.2019.117478 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.

accelerates 10% change in service life [2]. In recent years, many studies have been undertaken to track the process of both laboratory and field compaction, and three main factors influencing the compaction process have been taken into account as the following: (1) distribution and characteristics of aggregates, (2) content and type of asphalt binder, and (3) environment and method of compaction [3–5]. Most of these compacting parameters are easily quantified and controlled in laboratory analysis and simulation [6]. However, it is unclear why specimens produced by different compaction methods tend to different mechanical properties in spite of reaching the same degree of compaction [7].

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G. Qian et al. / Construction and Building Materials 235 (2020) 117478

In order to reproduce field conditions, standard molded samples have been manufactured in laboratory by several compaction methods including Marshall impact compaction (MIC), Superpave gyratory compaction (SGC), static compaction (SC), and linear kneading compaction (LKC). The MIC is the oldest but still the most widely used method [8]. The SGC is close to the field stress but still heterogeneous for aggregate orientation and AV distribution [9]. Mollenhauer et al. [10] proved that the specimens molded by the MIC and SGC have different internal skeleton structures with greater rigidity and permanent deformation resistance than the field samples. Other studies have attempted to determine kneading motions and internal AV distribution as a result of the difference between laboratory and field compaction by means of the X-ray CT technique [11,12]. Masad et al. [13] compared the internal structure of the SGC and LKC specimens and found that aggregates of the SGC have more preferred orientation and fewer contacts than the LKC and the size distribution of the air void is also different from the top to the bottom. Moreover, the fact has been recognized that the homogeneity of both aggregate and AV distributions is more easily controlled at the center of the molded sample than around the edge [14]. Therefore, a series of standard specimens with uniform compaction level are probably obtained by coring large-scale asphalt mixture samples to avoid the influence of mold boundaries. Meanwhile, the best way to produce the large-scale specimen is to develop a precision equipment for simulated roller compaction in; however, it is obvious that this is not convenient and requires significant effort [15]. Aggregate specifications have not been thoroughly researched despite its importance in compaction [16]. The distribution curve and critical aggregate size were always recommended through field data surveys and experience [3]. Several attempts have been made to clarify the correlation between aggregate parameters and mixture performance. Considering the constructability and traffic indices, Bahia et al. [17] presented two indices, the compaction energy index (CEI) and the traffic densification index (TDI), to evaluate the compacted results and optimize the construction design. In the Bailey method, the gradation of aggregates was analyzed in detail and quantified in terms of curve shape features by three parameters: coarse aggregate (CA) ratio, fine aggregate ratio of coarse portion (FAc), and fine portion of fine aggregate (FAf) [18]. These Bailey parameters were determined on the classification of coarse and fine aggregates in a 2D plane [19]. However, this preliminary design of dense mixture is still a static approach on laboratory tests and experience. Recently, numerical simulation has been applied as an alternative to measure aggregate position, displacement, and rotation during the compaction process [20,21]. Over the past decade, the 2D discrete element method (DEM) has shown significant advantages in computational efficiency and particle motion description [22], and is often coupled with digital image technology (DIT) to characterize the dynamic mechanical process of the materials assembly [23]. Through a DEM simulation, the effect of aggregate characteristics and compaction methods on compaction level can be evaluated by preprocessing particles and boundary [24]. The objective of this study is to investigate the compaction process by tracking the AV and distribution of aggregates and asphalt mortar. Meanwhile, a numerical technique is developed to simulate the laboratory compaction by taking into account the critical aggregate size and boundary effect. This paper is organized as follows. The forthcoming section introduces the function and research background of the critical aggregate size. Meanwhile, a packing model of binary particle assembly was developed to determine the size limitation of fine particles. After that, it presents the preparation of asphalt mixture and description of compaction methods. And the 2D modeling procedures of both the MIC and SC methods are also described in detail. Following this, the applica-

bility of the 2D model is demonstrated through laboratory tests and simulation. The comparison of double-sided and single-sided compaction are discussed. The distributions of aggregates and asphalt mortar are also displayed in the horizontal direction. The final section summarizes the major findings of this study.

