Advances in Civil Engineering

Advances in Civil Engineering / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 5549891 | https://doi.org/10.1155/2021/5549891

Yulong Zhao, Ying Gao, Ke Zhang, Yao Zhang, Mingce Yu, "Establishment of Rutting Model of Wheel-Tracking Test for Real-Time Prediction of Rut Depth of Asphalt Layers", Advances in Civil Engineering, vol. 2021, Article ID 5549891, 16 pages, 2021. https://doi.org/10.1155/2021/5549891

Establishment of Rutting Model of Wheel-Tracking Test for Real-Time Prediction of Rut Depth of Asphalt Layers

Academic Editor: Valeria Vignali
Received09 Mar 2021
Revised27 May 2021
Accepted08 Jun 2021
Published21 Jun 2021

Abstract

The construction process control of asphalt layers directly affects the road service life and quality. The objective of this study was to establish a rutting model of the wheel-tracking test used for the real-time prediction of the rut depth of asphalt layers in the construction process. The gradation of asphalt mixture, asphalt content, and molding temperature were considered in the development of the new rutting model of the wheel-tracking test. The effects of these three factors on the high-temperature performance of asphalt mixture were analyzed. The order of importance of the factors affecting the high-temperature performance of asphalt mixture is the gradation of asphalt mixture, asphalt-aggregate ratio, and molding temperature. Overall, the predicted values of the rut depth of the wheel-tracking test are very close to the measured values. Furthermore, the difference between the rut depths of asphalt layers of the test group and the control group is small. These comparison results indicate that the new rutting model of the wheel-tracking test has high accuracy and good applicability for the test road.

1. Introduction

Rutting is one of the main early distresses in asphalt pavements, which not only significantly reduces the service quality and service life of the road but also poses a serious threat to the driving of the vehicle [13]. Through investigation, it was found that bad construction quality of the asphalt layer is one of the main reasons for the early distresses in asphalt pavement [4, 5]. The construction indices are commonly used to control the construction quality of the asphalt layer [6, 7], such as asphalt-aggregate ratio, gradation of asphalt mixture, and rolling temperature. However, the development of rutting is not given in the construction process of the asphalt layer, which is not conducive to reducing the early distresses in asphalt pavements. Furthermore, when the construction factors affecting the asphalt pavement performance are in an unfavourable situation, although they meet the requirements of the construction specification, the asphalt pavement performance may not meet the expected requirements. Consequently, in the construction process of the asphalt layer, it is important to predict the rut depth of asphalt pavement in real time to control the construction quality of the asphalt layer.

A variety of rutting models have been established to estimate the development of the rut depth of asphalt pavement. The resilient strain of the asphalt layer, the number of load repetitions, and the temperature were used to predict the asphalt pavement rutting in the MEPDG design method [8]. The distinguishing feature of this model was the consideration of the mechanical response of the pavement structure and the asphalt mixture performance. The format of the NCHRP 1-40B rutting model [9] is the same as that of the MEPDG rutting model. The difference between these two models is that the material properties and volumetric properties of the asphalt mixture are adopted to adjust the permanent deformation constants for the NCHRP 1-40B rutting model. The NCHRP 9-25 rutting model is based on the material properties, voids in the mineral aggregate, and compaction parameters to estimate the rutting rate [10]. Le et al. [11] proposed a rutting model for South Korean Pavement Design Guide, and the model variables are the number of loadings, temperature, and initial air voids. The rutting model proposed by Kim et al. [12] is based on the number of loads, the loading time, temperature, and the ratio of shear stress to shear strength. In China, the JTG D50-2017 rutting model is used to verify the alternative pavement structure in the design phase of road projects [13]. The model variables mainly include the permanent deformation equivalent temperature of the asphalt layer, the rut depth of the wheel-tracking test, and the number of load repetitions. Wang et al. [14] established a rutting model for the Asphalt Pavement Analyzer rutting test. The variables mainly include temperature, loading time, loading level, and air void.

To predict the rut depth of asphalt pavement in real time during construction, an accurate rutting model needs to be selected. When determining the rutting model, two principles should be considered: (1) the rutting model should have high accuracy for road construction projects; (2) the model variables can be detected or predicted in the construction process of the asphalt layer. The accuracy of the rutting models is best compared by the long-term follow-up monitoring of the actual road rut, but there are currently no corresponding complete test data. Each rutting model is based on a certain foundation, which is inseparable from the materials, pavement design, and construction methods of the corresponding countries. Therefore, the applicability of the foreign rutting model in China needs further verification. In China, the pavement structure is designed according to JTG D50-2017 [13]. Its design requirements can be used for construction process control. Furthermore, the JTG D50-2017 rutting model [13] had been verified by the actual road projects of China. Therefore, the JTG D50-2017 rutting model [13] was selected for real-time prediction of the permanent deformation of asphalt layers.

The JTG D50-2017 rutting model [13] needs to input the permanent deformation of the wheel-tracking test. This model variable cannot be detected in real time during the actual construction of the asphalt layer. It is necessary to estimate the rut depth of the wheel-tracking test according to the real-time detection data of the asphalt layer. Therefore, the model for rut depth of the wheel-tracking test should be proposed as the submodel of the JTG D50-2017 rutting model [13].

According to the NCHRP 704 report [15] and the findings by Rahman [16], the main factors affecting the high-temperature performance of asphalt pavement are the gradation of asphalt mixture, the properties of the asphalt, the effective asphalt content, the air voids, and the thicknesses of the asphalt layers. In the asphalt mixture plant, the gradation of the asphalt mixture can be predicted in real time by the digital image processing technology [1720] or online detection [6, 7]. The asphalt-aggregate ratio can be detected in real time by the online detection of the asphalt mixture plant [6]. The ground-penetrating radar can be used to estimate the air voids and the thicknesses of the asphalt layers in real time [2124]. Based on the air voids and the related information of aggregates, the effective asphalt content can be calculated [6].

