# Study on the Compaction Performance and Its Key Influencing Factors of Asphalt Mixture Based on Intelligent Sensing Techniques

Authors: Rong Ma; Zhijun Wang
Journal: Journal of Sensors
 (2023)

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## Abstract

Real-time and accurate compaction quality monitoring is one of the key steps in the pavement construction. In this study, the effect of roller passes and temperature on the compactness of asphalt mixtures and its uniformity is discussed using the semivariance function. Further, the indoor asphalt mixture gyratory compaction test was carried out based on the embedded SmartRock sensor, with the aim of monitoring the changes in rotation and contact forces between aggregates. In addition, the effect of temperature on the dynamic response of SmartRock is discussed, and the compaction mechanism of asphalt mixtures is investigated from a microscopic scale. It is found that compaction degree and uniformity are not always positively correlated with the number of roller passes and that excessive compaction leads to a decrease in compaction effect; meanwhile, the variation in the compactness of asphalt mixtures can be characterized by the dynamic response of SmartRock, which providing a feasible method for real-time monitoring of the compaction process and optimization of the construction plan.


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## Body

## 1. Introduction

Compaction is one of the critical steps in road construction [1], which directly affects the service quality and durability of the road. Accurate, rapid, and comprehensive compaction quality detection technology is essential to ensure the compaction quality [1–3]. However, the traditional compaction quality control and acceptance (QC/QA) testing must be carried out in the field for sampling tests, resulting in a greater randomness and hysteresis of test results, making it difficult to realize the scientific detection of compaction quality and unable to achieve real-time and comprehensive detection of compaction quality.In order to overcome the shortcomings of traditional compaction QC/QA methods, a comprehensive and real-time compaction quality control technology—intelligent compaction (IC)—was developed. IC technology refers to the use of precision sensing modules, GPS positioning system, on-board reporting system, and other devices in the process of compaction control [3, 4], and the intelligent compaction values (ICMVs), position coordinates, and other information can be transmitted to construction management department in real time. The results of the Federal Highway Administration’s (FHWA) research of IC cases showed that there is a good correlation between ICMVs and pavement structural modulus calculated by the falling weight deflectometer (FWD) [3]. However, changes in temperature of asphalt mixtures can significantly affect the displacement and rotation of the aggregates under compaction [4] and also affect the correlation between ICMV and in situ test results [5, 6]. Although there are some studies on the influence of temperature on the compaction process [7–9], it is not easy to reveal the effect of temperature on the compaction process from the mesoscale mechanism. This is due to the complex interaction between the aggregates inside the asphalt mixture [10, 11]; few methods are available to directly perceive the true dynamic response between aggregates during compaction. Therefore, it is necessary to study the influence of temperature on the compaction process through engineering field as well as indoor tests.The purpose of this study is to investigate the influence of temperature change on the compaction process through engineering field and laboratory tests. The effect of temperature variation on the compactness and its uniformity was studied using the semivariogram of geostatistical model. The relationships between the roller passes, temperature, and CMV were quantitative discussed, and the compactness distribution of the test section is presented comprehensively. Further, the effects of temperature variation on stress, rotation, and acceleration of the aggregates were investigated on a mesoscale using SmartRock.


## 2. In Situ and Laboratory Compaction Tests

### 2.1. IC Project Information

The test section of this study is in an expressway construction project in Shandong Province, China, which has a total length of 200 m and the width is 11.25 m, as shown in Figures1(a) and 1(b). The BOMAG double steel wheel vibratory rollers were used in the project, and the main mechanical parameters are shown in Table 1. The intelligent roller is equipped with acceleration sensor, infrared temperature sensor, positioning system, and compactness display device, as shown in Figure 1(c). The compaction degree and temperature change can be recorded and displayed in real time, so that project managers can adjust the compaction schedules in time based on the information.Figure 1
(a) The project location. (b) The pavement project. (c) The IC roller.
(a)(b)(c)Table 1
Main roller mechanical parameters.
ParameterValueTotal static mass (kg)13200Diameter (mm)1236Width (mm)1235Vibration amplitude (mm)0.69/0.29Vibration frequency (Hz)48/60Vibration force (Kn)18.6/12.1