2. Determination of critical aggregate in packing analysis 2.1. Background The critical aggregate size (CAS) was defined as the control sieve size to distinguish the coarse and fine aggregates in particle analysis and is critical to evaluate packing behaviors of mixtures. In general, the packing result of mixtures can be divided into the framework-dense structure and the suspend-dense structure, based on the control sieve and the content of fines (FC). However, the CAS for different gradation designs is not identified as an invariable value, e.g., the No. 4 sieve (4.75 mm) for cement concretes and the No. 8 sieve (2.36) for asphalt mixtures in China. Meanwhile, it is unreasonable that the fixed control criterion is applied to evaluate the packing behavior of mixtures for different nominal maximum particle sizes (NMPSs), by which the FC designed declines significantly with the increase of the NMPS in a series of dense gradation curves [Eq. (1)] provided by Fuller [25]. Therefore, the attempts to determine the alterable CAS for different NMPSs were carried out on trails with particle interface theory [26] and geometrical calculations [27].


PðiÞ ¼ 100

Di Dmax

n ð1Þ

where, P(i) stands for the percentage of passing through No. i sieve; Di stands for the diameter of No. i sieve; Dmax stands for the maximum aggregate size; n is the exponent (0.45~0.7 by default). Weymouth et al. [26] believed that the idealized dense accumulation meets the non-interference of particles that the voids between first sieve particles should be filled by the secondary sieve particles, whoso remaining voids should be filled by the next sieve particles in turns. Then the further works on its packing theory were conducted for a decade years, from which the Bailey method was summarized to evaluate the volumetric features of asphalt mixtures. The CAS for the Bailey method is approximate to be the mean result in the mathematical model of 2D circles, which was displayed in Fig. 1 including four combinations of interfaces between three pile-up particles [18]. Thus, the coarse and fine aggregates were separated by the primary control sieve (PCS), which is 0.22 of the NMPS. However, it is still an empirical method in spite of the practical significance, since it cannot be verified by theory models.

2.2. Developed packing model of binary particle assembly More and more attentions have been paid to explore the packing behavior of binary particle assemblies with the demand for further sense of particle structure and engineering problems [28]. Most of algorithms were reported to analyze the packing behavior taking coarse and fine particles as a whole, but it remains a challenge that the minimum diameter for limiting fine particles is unclear when coarse particles are packed first. At the same time, it is evident that the declining the size of fine particles in a box requires more particles which raises computational costs. Thus, a new packing mode was developed to determine the size limitation of fine particles, which can be used to optimize the packing model of mixtures.

G. Qian et al. / Construction and Building Materials 235 (2020) 117478

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Fig. 1. Void between three circles with the same diameter (Dc): (a) 0.15Dc for all roundness; (b) 0.20Dc for two roundness and one flakiness; (c) 0.24Dc for one roundness and two flakiness; (d) 0.29Dc for all flakiness.

The developed algorithm was implemented as following steps in a general DEM software [29]. A 2D schematic diagram and a 3D example of packing model are presented in Fig. 2. (1) The first step is the compaction and fixation of coarse particles. Coarse particles were randomly accumulated in gravity within the cylinder boundary of 100 mm in diameter. The skeleton structure remains constant in the next steps by resetting the velocity and fixing the positions of coarse particles. (2) The second step is to fill the fine particles. The command GENERATE was utilized to create an assembly of balls without overlapping each other or any preexisting balls. It generated 1000 fine particles with 1 mm in diameter, which are distributed randomly in voids between pre-generated particles. If the fine particles were in contact with the boundary, the contacted particles would be deleted. (3) The last step is to amplify the radius of fine particles. The radius of fine particles was magnified by 1.01 and balanced by 1000 cycles in particle systems to full fill the void gaps of coarse particles. This process was repeated until the contact number of one of fine particles reaches the target value (3 for 2D and 4 for 3D). Note that a monitoring program is needed to identify the contact within fine particles, on which one of particles would be deleted to ensure that the contact number of filled particle is effective. In final, the current diameter of fine particles is viewed as the minimum filling size of the void between coarse particles.

2.3. Analysis of critical aggregate in 2D and 3D The developed packing models were implemented by four main sieve of coarse particles in asphalt mixture. The diameters of coarse

particles are 9.6 mm, 13.2 mm, 16 mm, 19 mm, and 26.5 mm, respectively. Error bars displays that it exists a difference between multiple simulated results since the structural feature of packing models is random in space. In order to ensure the reliability and stability of filling results, 9 set of tests were carried out for each diameter. The results of fine particle filling tests are shown in Fig. 3. The initial AV of packing models raises along with the increasing size of coarse particles. Due to the effect of boundary, the gradient for change of AV is more rapid with the coarse particle size. Yet it is obvious that the filling results in packing models are less affected by the boundary, since the minimum filling size presents linear growth approximately. The linear fitting results were marked by the dark (blue) line with more than 95% in the R-square. In the 2D model, the slope (0.195) of fitting line is lower than 0.22 determined by the Bailey method, whose R-square is 90.58%. In the 3D model, the results of filling size are close to the line of 0.22 slope with 97.73% R-square. From the packing analysis, it suggests that the PCS (0.22NMPS) is applicable to separate the coarse and fine particles in 3D, although it doesn’t take into account the random arrangement of spheres in space. This finding is consistent with the practice research of the Bailey method [18]. For the 2D assembly, the CAS is around 0.195, which is obviously less than the PCS.