This paper aims to propose a rutting model of the wheel-tracking test for monitoring the construction quality of the asphalt layer in real time. The model variables can be determined in real time during the construction of the asphalt layer. The wheel-tracking test was conducted according to T 0719-2011 of JTG E20-2011 [25]. The rutting model of the wheel-tracking test was verified by road test results. The JTG D50-2017 rutting model [13] was used to predict the rutting development of the pavement structure. Furthermore, the effect of the deviation of the rutting model of the wheel-tracking test on the rut depth of pavement structure was analyzed.

2. Experimental Program

2.1. Raw Materials

Two types of asphalt mixtures, AC-13 and SUP-20, were used for the wheel-tracking test.

2.1.1. Asphalt Binder

The SBS-modified asphalt (Ι-C) was selected for AC-13. Its properties are shown in Table 1. The requirements for the material properties of asphalt were according to JTG F40-2004 [6]. The tests shown in Table 1 were conducted based on JTG E20-2011 [25]. Table 2 presents the properties of asphalt for SUP-20, which was SBS-modified asphalt with the performance grade of PG 76-22.


ItemTested resultRequirementTest method [25]

Penetration (25°C, 100 g, 5 s) (0.1 mm)6860 ∼ 80T 0604
Penetration index0.32≥ − 0.4T 0604
Ductility (5°C, 5 cm/min) (cm)44≥ 30T 0605
Softening point (°C)77.9≥ 55T 0606
Kinematical viscosity (135°C) (Pa s)1.73≤ 3T 0625
Flashpoint (°C)256≥ 230T 0611
Solubility in trichloroethylene (%)99.64≥ 99T 0607
Elastic recovery (%)85.8≥ 65T 0662
Density (25°C) (g/cm3)1.023NAT 0603
Storage stability (°C)1.3≤ 2.5T 0661
Thin-film oven test (163°C)Mass loss (%)−0.04919−1.0 ∼ 1.0T 0609
Residual penetration ratio (25°C) (%)73.5≥ 60T 0604
Residual ductility (5°C) (cm)26.37≥ 20T 0605


ItemTested resultRequirementTest method

Original asphalt135°CKinematical viscosity (Pa s)2.500≤3AASHTO T 316 [26]
DSR76°CG (kPa)1.52G·sinδ ≥1.0AASHTO T 315 [27]
δ69.2
G·sinδ (kPa)1.63
DSR82°CG (kPa)0.869
δ67.7
G·sinδ (kPa)0.939
RTFOTDSR76°CG (kPa)2.58G·sinδ ≥2.2AASHTO T 240 [28]
δ66.2
G·sinδ2.82AASHTO T 315 [27]
DSR82°CG (kPa)1.52
δ68.2
G·sinδ (kPa)1.64
PAVBBR−12°CS (MPa)145S ≤300AASHTO R 28 [29]
mm ≥0.300
−18°CS (MPa)268AASHTO T 313 [30]
m0.284
DSR31°CG (kPa)1010G·sinδ ≤5000
AASHTO R 28 [29]
δ64.4
G·sinδ (kPa)910.9AASHTO T 315 [27]

Note. RTFOT: Rolling Thin-Film Oven Test. PAV: Pressurized Aging Vessel. DSR: Dynamic Shear Rheometer. BBR: Bending Beam Rheometer. G denotes the complex shear modulus. δ denotes the phase angle.
2.1.2. Mineral Aggregate

The sizes of the aggregates used for AC-13 were 0∼2.36 mm, 2.36∼4.75 mm, 4.75∼9.5 mm, and 9.5∼13.2 mm. The properties of these aggregates are shown in Table 3. The tests shown in Table 3 are carried out in accordance with JTG E42-2005 [31]. Five different sizes of aggregates were used for SUP-20, which were 0∼2.36 mm, 2.36∼4.75 mm, 4.75∼9.5 mm, 9.5∼13.2 mm, and 13.2∼19 mm. Table 4 lists the properties of the aggregates for SUP-20. The mineral powder is limestone flour. According to JTG F40-2004 [6], the mineral powder for the asphalt mixture must be ground from hydrophobic stone such as limestone or strong basic rock in magmatic rock, and the soil impurities in the original stone should be removed.


ItemTest resultRequirementTest method

Coarse aggregateCrushing value (%)16.9≤26T0316
Loss by abrasion and impact of the sample (%)17.5≤28T0317
9.5∼13.2 mmPercentage of flat-elongated particles (%)6.9≤12T0312
4.75∼9.5 mm6.3≤18
Polished stone value46≥42T0321

Fine aggregateSand equivalent (%)68≥60T 0334
Solidness (%)16≥12T 0340


ItemTest resultRequirementTest method

Angularity of coarse aggregate100≥85/80ASTM D5821 [32]
Angularity of fine aggregate46.6≥45AASHTO T304 [33]
Percentage of flat-elongated particles (%)7.3≤15T 0312 JTG E20-2011 [25]
Sand equivalent (%)68≥45AASHTO T 176 [34]

2.2. Design of Mix Proportion

The designed gradations of AC-13 and SUP-20 are shown in Figure 1. For AC-13 and SUP-20, the optimum asphalt-aggregate ratios (OARs), which were 5.0% and 4.5%, were determined based on the Marshall design method and Superpave design method, respectively. The molding temperature of specimens was 160°C, which was related to the initial rolling temperature. The design results for the asphalt mixtures of AC-13 and SUP-20 are shown in Table 5. According to the location of the road construction project, the design results met the technical requirements of JTG F40-2004 [6] and AASHTO M 323 [35], respectively.