### 2.2. ICMV and Geostatistical Model

In 1974, Heinz Thumer of Sweden proposed ICMV for the first time and tested it on Dynapac smooth wheel vibratory roller and granular soil [12–14]. In 1976, Swedish geodynamic company and Dynapac jointly developed the compaction measurement index based on CMV method [15, 16]. The index obtains the response signal of the roller under vibration excitation through the acceleration sensor and puts forward a method to judge the compaction quality by the ratio of second harmonic wave to shock wave, where C is a constant, generally taken as 300. The specific calculation method is as follows:
(1)CMV=C×A2ΩAΩ.CMV is a representative QC/QA index of compaction; it can evaluate the compaction degree based on changes in the acceleration signal of the vibrating wheel. Its working principle is shown in Figure2.Figure 2
(a) Schematic diagram of compaction process. (b) The variation of harmonics during compaction.
(a)(b)The semivariogram model based on geostatistical not only is able to describe the variation of continuous compaction parameters [17] but also describe its random variability. It is defined as half of the average variance between data values with a certain distance. The calculation method is shown in
(2)rh=12nh∑i=1nhzxi+h−zxi2,where nh is the logarithm of continuous compaction monitoring points on a compaction surface within h distance. nh, zxi, and zxi+h are the actual observed values of continuous compaction detection parameters at spatial positions xi and xi+h, respectively.The semivariogram curve is a function diagram of the semivariogram valuerh versus distance h [18]. It has three characteristic parameters: range, sill, and nugget, as shown in Figure 3. The semivariogram is generally expressed by the variation curve. The abscissa is the step size, and the ordinate is the value of the semivariogram. It can be seen from Figure 3 that the closer the distance, the smaller the semivariogram value and the greater the correlation. When the distance h approaches 0, which limr0=C0, it is called nugget effect, and C0 is called nugget constant. It reflects the degree of random variation of continuous compaction parameters. The farther the distance is, the greater the value of the semivariogram, indicating that the correlation between the attribute values of the two points is smaller. When h tends to infinity, where limr∞=C0+C, C is called the partial sill value. It reflects the nonuniformity of the compacted material.Figure 3
Typical sample semivariogram.This study discussed the homogeneity of asphalt mixtures at different compaction passes and temperatures based on semivariance functions. The calculated scatter cannot directly characterize the spatial variability of the continuous compaction degree. Therefore, in order to determine the value of rangea, the nugget value C0, and the partial sill value C, a semivariance function needs to be fitted, as shown in
(3)rh=0h=0,C0+C1−exp−hah>0.


### 2.3. Indoor Test Materials and Methods

#### 2.3.1. Characteristic of SmartRock Sensor

Based on the field compaction tests, the SmartRock-based indoor gyratory compaction test was conducted to discuss the effect of temperature on the compaction process of asphalt mixtures. SmartRock is a sensor developed based on microelectromechanical system (MEMS) technology, which integrates several data acquisition modules such as stress, temperature, rotation, and acceleration, as well as Bluetooth wireless transmission module, enabling real-time acquisition and wireless transmission of multiple data [19–22], as shown in Figure 4.Figure 4
(a) SmartRock sensor. (b) Wireless antenna.
(a)(b)SmartRock sensor has two coordinate systems: global coordinates (X-Y-Z) and local coordinates (X′-Y′-Z′), as shown in Figure 5, and the local coordinate changes with the movement of SmartRock. Conversion relationship between local and global coordinates can be expressed by
(4)xyz=Rx,y,z,,where (x, y, z) and (x′, y′, z′) are the coordinate points in the global and local coordinate systems, respectively, and R is the transformation matrix between the global and local coordinate systems, which is specifically expressed as
(5)R=cosx,,xcosx,,ycosx,,zcosy,,xcosy,,ycosy,,zcosz,,xcosz,,ycosz,,z.Figure 5
Global-local coordinate system transformation.


#### 2.3.2. Laboratory Test Material

AC-20 asphalt mixture was selected for indoor rotary compaction tests; the specific gradation is shown in Table2. The mixture was mixed at the optimum oil-to-rock ratio to better monitor the effect of temperature changes on the compaction process of the mixture. According to the existing research results, the mixture in the middle of the specimen has a good compaction effect and can avoid the influence of the mold boundary effect; SmartRock sensor is embedded in the mixture in this study, as shown in Figure 6. Then, put the mixture into the oven for 2 hours. During the test, the oven temperatures are set at 110°C, 130°C, 150°C, and 170°C, respectively, and the specimens are rotated and compacted after aging [18–20].Table 2
AC-20 aggregate grading.
Sieve size (mm)26.5191613.29.54.752.361.180.60.30.150.75Design gradation10099.585.670.456.838.028.619.812.49.27.56.0Upper limit1001009082704636282216127Lower limit1009074625032221610643Figure 6
(a) Gyratory compactor and data acquisition device. (b) Spatial location of intelligent self-sensing aggregate sensor.
(a)(b)