3. Testing materials and methods 3.1. Asphalt binder and aggregates The AH70#-A basis asphalt from Maoming in China was used in this study. The physical properties of the basis asphalt are presented in Table 1. These tests are in accordance with relevant Chinese standards [30,31]. The mechanical behavior of asphalt materials must be taken into consideration to understand the per-

Fig. 2. Packing model of large particles with 16 mm in diameter: (a) 2D Representation; (b) 3D Result for assembly filled by 1000 spheres.

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G. Qian et al. / Construction and Building Materials 235 (2020) 117478

Fig. 3. Simulated results for filling coarse particles: (a) 2D model; (b) 3D model.

formances of mixtures. Many advanced models have been presented, e.g. 2S2P1D, which describe the linear viscoelastic rheological properties of binders accurately [32,33]. However, it is a complex and pending work to implement the relevant constitutive model in the DEM, and the Burger’s model was used as a simplify substitute in this study. Burger’s model is one of practical models for predicting the viscoelastic behavior and was successfully applied to the DEM calculation. Its constitutive equation for the contact between discrete particles is given by Eq. (2). According to the fitting result mentioned by Majidi [34], the macro-scale parameters of Burger’s model are presented in Table 2. And then the parameters in each contact can be obtained through the macro-scale parameters multiplied by the diameter of particles.

 Fþ


 Ck 1 1 C k C m 00 C k C m 00 F0 þ þ Cm þ F ¼ C m U 0  U Kk Km Kk KkKm Kk

ð2Þ

where, F stands for the contact force; U stands for the total displacement of the Burger’s model. The samples in this study consist of AC-20 and AC-25, both of which are widely applied as the asphalt surface course in China’s highways. The curve of these aggregate gradations is showed in Fig. 4. The aggregate in the samples is selected as the limestone from Zhuzhou in China. Table 3 shows the properties of testing aggregates according to the Chinese standard [35]. Through the Marshall mixture proportion design, the optimum bitumen-stone ratios of AC-20 and AC-25 are estimated at 4.6% and 4.0% by mass, respectively. 3.2. Compaction methods Temperatures of mixture and compaction were carefully controlled to 163 °C and 150 °C respectively, to ensure high quality specimens produced by all compaction methods. The laboratory compaction methods studied herein mainly include MIC and SC

in accordance with Standard JTG E20-2011 in China [31]. In general, the volume density determined by laboratory compacting tests was considered as the target density in the filed compaction. Samples were prepared via the MIC method according to AASHTO T245. The size of standard molds is 101.6 mm in diameter, and 63.5 mm in height. The 4536 g ± 9 g hammer is lifted and released at the height of 457.2 mm ± 1.5 mm. The drive mechanism allows free fall at a nominal 50 impacts within 1 min. The purpose of this experiment is to compare it with the static pressure method which is only subjected by the single-sided loading. Meanwhile, the boundary effect on the uneven distribution is also a topic of this study. Therefore, these MIC specimens in laboratory were hammered by 150 times at the same direction, differing from two side hammering in standard test method. This treatment is also convenient to measure the height and the AV of specimens. The materials and gradation of the SC are the same as that of the MIC. The cylindrical specimen produced by the SC is 100 mm  100 mm. The mold is 180 mm in height. Both the mold and spacer containing asphalt mixture are placed on the press platform and preloaded with a pressure of 0.1 MPa to ensure sufficient contact between the indenter and the asphalt mixture. Following this, the press plate rises at a certain speed and compacts the specimen until it reaches the target height.