TypeOARs (%)Air voids (%)Voids filled with asphalt (%)Voids in mineral aggregate (%)Marshall stability (kN)Marshall flow (0.1 mm)

AC-135.03.872.814.011.1726.9
SUP-204.54.069.313.0

2.3. Variability Design of Construction Control Indices

According to the requirements of construction specifications of asphalt pavements [6, 7], the influencing factors of the construction quality of asphalt layers mainly include raw material quality, mineral gradation, asphalt content, construction temperature, rolling passes, degree of compaction, thickness of the asphalt layer, and evenness. By analyzing these factors, it can be found that, for the construction process of the asphalt layer, the main factors affecting the construction quality can be essentially summarized as mineral gradation, asphalt content, rolling temperature, rolling passes, and thickness of the compacted asphalt layer. With the widespread use of Global Positioning System-Real-Time Kinematic (GPS-RTK) technology, the number of rolling passes is well controlled [36]. RTK is a real-time dynamic relative positioning technology using GPS carrier phase observations; the working principle of RTK is that the base station transmits its observations and station coordinate information to the mobile station through the data link. Therefore, the main considered factors of construction quality were mineral gradation, asphalt content, and rolling temperature in this study, which can be determined in real time. In the asphalt mixture plant, the allowable fluctuation ranges of mineral gradation for the AC-13 and SUP-20 are shown in Figures 1(a) and 1(b), respectively [6, 37]. The asphalt content should be controlled at the range of design value ±0.3% [6]. In this research, the degree of fluctuation in the molding temperature was designed to be ±15°C.

The aggregate segregation and temperature differential can occur in the production, transportation, and paving processes of asphalt mixture. Previous studies have shown that aggregate segregation and temperature differential have adverse effects on the asphalt pavement performance [3842]. Therefore, the influence of segregation on the high-temperature performance of asphalt mixture was considered for establishing a more suitable rutting model of the wheel-tracking test. In the actual construction process, aggregate segregation is very complex. In this study, the degree of segregation aggregate is mainly characterized by the percent passing of 2.36 mm sieve, which is the key sieve. The contained aggregates in the asphalt mixture are divided into two parts by the 2.36 mm sieve. Part 1 was composed of particles passing through the 2.36 mm sieve. The particles retained on the 2.36 mm sieve composed part 2. The following method was used to simulate the aggregate segregation of asphalt mixture: increasing (decreasing) the mass of each size of particles of P1 part in equal proportion and decreasing (increasing) the mass of each size of particles of P2 part correspondingly. Four different levels of aggregate segregation for AC-13 were designed in this study, which are shown in Figure 2. For the temperature differential, the set molding temperatures were 145°C and 130°C. The air voids of asphalt mixtures with different degrees of segregation were determined by the means of the Superpave gyratory compactor, and the effect of nonsegregation area on the segregation area was considered in this study [43]. Further information related to the determination of air voids can be found in [43]. The test conditions of different groups of asphalt mixtures are shown in Table 6.


GroupGradationAsphalt content (%)Molding temperature (°C)Air voids (%)

1AC-13Upper limit of fluctuation4.51451
2AC-13Upper limit of fluctuation4.51602
3AC-13Upper limit of fluctuation4.51753
4AC-13Upper limit of fluctuation4.81454
5AC-13Upper limit of fluctuation4.81605
6AC-13Upper limit of fluctuation4.81756
7AC-13Upper limit of fluctuation5.01457
8AC-13Upper limit of fluctuation5.01608
9AC-13Upper limit of fluctuation5.01759
10AC-13Design gradation4.514510
11AC-13Design gradation4.516011
12AC-13Design gradation4.517512
13AC-13Design gradation4.814513
14AC-13Design gradation4.816014
15AC-13Design gradation4.817515
16AC-13Design gradation5.014516
17AC-13Design gradation5.016017
18AC-13Design gradation5.017518
19AC-13Lower limit of fluctuation4.514519
20AC-13Lower limit of fluctuation4.516020
21AC-13Lower limit of fluctuation4.517521
22AC-13Lower limit of fluctuation4.814522
23AC-13Lower limit of fluctuation4.816023
24AC-13Lower limit of fluctuation4.817524
25AC-13Lower limit of fluctuation5.014525
26AC-13Lower limit of fluctuation5.016026
27AC-13Lower limit of fluctuation5.017527
28AC-13Aggregate segregation 14.116028
29AC-13Aggregate segregation 23.516029
30AC-13Aggregate segregation 35.516030
31AC-13Aggregate segregation 46.216031
32AC-13Design gradation4.813032
33SUP-20Upper limit of fluctuation4.014533
34SUP-20Upper limit of fluctuation4.016034
35SUP-20Upper limit of fluctuation4.017535
36SUP-20Upper limit of fluctuation4.314536
37SUP-20Upper limit of fluctuation4.316037
38SUP-20Upper limit of fluctuation4.317538
39SUP-20Upper limit of fluctuation4.614539
40SUP-20Upper limit of fluctuation4.616040
41SUP-20Upper limit of fluctuation4.617541
42SUP-20Design gradation4.014542
43SUP-20Design gradation4.016043
44SUP-20Design gradation4.017544
45SUP-20Design gradation4.314545
46SUP-20Design gradation4.316046
47SUP-20Design gradation4.317547
48SUP-20Design gradation4.614548
49SUP-20Design gradation4.616049
50SUP-20Design gradation4.617550
51SUP-20Lower limit of fluctuation4.014551
52SUP-20Lower limit of fluctuation4.016052
53SUP-20Lower limit of fluctuation4.017553
54SUP-20Lower limit of fluctuation4.314554
55SUP-20Lower limit of fluctuation4.316055
56SUP-20Lower limit of fluctuation4.317556
57SUP-20Lower limit of fluctuation4.614557
58SUP-20Lower limit of fluctuation4.616058
59SUP-20Lower limit of fluctuation4.617559
60SUP-20Design gradation4.313060