## 2.1. IC Project Information

The test section of this study is in an expressway construction project in Shandong Province, China, which has a total length of 200 m and the width is 11.25 m, as shown in Figures1(a) and 1(b). The BOMAG double steel wheel vibratory rollers were used in the project, and the main mechanical parameters are shown in Table 1. The intelligent roller is equipped with acceleration sensor, infrared temperature sensor, positioning system, and compactness display device, as shown in Figure 1(c). The compaction degree and temperature change can be recorded and displayed in real time, so that project managers can adjust the compaction schedules in time based on the information.Figure 1
(a) The project location. (b) The pavement project. (c) The IC roller.
(a)(b)(c)Table 1
Main roller mechanical parameters.
ParameterValueTotal static mass (kg)13200Diameter (mm)1236Width (mm)1235Vibration amplitude (mm)0.69/0.29Vibration frequency (Hz)48/60Vibration force (Kn)18.6/12.1


## 2.2. ICMV and Geostatistical Model

In 1974, Heinz Thumer of Sweden proposed ICMV for the first time and tested it on Dynapac smooth wheel vibratory roller and granular soil [12–14]. In 1976, Swedish geodynamic company and Dynapac jointly developed the compaction measurement index based on CMV method [15, 16]. The index obtains the response signal of the roller under vibration excitation through the acceleration sensor and puts forward a method to judge the compaction quality by the ratio of second harmonic wave to shock wave, where C is a constant, generally taken as 300. The specific calculation method is as follows:
(1)CMV=C×A2ΩAΩ.CMV is a representative QC/QA index of compaction; it can evaluate the compaction degree based on changes in the acceleration signal of the vibrating wheel. Its working principle is shown in Figure2.Figure 2
(a) Schematic diagram of compaction process. (b) The variation of harmonics during compaction.
(a)(b)The semivariogram model based on geostatistical not only is able to describe the variation of continuous compaction parameters [17] but also describe its random variability. It is defined as half of the average variance between data values with a certain distance. The calculation method is shown in
(2)rh=12nh∑i=1nhzxi+h−zxi2,where nh is the logarithm of continuous compaction monitoring points on a compaction surface within h distance. nh, zxi, and zxi+h are the actual observed values of continuous compaction detection parameters at spatial positions xi and xi+h, respectively.The semivariogram curve is a function diagram of the semivariogram valuerh versus distance h [18]. It has three characteristic parameters: range, sill, and nugget, as shown in Figure 3. The semivariogram is generally expressed by the variation curve. The abscissa is the step size, and the ordinate is the value of the semivariogram. It can be seen from Figure 3 that the closer the distance, the smaller the semivariogram value and the greater the correlation. When the distance h approaches 0, which limr0=C0, it is called nugget effect, and C0 is called nugget constant. It reflects the degree of random variation of continuous compaction parameters. The farther the distance is, the greater the value of the semivariogram, indicating that the correlation between the attribute values of the two points is smaller. When h tends to infinity, where limr∞=C0+C, C is called the partial sill value. It reflects the nonuniformity of the compacted material.Figure 3
Typical sample semivariogram.This study discussed the homogeneity of asphalt mixtures at different compaction passes and temperatures based on semivariance functions. The calculated scatter cannot directly characterize the spatial variability of the continuous compaction degree. Therefore, in order to determine the value of rangea, the nugget value C0, and the partial sill value C, a semivariance function needs to be fitted, as shown in
(3)rh=0h=0,C0+C1−exp−hah>0.