3.3. Description of compaction process Compaction is intended to reduce AV, optimize the aggregate contact behavior, and increase bulk specific gravity. Once the volume parameter of the compacted specimen during the loading process has been determined, the density or AV is calculated using the following equations:

qcur ¼

W=V

Table 1 Measurements and specifications of the basis asphalt. Performance

Value

Required

Penetration (25 °C, 100 g, 5 s) (0.1 mm) Ductility (5 cm/min, 10 °C) cm (5 cm/min, 15 °C) cm Softening Point TR&B (°C) Penetration index Wax content (%) Solubility (%) Flash point (°C) Density 15 °C (g/cm3)

69 19.3 >100 48 0.76 2.0 99.8 330 1.034

60~80 15 100 46 1.5~+1.0 2.2 99.5 260 Measured

ð3Þ

cw

AV cur ¼ 1 -

ð1 - AV final Þqcur

qfinal

ð4Þ

where V is the current specimen volume (cm3); W is the current specimen weight (g), cw is the density of water (1.0 gcm3 at the temperature of 21 °C), qcur is the current bulk specific gravity at each number of compaction, qfinal is the bulk specific gravity after the compaction, AVcur is the current air void content, and AVfinal is the air void content after the compaction.

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G. Qian et al. / Construction and Building Materials 235 (2020) 117478 Table 2 Parameters of the Burger’s model. Items

Temperature (°C)

Km

Cm

Kk

Ck

Model by Majidi

150

4.83E4

4.64E2

1.26E2

9.22E1

rected cross-section method. Thus, the probabilistic corrected cross-section method was employed in this study.

Nj ¼

n X

ZZ kji Ni ; kji ¼

i¼j

rj  1=2 dr j dri 2 r i r i  r 2j

ð6Þ

4. Description of numerical models for compaction

where, Ni and Nj are the amount of aggregate in No. i sieve and No. j sieve, kji is the probability that an aggregate of No. i sieve was cut into the sectional disk of No. j sieve, and ri and rj are the random aggregate radius in No. i sieve and No. j sieve. For the gradation of AC-20 and AC-25, the CASs are 3.705 and 4.992 respectively, which is close to 4.75 mm. It is assumed in general that both fine aggregate and asphalt binder are mixed together as asphalt mortar [37]. Therefore, the model of asphalt mixture is composed of spherical particles with different sizes representing the coarse aggregate (>4.75 mm) and the asphalt mortar (2.36 mm) respectively.

4.1. DEM simulation of aggregates and asphalt mortar

4.2. DEM simulation of compaction method

The coarse aggregate in the asphalt mixture consists of different size particles whose shapes are irregular and difficult to be described. The geometric characters of the asphalt mixture has been covered in another literature [21]. This study focus on the gradation and movement of aggregates. The shape of the aggregates were simplified into spherical particles. The calculation results with spherical particles are reliable and suitable for displaying the interaction and displacement between aggregates during the compaction [24]. The random assembly of two-dimensional aggregates is mainly related to the conversion relationship of the mass ratio of different sieves from 3D to 2D. There are two common conversion methods [36]: (1) a mass simplified area method and (2) a probabilistic corrected cross-section method. The former directly gets the total area and the amount of particles through the quality ratio of the gradation using Eq. (5).

The default rigid boundary in the DEM software [29] is established by the command WALL and its stiffness, friction coefficient, and motion state are all able to be controlled by the script code. The boundary defined by the WALL command does not allow for bending deformation and has no mass. Using the CLUMP command, a mass-wall boundary was developed in this study since the MIC hammer falls under an inertia force. As shown in Fig. 5 (a), this boundary consists of many arranged particles, of which the masses are assigned to the centroid of the boundary and the relative position are fixed to each other particles. Both the MIC method and the SC method were investigated by the 2D DEM. In the MIC method, a boundary of 10 mm in thickness was generated by small particles with a diameter of 1 mm, whose density qb was calculated using Eq. (7). The initial speed of the boundary was determined by the kinetic energy after the hammer falling process, as shown in Eq. (8). The description of the MIC process is divided into three steps: (1) the vertical speed of the masswall boundary is initialized, (2) asphalt mixture has been compacted and balanced until the speed of the boundary is 0, and (3) count the number of compactions and start the first step again.

Fig. 4. Gradation of aggregates in AC-20 and AC-25 mixture.

N¼

Sð1  nÞ 

p

r2



; r¼

ðr min þ rmax Þ 2

ð5Þ

where, N is the amount of particles, S is the preset area of aggre

gates in a certain sieve, n is the preset porosity, r is the mean of the particle radius, and rmax and rmin are the maximum and the minimum particle radius, respectively. Some researchers views that 2D model is the projection of a three-dimensional model onto a plane. Thus, it exists a stochastic probability model to represent the particle distribution on the projection plane. The distribution of aggregate area in a 2D interface is different from the initial gradation in 3D, as described in Eq. (6). Assuming that the number of aggregates is great enough, the distribution of particle diameter in a plane will be close to that of the section in a realistic specimen. The result of the mass simplified area method will be the special solution of the probabilistic cor-

qb ¼

mh dm t b

ð7Þ

where, mh is the mass of the Marshall hammer, dm is the diameter of the Marshall mold, and tb is the thickness of the clumping boundary.