2.4. Specimen Preparation and Testing Procedures

The specimen preparation was conducted according to T 0703-2011 of JTG E20-2011 [25]. The size of the slab specimen for the wheel-tracking test is 300 mm (long) × 300 mm (wide) × 50 mm (height). First, the mesh mold was put into an oven at a temperature of 100°C for one hour. Then, the mesh mold was taken out of the oven. A plain paper was laid in the mesh mold. Next, the asphalt mixture was mixed again by a spatula. The asphalt mixture was placed evenly in the mesh mold from side to center, and the middle part of the paved asphalt mixture was slightly higher than the surrounding area. The frame was removed, and a preheated compact hammer was used to tamp the paved asphalt mixture from the edge to the middle, as shown in Figure 3. When the asphalt mixture reached the molding temperature, a plain paper was placed on the surface of the asphalt mixture. Then, the mesh mold containing the asphalt mixture was put on the platform of the roller compactor. The total load used for compaction was adjusted to 9 kN. In general, the set air voids can be achieved by 24 passes according to the T 0703-2011 of JTG E20-2011 [25].

The wheel-tracking test was required by the JTG D50-2017 rutting model of the asphalt pavement, which was performed in accordance with T 0719-2011 of JTG E20-2011 [25], as shown in Figure 4. The test temperature was 60°C, and the wheel pressure was 0.7 MPa. The slab specimen together with the mesh mold was placed in an incubator with a temperature of 60 ± 1°C for 5 hours. Then, the slab specimen together with the mesh mold was placed on the test platform of the rutting test machine. The wheel reciprocates over the specimen with a travel distance of 230 ± 10 mm. The frequency of the wheel ranges from 42 ± 1 passes per minute across the specimen. The rutting test machine was started to make the test wheel travel back and forth. The rut depth was collected by the rut deformation automatic recorder.

2.5. JTG D50-2017 Rutting Model of Asphalt Pavement

According to JTG D50-2017 [13], the rut depth of asphalt layers can be predicted using the following equations:where Ra is the rut depth of the asphalt layers (mm); Rai is the rut depth of sublayer i (mm); n is the number of sublayers; Tpef is the equivalent temperature for the permanent deformation of the asphalt layer (°C); Ne3 is the number of equivalent load repetitions on the design lane; hi is the thickness of sublayer i (mm); h0 is the thickness of the slab specimen for the wheel-tracking test (mm); R0i is the permanent deformation of wheel-tracking test for the asphalt mixture of sublayer i when the number of wheel passes is 2520 (mm); kRi is the comprehensive correction coefficient; zi is the depth of sublayer i (mm); ha is the total asphalt layers thickness (mm); pi is the vertical compressive stress of the top surface of sublayer i (MPa).

3. Results and Discussion

3.1. Results of Wheel-Tracking Test

Table 7 presents the results of the wheel-tracking test.


GroupRD (mm)GroupRD (mm)GroupRD (mm)GroupRD (mm)

12.086163.822316.474461.635
21.961173.915322.996471.352
31.490184.451332.193482.965
41.585193.955342.155493.223
51.479203.823352.126503.368
61.657213.011362.068512.985
73.263224.141371.936522.726
83.966234.960381.919532.667
94.449245.319391.862543.418
102.398255.689401.711553.227
111.729265.920412.131562.982
121.315276.968421.758574.710
132.563281.467431.669584.409
141.601292.485441.522594.653
151.636306.044451.703601.796

Note. RD is the rut depth.
3.1.1. Effect of Gradation on Rut Depth of Wheel-Tracking Test

Based on Tables 6 and 7, the influence of gradation level on rut depth of wheel-tracking test is shown in Figure 5. For the design gradation of AC-13, the rut depth is 2.603 mm, which is the average value of the rut depths of groups 10∼18. From Figure 5, it can be observed that, for the set gradation levels, the rut depth increases as the gradation level changes from the upper limit of fluctuation through the design gradation to the lower limit of fluctuation. This is because the asphalt content is at a high level for the three gradation levels. When the gradation level changes from fine gradation to coarse gradation, the specific surface area of the mineral material decreases. Therefore, the free asphalt increases, which leads to the increase of the rut depth. Furthermore, when the gradation changes from the design level to the lower limit of the gradation fluctuation, the asphalt mixture becomes difficult to be compacted, causing a decrease in the high-temperature performance of the asphalt mixture.

3.1.2. Effect of Asphalt Content on Rut Depth of Wheel-Tracking Test

Figure 6 presents the relationship between the asphalt content and the rut depth of the wheel-tracking test according to Tables 6 and 7. For 4.8% of the asphalt content of AC-13, the rut depth is 2.771 mm, which is the average value of the rut depths of groups 4, 5, 6, 13, 14, 15, 22, 23, and 24. For the set asphalt content, the curve of the rut depth is relatively flat when the asphalt content ranges from 4.5% (4.0%) to 4.8% (4.3%). The rut depth of the wheel-tracking test increases as the asphalt content increases from 4.8% (4.3%) to 5.0% (4.6%). As the asphalt content increases, the free asphalt in the asphalt mixture increases, which leads to a decrease in the high-temperature stability of the asphalt mixture.

3.1.3. Effect of Molding Temperature on Rut Depth of Wheel-Tracking Test

Figure 7 shows the effect of molding temperature on the rut depth of the wheel-tracking test on the basis of Tables 6 and 7. For 160°C of the molding temperature of AC-13, the rut depth of the wheel-tracking test is 3.262 mm, which is the average value of the rut depths of groups 2, 5, 8, 11, 14, 17, 20, 23, and 26. When the molding temperature raises from 145°C to 160°C, the rut depth of the wheel-tracking test decreases. This is mainly because the asphalt consistency is reduced, the asphalt film thickness increases, and the mixture becomes easy to be compacted. However, the rut depth of the wheel-tracking test increases as the molding temperature ranges from 160°C to 175°C. This can be attributed to the decrease of air voids, the decrease of the asphalt film thickness, and the increase of free asphalt.