## 2.3. Indoor Test Materials and Methods

### 2.3.1. Characteristic of SmartRock Sensor

Based on the field compaction tests, the SmartRock-based indoor gyratory compaction test was conducted to discuss the effect of temperature on the compaction process of asphalt mixtures. SmartRock is a sensor developed based on microelectromechanical system (MEMS) technology, which integrates several data acquisition modules such as stress, temperature, rotation, and acceleration, as well as Bluetooth wireless transmission module, enabling real-time acquisition and wireless transmission of multiple data [19–22], as shown in Figure 4.Figure 4
(a) SmartRock sensor. (b) Wireless antenna.
(a)(b)SmartRock sensor has two coordinate systems: global coordinates (X-Y-Z) and local coordinates (X′-Y′-Z′), as shown in Figure 5, and the local coordinate changes with the movement of SmartRock. Conversion relationship between local and global coordinates can be expressed by
(4)xyz=Rx,y,z,,where (x, y, z) and (x′, y′, z′) are the coordinate points in the global and local coordinate systems, respectively, and R is the transformation matrix between the global and local coordinate systems, which is specifically expressed as
(5)R=cosx,,xcosx,,ycosx,,zcosy,,xcosy,,ycosy,,zcosz,,xcosz,,ycosz,,z.Figure 5
Global-local coordinate system transformation.


### 2.3.2. Laboratory Test Material

AC-20 asphalt mixture was selected for indoor rotary compaction tests; the specific gradation is shown in Table2. The mixture was mixed at the optimum oil-to-rock ratio to better monitor the effect of temperature changes on the compaction process of the mixture. According to the existing research results, the mixture in the middle of the specimen has a good compaction effect and can avoid the influence of the mold boundary effect; SmartRock sensor is embedded in the mixture in this study, as shown in Figure 6. Then, put the mixture into the oven for 2 hours. During the test, the oven temperatures are set at 110°C, 130°C, 150°C, and 170°C, respectively, and the specimens are rotated and compacted after aging [18–20].Table 2
AC-20 aggregate grading.
Sieve size (mm)26.5191613.29.54.752.361.180.60.30.150.75Design gradation10099.585.670.456.838.028.619.812.49.27.56.0Upper limit1001009082704636282216127Lower limit1009074625032221610643Figure 6
(a) Gyratory compactor and data acquisition device. (b) Spatial location of intelligent self-sensing aggregate sensor.
(a)(b)


## 2.3.1. Characteristic of SmartRock Sensor

Based on the field compaction tests, the SmartRock-based indoor gyratory compaction test was conducted to discuss the effect of temperature on the compaction process of asphalt mixtures. SmartRock is a sensor developed based on microelectromechanical system (MEMS) technology, which integrates several data acquisition modules such as stress, temperature, rotation, and acceleration, as well as Bluetooth wireless transmission module, enabling real-time acquisition and wireless transmission of multiple data [19–22], as shown in Figure 4.Figure 4
(a) SmartRock sensor. (b) Wireless antenna.
(a)(b)SmartRock sensor has two coordinate systems: global coordinates (X-Y-Z) and local coordinates (X′-Y′-Z′), as shown in Figure 5, and the local coordinate changes with the movement of SmartRock. Conversion relationship between local and global coordinates can be expressed by
(4)xyz=Rx,y,z,,where (x, y, z) and (x′, y′, z′) are the coordinate points in the global and local coordinate systems, respectively, and R is the transformation matrix between the global and local coordinate systems, which is specifically expressed as
(5)R=cosx,,xcosx,,ycosx,,zcosy,,xcosy,,ycosy,,zcosz,,xcosz,,ycosz,,z.Figure 5
Global-local coordinate system transformation.


## 2.3.2. Laboratory Test Material

AC-20 asphalt mixture was selected for indoor rotary compaction tests; the specific gradation is shown in Table2. The mixture was mixed at the optimum oil-to-rock ratio to better monitor the effect of temperature changes on the compaction process of the mixture. According to the existing research results, the mixture in the middle of the specimen has a good compaction effect and can avoid the influence of the mold boundary effect; SmartRock sensor is embedded in the mixture in this study, as shown in Figure 6. Then, put the mixture into the oven for 2 hours. During the test, the oven temperatures are set at 110°C, 130°C, 150°C, and 170°C, respectively, and the specimens are rotated and compacted after aging [18–20].Table 2
AC-20 aggregate grading.
Sieve size (mm)26.5191613.29.54.752.361.180.60.30.150.75Design gradation10099.585.670.456.838.028.619.812.49.27.56.0Upper limit1001009082704636282216127Lower limit1009074625032221610643Figure 6
(a) Gyratory compactor and data acquisition device. (b) Spatial location of intelligent self-sensing aggregate sensor.
(a)(b)