1 mh v 2i ¼ mh gH 2

ð8Þ

where, vi is the initial vertical speed of the mass-wall boundary, H is the falling height of the Marshall hammer, and g is the acceleration of gravity (9.8 ms2).

Table 3 Physical properties of the crushed limestone. Aggregates

Liquid limit (%)

Plasticity index (%)

Crushed stone value (%)

Acicular content (%)

Fine Coarse

22.4 –

6.7 –

– 21.7

– 10.4

6

G. Qian et al. / Construction and Building Materials 235 (2020) 117478

Fig. 5. Simulations of compaction methods: (a) The MIC method with a mass-wall boundary; (b) The SC method with a servo boundary.

In the SC method, the preloaded pressure is set to 0.1 MPa by using the default rigid boundary in the DEM simulation. The upper rigid wall was applied at a constant speed, which was operated by the servo mechanism in the FISH code, as described in Eq. (9).The speed of the wall is decreased at a rate of 5 mm/s until the height of the specimen reaches the target value (such as 100 mm).

vw ¼

aðr  rmax ÞL K w t step

ð9Þ

where vw is the current speed of the loading plate, a is the relaxation factor with a default of 0.5, r is the current stress of the plate, rmax is the maximum stress required, L is the length of the plate, Kw is the total stiffness of the particles contacting with the plate, and tstep is the current time step. 4.3. Mesoscopic parameters Asphalt mixture is a multiphase system and it is required to define the contact parameters separately for the aggregate and the asphalt mortar. Its internal mechanical behavior can be characterized by contact-stiffness and local damping. The contact stiffness depends on the effective modulus of materials and the radius of particles by the deformability method [29]. The modulus and density of materials can be obtained by Wang’s researches [38]. And then the DEM model of compaction was calibrated by fine adjusting the stiffness of particles in the MIC method, as shown in Table 4. These value was kept constant in the simulation of SC method. Meanwhile, the asphalt mortar was also assigned the Burger’s model in Table 2, to simulate its viscoelasticity at high temperature (150 °C). 5. Results and discussion 5.1. Comparison between laboratory and simulation By measuring variations of the AV in different compaction processes, the compacting effect of two type of asphalt mixtures was evaluated. The AV of laboratory specimens was calculated by Eq. (4). The information on particle sizes was generated for numerical specimens by the same pseudo-random number generator. It is

recognized that the simulated results are disable to reach the density state of the laboratory tests, subject to the 2D packing structure. In this study, the ratio of the current AV to the final AV was applied to characterize the compaction degree of mixtures during operation in the laboratory and simulation. The laboratory-measured and simulated AVs are plotted against the number of hammer and static pressure in Fig. 6. In the laboratory, it is difficult to accurately obtain the AV of double-sided compacted samples. Meanwhile, the SC compaction is always loading at single side. For the purpose of comparing the two compacting methods at same conditions as possible, both numerical simulation and experimental verification were adopted by the single-sided compaction. It is evident that the simulated results are consistent with the experimental results, with the R-square larger than 90%. It is verified that the DEM simulation is an effective way to evaluating the compacting effect of the compaction process. From the results of MIC method, it displays that the compaction degree of specimen increases with increasing the number of hammer, and the specimen may hardly be compacted after it is subjected by 75 times hammer. For the SC method, the limitation of compaction also exists that the AV decreases slowly when the pressure exceeds 40 MPa. Comparing different size gradation, two type of asphalt mixtures have similar variation trends of AVs in the compaction process. It is obvious that the samples of AC-25 gradation are more difficult to be compacted than those of AC-20 gradation. The reason may be that the CA ratio of AC-25 is larger than that of AC-20 and the large size particle resists to be transferred. 5.2. Comparison of double-sided and single-sided compaction In laboratory MIC tests, the specimen must be struck by 75 times hammer per side to lower the uneven distribution. It is not clear how to evaluate the effect of compacting direction on the compaction results of mixtures. Therefore, the specimens of AC20 mixtures were compacted by DEM simulation in one direction and two direction, respectively. The coordination number is defined as the average number of contacts between particles, and is always used to judge the density of mixtures. Fig. 7(a) shows the change of coordination number along the depth direction. The coordination number of particles presents