On the other hand, the importance of each factor can be evaluated according to Figures 57. For the given factor levels, the order of factors is gradation, asphalt content, and molding temperature in accordance with the influence degree of factors from strong to weak.

3.2. Rutting Model of Wheel-Tracking Test

According to NCHRP 704 report [15] and Rahman et al. [44], the main factors affecting the permanent deformation of the asphalt layer are cumulative percent retained on the 3/4 inch, 3/8 inch, no. 4 standard sieves, percent passing of no. 200 standard sieves, the properties of asphalt, effective asphalt content, air voids, effective binder content, and the thickness of the asphalt layer. For the properties of asphalt, Rahman et al. [44] mainly used two viscosity-temperature susceptibility parameters A and VTS to predict the rutting depth of the Hamburg Wheel-Tracking Device test. In this study, the established rutting model of the wheel-tracking test will be used to evaluate the high-temperature performance of the asphalt layer in real time. Unfortunately, the properties of asphalt and the thickness of the asphalt layer are not easy to be detected in real time. The air voids of the asphalt layer can be estimated according to the findings by Zhao [43] or measured by the intelligent roller [45]. Then, the effective binder content can be calculated based on JTG F40-2004 [6]. The gradation of asphalt mixture can be in real time obtained from the asphalt mixture plant. This paper mainly considers the effects of gradation, effective asphalt content, and air voids on the permanent deformation of the asphalt mixture. The data used to establish the rutting model of the wheel-tracking test are shown in Table 8.


GroupP38 (%)P4 (%)P200 (%)Vbeff (%)Va (%)RD (mm)

114.242.789.455.02.086
214.242.789.494.61.961
314.242.789.514.41.490
414.242.7810.154.31.585
514.242.7810.203.81.479
614.242.7810.213.71.657
714.242.7810.833.73.263
814.242.7810.903.13.966
914.242.7810.922.94.449
1020.248.769.434.82.398
1120.248.769.474.41.729
1220.248.769.494.21.315
1320.248.7610.144.02.563
1420.248.7610.173.71.601
1520.248.7610.193.51.636
1620.248.7610.873.03.822
1720.248.7610.902.73.915
1820.248.7610.922.64.451
1926.254.749.176.93.955
2026.254.749.216.43.823
2126.254.749.256.03.011
2226.254.749.905.84.141
2326.254.749.935.54.960
2426.254.749.965.25.319
2526.254.7410.595.05.689
2626.254.7410.664.45.920
2726.254.7410.684.26.968
2822.752.45.28.198.71.467
2925.256.24.36.5110.62.485
3017.744.96.811.813.16.044
3115.241.27.713.412.56.474
3220.248.7610.005.32.996
3333.256.26.68.315.22.193
3433.256.26.68.325.02.155
3533.256.26.68.334.92.126
3633.256.26.68.964.72.068
3733.256.26.69.004.31.936
3833.256.26.69.014.21.919
3933.256.26.69.624.11.862
4033.256.26.69.673.71.711
4133.256.26.69.693.52.131
4239.262.24.68.465.01.758
4339.262.24.68.474.91.669
4439.262.24.68.504.61.522
4539.262.24.69.144.31.703
4639.262.24.69.164.11.635
4739.262.24.69.173.91.352
4839.262.24.69.833.42.965
4939.262.24.69.873.13.223
5039.262.24.69.883.03.368
5145.268.22.68.346.42.985
5245.268.22.68.395.92.726
5345.268.22.68.415.62.667
5445.268.22.69.094.93.418
5545.268.22.69.114.73.227
5645.268.22.69.144.42.982
5745.268.22.69.734.54.710
5845.268.22.69.774.14.409
5945.268.22.69.784.04.653
6039.262.24.69.045.31.796

Note. P38 is the cumulative percent retained on a 3/8-inch sieve. P4 is cumulative percent retained on no. 4 sieve. P200 is the percent passing no. 200 sieve. Vbeff is effective asphalt content. Va is the air voids of asphalt mixture.

According to the above analysis of the factors affecting the asphalt pavement rutting, a new rutting model of the wheel-tracking test was established based on the data shown in Table 8. Data fitting was carried out in a nonlinear way. The new rutting model was mainly in the form of polynomials.

Based on Table 8, the estimated values of the rut depth of wheel-tracking test are shown in Figure 8, compared with the measured values. Different from the previous rut prediction model of the wheel-tracking test [44], this model can predict the rut depth of the compacted asphalt mixture in real time.

3.3. Verification of Rutting Model of Wheel-Tracking Test

After the rutting model of the wheel-tracking test is established, it needs to be verified based on road construction projects. Figure 9 and Table 9 present the gradations and asphalt contents of the asphalt mixture sampled from a highway construction project in Jiangsu province of China by the extraction tests (ETs), respectively. The asphalt mixture was prepared according to the results of the ETs. The molding temperature is shown in Table 10, which is measured by the infrared temperature sensors. Based on the air voids of the asphalt mixture determined by the Superpave gyratory compactor, the slab specimens were molded, and the rut depths of these specimens were obtained using the wheel-tracking test. For the extraction test groups, the predicted and measured values of the rut depth of the wheel-tracking test are shown in Figure 10.