## 3. Results and Discussions

### 3.1. Relationship between CMV and Roller Passes

Table3 shows the mathematical and geostatistical values of CMV for asphalt mixtures under different roller passes. It can be seen from the table that with the increase of roller passes, the mean value of CMV do not show an increasing trend with the increase of roller passes, i.e., a small decrease in the average value of CMV occurred after the completion of the sixth series of compaction. In addition, attention should be paid not only to the variation of compaction degree during the compaction process but also to the compaction uniformity which will have a significant influence on the construction quality and long-term service performance of pavement. This study further evaluates the variation of compaction uniformity during compaction through standard deviation (STD), coefficient of variation (COV), and partial sill C of CMV. It can be seen from Table 3 that the STD and C of CMV reach the minimum after the fifth compaction. Combined with the average value of the CMV and the number of roller passes, the compaction degree of the mixture and its homogeneity reaches the optimum level after the 5th compaction. Therefore, the compaction degree and uniformity will not always have a positive correlation with the number of compaction passes.Table 3
Compaction results with different roller passes.
Roller passesEvaluation results of mathematical statisticsGeostatistical evaluation resultsMeanμStandard deviationσNuggetC0SillC126.0913.495.9239.53256.4011.244.3335.32386.949.727.2432.844100.437.243.8131.805116.315.823.9828.926114.156.562.4930.66


### 3.2. Relationship between CMV and Temperature

Changes in temperature can have a significant effect on the dynamic modulus of the asphalt mixture during compaction, resulting in changes in CMV. This study investigates the effect of temperature variations on compaction degree and uniformity for the same pavement structure and mechanical parameters. Figure7 shows the temperature and CMV distribution of the asphalt mixture monitored during the first and last compaction in the test section. Through linear correlation analysis, it is found that there is a certain correlation between CMV and temperature. During the first compaction, the CMV values in the high temperature region are relatively low, while the high CMV values are mainly distributed in the low temperature region, as shown in Figures 7(a) and 7(b). However, during the last compaction, the asphalt mixture in the higher temperature area shows a higher CMV value, as shown in Figures 7(c) and 7(d).Figure 7
(a) Compaction degree and (b) temperature results of the first pass. (c) Compaction degree and (d) temperature results of the final pass.
(a)(b)(c)(d)In order to quantitatively describe the relationship between temperature and CMV during compaction, this study divides the temperature range into three classes based on the temperature of the initial compaction monitoring: class I, 180-150°C; class II, 150-120°C; and class III, 120-90°C; the corresponding areas in different temperature ranges are defined as C-I, C-II, and C-III. From Figure8, although only a few areas of the first compaction had an asphalt mixture temperature below 120°C, the CMV values in this area did not show a large gap with those in other compacted areas. This is mainly due to the higher modulus of the asphalt mixture in the lower temperature area during the initial compaction, which results in a higher CMV value. However, during the last compaction, the CMV value of the asphalt mixture in the higher temperature zone increased significantly, which is much higher than that monitored in the lower temperature zone, as shown in Figure 8. This is due to the reduction in viscosity caused by the asphalt at high temperatures. The aggregates are prone to displacement and rotation under external loads and the lubrication of the asphalt, resulting in an asphalt mix that is easily compacted and has an increased modulus. Conversely, when the temperature of the asphalt mixture decreases, the viscosity of the asphalt increases. In this case, the aggregate has to overcome greater resistance when it is displaced or rotated, resulting in a less compacted asphalt mixture and a slower increase in modulus.Figure 8
Variation of CMV in the compaction zone at different temperatures.This study evaluates the compaction uniformity of asphalt mixtures in areas C-I, C-II, and C-III after the last compaction based on semivariation function, as shown in Figure9 and Equations (6)–(8). In the area of C-I with higher temperature, the partial sill C is 11.79, which indicates that the compaction degree variation in this area is low and the uniformity of compacted asphalt mixture is high. Low temperatures in C-III lead to an increase in asphalt viscosity, which reduces compaction uniformity to some extent.
(6)y=C0+C1−e−h/a,R2=0.89,C0=12.18,C=11.79,a=5.37,(7)y=C0+C1−e−h/a,R2=0.84,C0=4.24,C=17.35,a=1.34,(8)y=C0+C1−e−h/a,R2=0.86,C0=35.23,C=41.35,a=4.37.Figure 9
(a) Geostatistical analysis results for region C-I. (b) Geostatistical analysis results for region C-II. (c) Geostatistical analysis results for region C-III.
(a)(b)(c)