Table 4 Mesoscopic parameters of contact. Component

Density

Asphalt Mortar Aggregate

1.2E3 2.7E3

Contact Stiffness Normal

Tangential

2.1E8 3.2E9

2.1E8 3.2E9

Friction

Local Damping

0.3 0.3

0.7 0.7

G. Qian et al. / Construction and Building Materials 235 (2020) 117478

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Fig. 6. Laboratory and simulated results of compaction: (a) MIC method by single-side; (b) SC method.

large in the middle and small on both sides. It suggests that the compaction effect in the middle of the specimen is good, and the motion of particles at the upper and lower surfaces is greatly affected by the hammer. Comparing the single-sided compaction, the displacement of particles compacted by double-sided hammer displays a significant change as shown in Fig. 7 (b). The average

displacement of asphalt mortar is larger than that of coarse aggregates, but is less affected by the compaction mode. The movement of asphalt mortar is limited, since the location of coarse aggregates divides the particles of asphalt mortar into several relatively fixed areas. The coarse aggregates at the bottom move less during singlesided hammer, which resizes further compaction. By double-sided

Fig. 7. Simulated results of double-sided and single-sided compaction of MIC method in AC-20: (a) Coordination number; (b) Average particle displacement.

Fig. 8. Simulated results of particle distribution and air void content of MIC method in AC-20: (a) Initial state at 9 times hammer; (b) Final state at 150 times hammer.

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Fig. 9. Simulated results of particle distribution and air void content of SC method in AC-20: (a) Initial state at 0.1 MPa; (b) Final state at 70 MPa.

hammering, coarse aggregates are moved to accumulate more closely, thus the coordination number at the bottom increases. 5.3. Distribution of aggregates and asphalt mortar using large-scale model To avoid the boundary effect, the longitudinal length should be over 20 times the NMPS. A large-scale specimen was modeled to analyze the motion of the aggregates and asphalt mortar. The initial size of the large-scale specimen is set to 650 mm in length and 200 mm in height. The specimen was divided into 13 measuring section (50 mm) along the horizontal direction to analyze the particle distribution in initial and final state. Fig. 8 plots the simulated distribution results of particle area and AV compacted by MIC method, taking AC-20 mixture as an example. For the initial state, the particle area distribution of each size sieves (<19 mm) is relatively uniform along the horizontal direction, and the AV of specimen depends on the location of particles larger than 19 mm in diameter. After 150 times hammer, the position of large size particles (>16 mm) remains within the initial section, while the area of other particles changes to fill the big air void of sections around them. Fig. 9 displays the simulated results of SC method. It shows that two groups of samples have similar initial states between MIC method and SC method, since the algorithm of random generation and accumulation of particles is consistent in the DEM software. With the increase of pressure, the AV of SC specimen decreases, which is mainly attributed to the movements of asphalt mortar and coarse aggregates less than 9.5 mm. It is obvious that the AV of final state is significantly different in the middle of specimen, where a local stable structure was possibly formed between the large particles to resist the deformation due to compaction. However, as a whole, the final AV of largescale specimen is less affected by boundary. The compaction effect of the MIC method is higher than that of the SC method. Although a dense specimen can be achieved by compaction method, the size distribution of particles is still uneven in horizontal direction, since the position of large size particles (>16 mm) is difficult to be changed in the compaction process. 6. Conclusions Compaction process of asphalt mixture is investigated by the laboratory tests and DEM simulations in this study. A packing model of binary particle assembly was developed to determine the size limitation of fine particles. Variations of the AV during the compaction process are measured and analyzed through the

laboratory test and simulation of the MIC and SC methods. Distributions of critical aggregates and asphalt mortar are also displayed by a large-scale specimen. The major conclusions are summarized as follows: (1) The binary particles assembly is affected by boundary that its initial AV raises rapidly along with the increasing size of coarse particles. However, the variation of CAS presents linear growth approximately, less affected by the boundary. The PCS (0.22NMPS) is applicable to separate the coarse and fine particles in the 3D assembly. For the 2D assembly, the CAS is around 0.195, which is obviously less than the PCS. (2) The simulated results for the MIC method and the SC method are in agreement with the experimental results exceeding 90% in the R-square. It is verified that the DEM simulation is an effective way to evaluating the compacting effect of the compaction process. By double-sided hammering, coarse aggregates are moved to accumulate more closely, thus the coordination number at the bottom increases. (3) As a whole, the final AV of large-scale specimen is less affected by boundary. The compaction effect of the MIC method is higher than that of the SC method. Although a dense specimen can be achieved by compaction method, the size distribution of particles is still uneven in horizontal direction, since the position of large size particles (>16 mm) is difficult to be changed in the compaction process.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments The research was supported by National Key R&D Program of the Ministry of Science and Technology of China (2018YFB1600100) and National Natural Science Foundation of China (51778071, and 51808058). The project was also supported by Open Fund of National Engineering Laboratory of Highway Maintenance Technology (Changsha University of Science & Technology) (kfj180103), the Special Funds for the Construction of Innovative Provinces in Hunan, China (2019SK2171) and Hunan Provincial Innovation Foundation for Postgraduate (CX2018B521).