Number of the ETs12345678910

Asphalt content (%)4.294.334.324.324.554.344.394.324.304.31


Number of the ETs12345678910

Molding temperature (°C)154161150153155151155152161160

The applicability of the rutting model of the wheel-tracking test was evaluated by using the deviation between the estimated and the measured values of each extraction test group. According to Figure 10, the average deviation between the predicted and the measured values of rut depths of the extraction groups is 6.62%. The maximum deviation of these extraction test groups is 19.19%. Except for the 8th extraction group, the absolute values of the deviations of the other extraction groups were less than 14%. Overall, the deviation between the predicted and measured values of the rut depths of these extraction test groups is relatively small, indicating that the rutting model of the wheel-tracking test established in this study has good applicability for the test road.

3.4. Effect of Deviation of Rutting Model of Wheel-Tracking Test on Prediction Accuracy of JTG D50-2017 Rutting Model of Asphalt Layers

The influence of the difference between the estimated and measured values of rut depths of wheel-tracking test on the rut depths of asphalt layers predicted by the JTG D50-2017 rutting model [13] was investigated in this study.

3.4.1. Asphalt Pavement Structure and Traffic Parameters

The pavement structure was mainly determined based on the design data of the highway construction project and JTG D50-2017 [13], which is shown in Figure 11. The traffic volume of large passenger cars and trucks on the highway section was set at 4,750 vehicles per day, with an annual growth rate of 5%. The design life of this highway was 15 years. According to the recommended value of JTG D50-2017 [13], the directional distribution factor was set to 0.55, and the lane distribution factor was set to 0.78. The vehicle class distribution factors were determined according to truck traffic classification (TTC) 1 of JTG D50-2017 [13], as shown in Table 11.


TTC groupVehicle class distribution (%)
234567891011

16.415.31.40.011.93.116.320.425.20.0

According to JTG D50-2017 [13], the proportion of not fully loaded and fully loaded vehicles of each type is shown in Table 12; the conversion factor of equivalent single axle loads is shown in Table 13. The cumulative equivalent single axle loads can be calculated by [13]where N1 is the average daily equivalent single axle loads of the design lane; AADTT is the annual average daily truck and large-size passenger bus traffic (vehicle/d); DDF is the directional distribution factor; LDF is the lane distribution factor; m is the vehicle class number; VCDFm is the distribution factor of vehicle class m; EALFm is the conversion factor of equivalent single axle loads of vehicle class m; Ne is the cumulative equivalent single axle loads of the design lane in the design service life; t is the design service life of pavement (year); γ is the annual average growth rate of traffic in the design service life.


Vehicle classNot fully loaded vehicleFully loaded vehicles

20.850.15
30.900.10
40.650.35
50.750.25
60.550.45
70.700.30
80.450.55
90.600.40
100.550.45
110.650.35


Vehicle classTensile strain at the bottom of asphalt layer and permanent deformation of asphalt layerTensile stress at the bottom of inorganic binder stabilized layer
Not fully loaded vehicleFully loaded vehicleNot fully loaded vehicleFully loaded vehicle

20.82.80.535.5
30.44.11.3314.2
40.74.20.3137.6
50.66.30.672.9
61.37.910.21505.7
71.46.07.8553.0
81.46.716.4713.5
91.55.10.7204.3
102.47.037.8426.8
111.512.12.5985.4

The influence of the temperature condition at the location of the road project was considered by the JTG D50-2017 rutting model of the asphalt layers [13]. For the city of Nanjing and pavement structure, the equivalent temperature for the permanent deformation of the asphalt layer was 25.0°C, that is, Tpef = 25.0°C. According to JTG D50-2017 [13], the asphalt mixture layers shown in Figure 11 were layered, and the thicknesses of these sublayers were 10 mm, 15 mm, 15 mm, 20 mm, 20 mm, 20 mm, and 80 mm. The vertical compressive stress at the top of each sublayer was calculated using the elastic layer system theory. The load parameters and calculation point positions [13] are shown in Figure 12. By calculation, the rutting of the asphalt layer is 14.9 mm for the design service life, which meets the design requirement of JTG D50-2017 (≤15.0 mm) [13]. The development of rutting with the service life is shown in Figure 13.

3.4.2. Rutting Prediction Results of Asphalt Layers

The cylinder specimens used for the dynamic modulus test were molded according to the air voids of the asphalt mixtures for each extraction test group. The dynamic modulus test was conducted according to T 0738-2011 of JTG E20-2011 [25], and the test conditions were 20°C and 10 Hz. For each extraction test group, the results of the dynamic modulus test are shown in Figure 14. According to the JTG D50-2017 rutting model of the asphalt layers [13], the rutting prediction results of asphalt layers are shown in Figure 15. In this paper, the predicted rut depths of asphalt layers using the measured rut depths of wheel-tracking test are classified as the control group. The rest, which are based on the predicted rut depths of wheel-tracking test, are classified as the test group.

From Figure 15, the average deviation of the rut depths of the asphalt layers between the control group and the test group is 3.34%. For the extraction test groups 1∼10, the largest deviation is 9.52%. Furthermore, the absolute values of the deviations for these extraction test groups are all less than 10%. The results indicated that the influence of the deviation between the predicted and measured values of rut depths of wheel-tracking test on the rutting prediction results of JTG D50-2017 is small.