### 3.3. Analysis of the Effect of Temperature on the Compaction Process

It can be seen from Figure10(a) that the stress in the x, y, and z directions measured by SmartRock sensor gradually increases with the increasing of gyratory compaction cycles. It should be noted that the stress in the x and y directions is much lower than the stress in the z direction, because the mixture is subjected to a combination of vertical positive stress and horizontal shear stress during rotary compaction, which increases the density of the mixture, with the vertical positive stress being the main reason in the increase in compaction degree. At the same time, the change of specimen height during rotating compaction can be used to indicate the mixed compaction state. In the initial compaction phase, the voids between the mixes are large, the interlocking forces between the aggregates are weak, and the height of the specimen falls faster under external loads. After approximately 70 rotational compaction cycles, the height variation of the specimen stability and the rate of stress increase monitored by the SmartRock gradually plateaus, indicating that the voids between the aggregates are reduced at this point and the mixture is in a relatively stable state of compaction.Figure 10
(a) Contact force and specimen height change during compaction. (b) Temperature changes during compaction. (c) Relative rotation changes during compaction.
(a)(b)(c)As shown in Figure10(b), the rotation of SmartRock around the x and y axes follows a similar trend to that of the mixture height, with its relative rotation being larger at the initial compaction stage and decreasing as the number of compaction cycles increases until it reaches a relatively small plateau, at which point the rotation angle is low and stable, indicating that the mixture has reached a stable compaction state. It also further shows that the displacement and rotation of the aggregate in the vertical direction is one of the main reasons for the increase in compaction degree.In this study, the SmartRock sensor is embedded in the asphalt mixture to record the temperature changes inside the mixture during the gyratory compaction process in real time. Because the outer packing of the SmartRock sensor is a high temperature resistant coating, there is a slow heat conduction process when the mixture is initially embedded. Meanwhile, the temperature changes of the same asphalt mixture are collected in real time by digital thermometer in this test. As shown in Figure10(c), during the initial compaction phase, the temperature monitored by the sensor rises continuously and is the same as that recorded by an electronic thermometer at 60 compaction times, which realistically reflects the temperature change of the mixture during subsequent compaction.Figure11 shows the change in rotation of SmartRock in the x, y, and z directions at different temperatures. As the number of compaction cycles increases, the angle of rotation gradually smooths out. A stable and small value of the angle of rotation is one of the signs of a stable asphalt mixture compaction. In particular, the relative turning angles of the SmartRock in the x and y directions show a similar trend, changing from a relatively high turning angle to a stable plateau period, showing a consistent pattern with the change in specimen height during compaction. By analyzing the number of compaction cycles at different temperature conditions when the SmartRock’s rotation into the plateau phase, the number of compaction times required for SmartRock to research the plateau phase gradually decreases as the temperature increases, with a minimum value occurring at 150°C, followed by a significant increase, as shown in Figure 12. Higher temperatures increase the mobility of the aggregates in the asphalt, and under the action of external loads, the aggregates are more likely to be displaced and rotated to form a well-embedded skeletal structure, i.e., it is easier to form a compacted and dense mixture; when the temperature is low, the viscosity of the asphalt is greater, increasing the cohesion between the aggregates, and the compaction process needs more compaction energy to overcome the shear force; secondly, the viscosity of the binding material is poorly mobile and cannot form a complete asphalt film on the surface of the mineral particles, weakening the lubricating effect of the asphalt, which is not conducive to the displacement and rotation of the aggregates.Figure 11
Relative rotation changes under different compaction temperature.
(a)(b)(c)Figure 12
Number of gyratory compaction cycles required to reach plateau.