G. Qian et al. / Construction and Building Materials 235 (2020) 117478

References [1] L. Mo, X. Li, X. Fang, M. Huurman, S. Wu, Laboratory investigation of compaction characteristics and performance of warm mix asphalt containing chemical additives, Constr. Build. Mater. 37 (2012) 239–247. [2] Z.A. Khan, H.I.A.-A. Wahab, I. Asi, R. Ramadhan, Comparative study of asphalt concrete laboratory compaction methods to simulate field compaction, Constr. Build. Mater. 12 (6–7) (1998) 373–384. [3] A. Awed, E. Kassem, E. Masad, D. Little, Method for predicting the laboratory compaction behavior of asphalt mixtures, J. Mater. Civ. Eng. 27 (11) (2015) 04015016. [4] G. Airey, A. Collop, Mechanical and structural assessment of laboratory-and field-compacted asphalt mixtures, Int. J. Pavement Eng. 17 (1) (2016) 50–63. [5] E. Masad, A. Scarpas, A. Alipour, K.R. Rajagopal, C. Kasbergen, Finite element modelling of field compaction of hot mix asphalt. Part I: theory, Int. J. Pavement Eng. 17 (1) (2016) 13–23. [6] F. Gong, Y. Liu, X. Zhou, Z. You, Lab assessment and discrete element modeling of asphalt mixture during compaction with elongated and flat coarse aggregates, Constr. Build. Mater. 182 (2018) 573–579. [7] A.E. Hunter, L. McGreavy, G.D. Airey, Effect of compaction mode on the mechanical performance and variability of asphalt mixtures, J. Transp. Eng. 135 (11) (2009) 839–851. [8] P. Liu, H. Xu, D. Wang, C. Wang, C. Schulze, M. Oeser, Comparison of mechanical responses of asphalt mixtures manufactured by different compaction methods, Constr. Build. Mater. 162 (2018) 765–780. [9] P. Li, J. Su, P. Gao, X. Wu, J. Li, Analysis of aggregate particle migration properties during compaction process of asphalt mixture, Constr. Build. Mater. 197 (2019) 42–49. [10] K. Mollenhauer, M.P. Wistuba, Influence of asphalt compaction procedure on 3D deformation properties, Int. J. Pavement Eng. 17 (1) (2016) 5–12. [11] M.E. Kutay, E. Arambula, N. Gibson, J. Youtcheff, Three-dimensional image processing methods to identify and characterise aggregates in compacted asphalt mixtures, Int. J. Pavement Eng. 11 (6) (2010) 511–528. [12] C. Xing, H. Xu, Y. Tan, X. Liu, C. Zhou, T. Scarpas, Gradation measurement of asphalt mixture by X-ray CT images and digital image processing methods, Measurement 132 (2019) 377–386. [13] E. Masad, V. Jandhyala, N. Dasgupta, N. Somadevan, N. Shashidhar, Characterization of air void distribution in asphalt mixes using X-ray computed tomography, J. Mater. Civ. Eng. 14 (2) (2002) 122–129. [14] V. Dubois, C. De La Roche, O. Burban, Influence of the compaction process on the air void homogeneity of asphalt mixtures samples, Constr. Build. Mater. 24 (6) (2010) 885–897. [15] B. Hofko, R. Blab, A. Alisov, Influence of compaction direction on performance characteristics of roller-compacted HMA specimens, Int. J. Pavement Eng. 17 (1) (2016) 39–49. [16] L.N. Mohammad, K. Al-Shamsi, A look at the Bailey method and locking point concept in Superpave mixture design, Transportation Research Circular EC124: Practical Approaches to Hot-Mix Asphalt Mix Design and Production Quality Control Testing (2007) 12-32. [17] H.U. Bahia, T.P. Friemel, P.A. Peterson, J.S. Russell, B. Poehnelt, Optimization of constructibility and resistance to traffic: a new design approach for HMA using the superpave compactor, J. Assoc. Asphalt Paving Technol. 67 (1998) 189– 232. [18] W.R. Vavrik, W.J. Pine, G. Huber, S.H. Carpenter, R. Bailey, The bailey method of gradation evaluation: the influence of aggregate gradation and packing