4. Conclusions

In this study, a new rutting model of the wheel-tracking test was proposed as the submodel of the JTG D50-2017 rutting model of the asphalt layers, which can be used to monitor the construction quality of the asphalt layer in real time. The gradation, asphalt-aggregate ratio, and molding temperature were considered as the factors affecting the permanent deformation of the wheel-tracking test. The new rutting model of the wheel-tracking test was verified based on a road construction project. Furthermore, the effect of using the estimated value of rut depth of wheel-tracking test instead of the measured values was analyzed in this study. Based on the results and analysis, the following conclusions can be drawn:(1)For the given factor levels, the permanent deformation of the asphalt mixture increases as the gradation level changes from the upper limit of fluctuation through the design gradation to the lower limit of fluctuation. In the asphalt mixture plant, the coarser gradation of asphalt mixture has an adverse influence on the high-temperature of the asphalt mixture if the asphalt content is at a high level.(2)For the given factor levels, increasing the asphalt content overall leads to the increase of the permanent deformation of the asphalt mixture. There is a suitable molding temperature to obtain better high-temperature performance of asphalt mixture, which is near the designed molding temperature.(3)For the given factor levels, the influence degree of the three factors on the permanent deformation of the asphalt mixture is in such an order: gradation, asphalt content, and molding temperature.(4)Based on the results of the extraction test, the deviation between the predicted and measured values of rut depth of wheel-tracking test is small, which indicates that the new rutting model of the wheel-tracking test has good applicability for SUP20.(5)The deviation between the rut depths of asphalt layers of the test group and the control group is small, which also indicates that the new rutting model of the wheel-tracking test has good prediction accuracy for SUP20.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by the Shandong Provincial Natural Science Foundation (ZR2020QE274), Key Research and Development Program of Shandong Province (Soft Science Project) (2020RKB01602), Science and Technology Plan of Shandong Transportation Department (2019B63 and 2020B93), National Natural Science Foundation of China (51878168), Open Research Fund of National Engineering Laboratory for Advanced Road Materials (NLARM-ORF-2018-01), Provincial Natural Science Foundation of Anhui (1908085QE217), and Key Project of Natural Science Research of Anhui Provincial Department of Education (KJ2018A0668 and KJ2020A1214).