[19]

[20]

[21]

[22]

[23]

[24] [25] [26] [27] [28]

[29] [30] [31]

[32]

[33]

[34]

[35] [36] [37] [38]

9

characteristics on voids in the mineral aggregate (with discussion), J. Assoc. Asphalt Paving Technol. 70 (2001) 132–175. C. Xing, H. Xu, Y. Tan, X. Liu, Q. Ye, Mesostructured property of aggregate disruption in asphalt mixture based on digital image processing method, Constr. Build. Mater. 200 (2019) 781–789. L. Babout, K. Grudzien´, J. Wia˛cek, M. Niedostatkiewicz, B. Karpin´ski, M. Szkodo, Selection of material for X-ray tomography analysis and DEM simulations: comparison between granular materials of biological and non-biological origins, Granular Matter 20 (3) (2018) 38. J. Li, J. Zhang, G. Qian, J. Zheng, Y. Zhang, Three-dimensional simulation of aggregate and asphalt mixture using parameterized shape and size gradation, J. Mater. Civ. Eng. 31 (3) (2019) 04019004. J. Zhang, J. Li, Y. Yao, J. Zheng, F. Gu, Geometric anisotropy modeling and shear behavior evaluation of graded crushed rocks, Constr. Build. Mater. 183 (2018) 346–355. M.J. Khattak, A. Khattab, H.R. Rizvi, S. Das, M.R. Bhuyan, Imaged-based discrete element modeling of hot mix asphalt mixtures, Mater. Struct. 48 (8) (2015) 2417–2430. J. Chen, B. Huang, X. Shu, Air-void distribution analysis of asphalt mixture using discrete element method, J. Mater. Civ. Eng. 25 (10) (2013) 1375–1385. W.B. Fuller, S.E. Thompson, The laws of proportioning concrete, J. Transp. Eng. 59 (1907) 67–143. C.A.G. Weymouth, Effect of particle interface in mortars and concrete, Rock Products 36 (2) (1933) 26–30. H.-G. Kessler, Spheres model for gap grading of dense concretes, Betonwerk und Fertigteil-Technik 11 (1994) 63–76. K. Sobolev, A. Amirjanov, The development of a simulation model of the dense packing of large particulate assemblies, Powder Technol. 141 (1–2) (2004) 155–160. I. Itasca Consulting Group, PFC2D/3D Version 3.1 Manual, Itasca Consulting Group Inc, Minneapolis, Minnesota, 2005. R.I.o.H.M.o. Transport, In technical specification for construction of highway asphalt pavements, Ministry of Communication, Beijing, China, 2004. R.I.o.H.M.o. Transport, In standard test methods of bitumen and bituminous mixtures for highway engineering, Ministry of Communication, Beijing, China, 2011. N.I.M. Yusoff, D. Mounier, G. Marc-Stéphane, M.R. Hainin, G.D. Airey, H. Di Benedetto, Modelling the rheological properties of bituminous binders using the 2S2P1D Model, Constr. Build. Mater. 38 (2013) 395–406. J. Van Rompu, H. Di Benedetto, M. Buannic, T. Gallet, C. Ruot, New fatigue test on bituminous binders: experimental results and modeling, Constr. Build. Mater. 37 (2012) 197–208. B. Majidi, S.M. Taghavi, M. Fafard, D.P. Ziegler, H. Alamdari, Discrete element method modeling of the rheological properties of coke/pitch mixtures, Materials 9 (5) (2016) 334. R.I.o.H.M.o. Transport, The methods of aggregate for highway engineering, Ministry of Communication, Beijing, China, 2005. J. Chen, D. Zhang, X. Huang, Application of Particle Flow Code in Road Engineering, China Communications Press Co., Ltd, Beijing, China, 2015. Y. Liu, Z. You, Y. Zhao, Three-dimensional discrete element modeling of asphalt concrete: size effects of elements, Constr. Build. Mater. 37 (2012) 775–782. L.B. Wang, Mechanics of Asphalt: Microstructure and Micromechanics, McGraw Hill, New York, 2011.