References

  1. X. Zhao, A. Shen, and B. Ma, “Temperature adaptability of asphalt pavement to high temperatures and significant temperature differences,” Advances in Materials Science and Engineering, vol. 2018, Article ID 9436321, 16 pages, 2018. View at: Publisher Site | Google Scholar
  2. X. Dai, Y. Jia, S. Wang, and Y. Gao, “Evaluation of the rutting performance of the field specimen using the Hamburg wheel-tracking test and dynamic modulus test,” Advances in Civil Engineering, vol. 2020, Article ID 9525179, 15 pages, 2020. View at: Publisher Site | Google Scholar
  3. T. Chopra, M. Parida, N. Kwatra, and P. Chopra, “Development of pavement distress deterioration prediction models for urban road network using genetic programming,” Advances in Civil Engineering, vol. 2018, Article ID 1253108, 15 pages, 2018. View at: Publisher Site | Google Scholar
  4. A. E. Abu El-Maaty, A. Y. Akal, and S. El-Hamrawy, “Management of highway projects in Egypt through identifying factors influencing quality performance,” Journal of Construction Engineering, vol. 2016, Article ID 4823630, 8 pages, 2016. View at: Publisher Site | Google Scholar
  5. J. Shen, F. Li, and J. Chen, Analysis and Preventiue Techniques of Premature Damage of Asphalt Pavement in Expressway, China Communications Press, Beijing, China, 2004.
  6. J.TGF40-2004, Technical Specifications for Construction of Highway Asphalt Pavements, China Communications Press, Beijing, China, 2004.
  7. North Carolina Department of Transportation, Hot Mix Asphalt Quality Management System, North Carolina Department of Transportation, Raleigh, NC, USA, 2012.
  8. A. Inc, Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, National Cooperative Highway Research Program, Washington, DC, USA, 2004.
  9. F. Zhou, E. Fernando, and T. Scullion, A Review of Performance Models and Test Procedures with Recommendations for Use in the Texas Me Design Program, Texas Transportation Institute, Bryan, TX, USA, 2008.
  10. D. W. Christensen and R. F. Bonaquist, Volumetric Requirements for Superpave Mix Design, Transportation Research Board, Washington, DC, USA, 2006.
  11. A. T. Le, H. J. Lee, H. M. Park, and S. Y. Lee, “Development of a permanent deformation model of asphalt mixtures for South Korean pavement design guide,” Transportation Research Record, vol. 2095, no. 1, pp. 45–52, 2009. View at: Publisher Site | Google Scholar
  12. W. J. Kim, V. P. Le, H. J. Lee, and H. T. Phan, “Calibration and validation of a rutting model based on shear stress to strength ratio for asphalt pavements,” Construction and Building Materials, vol. 149, pp. 327–337, 2017. View at: Publisher Site | Google Scholar
  13. JTG D50-2017, Specifications for Design of Highway Asphalt Pavement, China Communications Press, Beijing, China, 2017.
  14. H. Wang, H. Tan, T. Qu et al., “Effects of test conditions on APA rutting and prediction modeling for asphalt mixtures,” Advances in Materials Science and Engineering, vol. 2017, Article ID 2062758, 11 pages, 2017. View at: Publisher Site | Google Scholar
  15. I. N. Fugro Consultants and S. Arizona, A Performance-Related Specification for Hot-Mixed Asphalt, Transportation Research Board, Washington, DC, USA, 2011.
  16. A. A. Rahman, M. M. Mendez Larrain, and R. A. Tarefder, “Development of a nonlinear rutting model for asphalt concrete based on weibull parameters,” International Journal of Pavement Engineering, vol. 20, no. 6, pp. 1–10, 2017. View at: Google Scholar
  17. T. Wang, Research of Intelligent Technology of Quality Process Control of Asphalt Pavement, Hebei University of Technology, Tianjin, China, 2014.
  18. P. Zou, The Design and Implementation of Real-Time Detection System for Aggregate Gradation Based on Multi-Source Vision, Chang’an University, Xi’an, China, 2015.
  19. I. S. Bessa, V. T. F. Castelo Branco, and J. B. Soares, “Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations,” Construction and Building Materials, vol. 37, pp. 370–378, 2012. View at: Publisher Site | Google Scholar
  20. M. Moaveni, Advanced Image Analysis and Techniques for Degradation Characterization of Aggregates, University of Illinois at Urbana-Champaign, Champaign, IL, USA, 2015.
  21. P. Shangguan, I. L. Al-Qadi, Z. Leng, R. L. Schmitt, and A. Faheem, “Innovative approach for asphalt pavement compaction monitoring with ground-penetrating radar,” Transportation Research Record, vol. 2347, no. 1, pp. 79–87, 2013. View at: Publisher Site | Google Scholar
  22. E. Kassem, A. Chowdhury, T. Scullion, and E. Masad, “Application of ground-penetrating radar in measuring the density of asphalt pavements and its relationship to mechanical properties,” International Journal of Pavement Engineering, vol. 17, no. 6, pp. 503–516, 2016. View at: Publisher Site | Google Scholar
  23. I. L. Al-Qadi, S. Lahouar, and A. Loulizi, “Successful application of ground-penetrating radar for quality assurance-quality control of new pavements,” Transportation Research Record, vol. 1861, no. 1, pp. 86–97, 2003. View at: Publisher Site | Google Scholar
  24. S. Zhao, P. Shangguan, and I. L. Al-Qadi, “Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data,” NDT & E International, vol. 73, pp. 1–7, 2015. View at: Publisher Site | Google Scholar
  25. JTGE20-2011, Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering, China Communications Press, Beijing, China, 2011.
  26. T. Aashto, Standard Method of Test for Viscosity Determination of Asphalt Binder using Rotational Viscometer, American Association of State Highway and Transportation Officials, Washington, DC, USA, 2017.
  27. T. Aashto, Standard Method of Test for Determining the Rheological Properties of Asphalt Binder using a Dynamic Shear Rheometer (DSR), American Association of State Highway and Transportation Officials, Washington, DC, USA, 2016.
  28. T. Aashto, Standard Method of Test for Effect of Heat and Air on a Moving Film of Asphalt Binder (Rolling Thin-Film Oven Test), American Association of State Highway and Transportation Officials, Washington, DC, USA, 2017.
  29. R. Aashto, Standard Practice for Accelerated Aging of Asphalt Binder Using a Pressurized Aging Vessel (PAV), American Association of State Highway and Transportation Officials, Washington, DC, USA., 2016.
  30. T. Aashto, Standard Method of Test for Determining the Flexural Creep Stiffness of Asphalt Binder Using the Bending Beam Rheometer (BBR), American Association of State Highway and Transportation Officials, Washington, DC, USA, 2016.
  31. JTGE42-2005, Test Methods of Aggregate for Highway Engineering, China Communications Press, Beijing, China, 2005.
  32. ASTM D5821, Standard Test Method for Determining the Percentage of Fractured Particles in Coarse Aggregate, ASTM International, West Conshohocken, PA, USA, 2017.
  33. T. Aashto, Standard Method of Test for Uncompacted Void Content of Fine Aggregate, American Association of State Highway and Transportation Officials, Washington, DC, USA, 2017.
  34. T. Aashto, Standard Method of Test For Plastic Fines in Graded Aggregates and Soils by Use of the Sand Equivalent Test, American Association of State Highway and Transportation Officials, Washington, DC, USA, 2017.
  35. M. Aashto, Standard Specification for Superpave Volumetric Mix Design, American Association of State Highway and Transportation Officials, Washington, DC, USA, 2017.
  36. L. Zhang, Research on Information-Based Control of Asphalt Pavement Compaction, Southeast University, Nanjing, China, 2014.
  37. DB32/T1246, Standard Specification for Construction of Jiangsu Province Expressway Asphalt Pavements, Nanjing, China, 2008.
  38. K. A. Willoughby, J. S. Uhlmeyer, J. P. Mahoney et al., “Construction-related variability in pavement mat density due to temperature differentials,” Transportation Research Record, vol. 1849, no. 1, pp. 166–173, 2003. View at: Publisher Site | Google Scholar
  39. A. Shen, Y. Guo, F. Che et al., “Influence of asphalt mixture segregation on long-term high temperature performance of asphalt pavement based on MMLS3 test,” China Journal Of Highway And Transport, vol. 25, no. 3, pp. 80–86, 2012. View at: Google Scholar
  40. M. Stroup-Gardiner and E. R. Brown, Segregation in Hot-Mix Asphalt Pavements, Transportation Research Board, Washington, DC, USA, 2000.
  41. W. Wu, Z. Tu, Z. Zhu et al., “Effect of gradation segregation on mechanical properties of an asphalt mixture,” Applied Sciences, vol. 9, no. 2, pp. 1–15, 2019. View at: Publisher Site | Google Scholar
  42. L. Garcia-Gil, R. Miró, and F. E. Pérez-Jiménez, “Evaluating the role of aggregate gradation on cracking performance of asphalt concrete for thin overlays,” Applied Sciences, vol. 9, no. 4, pp. 1–17, 2019. View at: Publisher Site | Google Scholar
  43. Y. Zhao, Research on Construction Process Control of Asphalt Layer Based on BIM Technology, Southeast University, Nanjing, China, 2018.
  44. A. A. Rahman, M. M. Mendez Larrain, and R. A. Tarefder, “Development of a nonlinear rutting model for asphalt concrete based on weibull parameters,” International Journal of Pavement Engineering, vol. 20, no. 9, pp. 1055–1064, 2019. View at: Publisher Site | Google Scholar
  45. Q. Xu, G. K. Chang, and V. Gallivan, “A sensing-information-statistics integrated model to predict asphalt material density with intelligent compaction system,” IEEE, vol. 20, no. 6, pp. 3204–3211, 2015. View at: Publisher Site | Google Scholar

Copyright © 2021 Yulong Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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