## 3.1. Relationship between CMV and Roller Passes

Table3 shows the mathematical and geostatistical values of CMV for asphalt mixtures under different roller passes. It can be seen from the table that with the increase of roller passes, the mean value of CMV do not show an increasing trend with the increase of roller passes, i.e., a small decrease in the average value of CMV occurred after the completion of the sixth series of compaction. In addition, attention should be paid not only to the variation of compaction degree during the compaction process but also to the compaction uniformity which will have a significant influence on the construction quality and long-term service performance of pavement. This study further evaluates the variation of compaction uniformity during compaction through standard deviation (STD), coefficient of variation (COV), and partial sill C of CMV. It can be seen from Table 3 that the STD and C of CMV reach the minimum after the fifth compaction. Combined with the average value of the CMV and the number of roller passes, the compaction degree of the mixture and its homogeneity reaches the optimum level after the 5th compaction. Therefore, the compaction degree and uniformity will not always have a positive correlation with the number of compaction passes.Table 3
Compaction results with different roller passes.
Roller passesEvaluation results of mathematical statisticsGeostatistical evaluation resultsMeanμStandard deviationσNuggetC0SillC126.0913.495.9239.53256.4011.244.3335.32386.949.727.2432.844100.437.243.8131.805116.315.823.9828.926114.156.562.4930.66


## 3.2. Relationship between CMV and Temperature

Changes in temperature can have a significant effect on the dynamic modulus of the asphalt mixture during compaction, resulting in changes in CMV. This study investigates the effect of temperature variations on compaction degree and uniformity for the same pavement structure and mechanical parameters. Figure7 shows the temperature and CMV distribution of the asphalt mixture monitored during the first and last compaction in the test section. Through linear correlation analysis, it is found that there is a certain correlation between CMV and temperature. During the first compaction, the CMV values in the high temperature region are relatively low, while the high CMV values are mainly distributed in the low temperature region, as shown in Figures 7(a) and 7(b). However, during the last compaction, the asphalt mixture in the higher temperature area shows a higher CMV value, as shown in Figures 7(c) and 7(d).Figure 7
(a) Compaction degree and (b) temperature results of the first pass. (c) Compaction degree and (d) temperature results of the final pass.
(a)(b)(c)(d)In order to quantitatively describe the relationship between temperature and CMV during compaction, this study divides the temperature range into three classes based on the temperature of the initial compaction monitoring: class I, 180-150°C; class II, 150-120°C; and class III, 120-90°C; the corresponding areas in different temperature ranges are defined as C-I, C-II, and C-III. From Figure8, although only a few areas of the first compaction had an asphalt mixture temperature below 120°C, the CMV values in this area did not show a large gap with those in other compacted areas. This is mainly due to the higher modulus of the asphalt mixture in the lower temperature area during the initial compaction, which results in a higher CMV value. However, during the last compaction, the CMV value of the asphalt mixture in the higher temperature zone increased significantly, which is much higher than that monitored in the lower temperature zone, as shown in Figure 8. This is due to the reduction in viscosity caused by the asphalt at high temperatures. The aggregates are prone to displacement and rotation under external loads and the lubrication of the asphalt, resulting in an asphalt mix that is easily compacted and has an increased modulus. Conversely, when the temperature of the asphalt mixture decreases, the viscosity of the asphalt increases. In this case, the aggregate has to overcome greater resistance when it is displaced or rotated, resulting in a less compacted asphalt mixture and a slower increase in modulus.Figure 8
Variation of CMV in the compaction zone at different temperatures.This study evaluates the compaction uniformity of asphalt mixtures in areas C-I, C-II, and C-III after the last compaction based on semivariation function, as shown in Figure9 and Equations (6)–(8). In the area of C-I with higher temperature, the partial sill C is 11.79, which indicates that the compaction degree variation in this area is low and the uniformity of compacted asphalt mixture is high. Low temperatures in C-III lead to an increase in asphalt viscosity, which reduces compaction uniformity to some extent.
(6)y=C0+C1−e−h/a,R2=0.89,C0=12.18,C=11.79,a=5.37,(7)y=C0+C1−e−h/a,R2=0.84,C0=4.24,C=17.35,a=1.34,(8)y=C0+C1−e−h/a,R2=0.86,C0=35.23,C=41.35,a=4.37.Figure 9
(a) Geostatistical analysis results for region C-I. (b) Geostatistical analysis results for region C-II. (c) Geostatistical analysis results for region C-III.
(a)(b)(c)


## 3.3. Analysis of the Effect of Temperature on the Compaction Process

It can be seen from Figure10(a) that the stress in the x, y, and z directions measured by SmartRock sensor gradually increases with the increasing of gyratory compaction cycles. It should be noted that the stress in the x and y directions is much lower than the stress in the z direction, because the mixture is subjected to a combination of vertical positive stress and horizontal shear stress during rotary compaction, which increases the density of the mixture, with the vertical positive stress being the main reason in the increase in compaction degree. At the same time, the change of specimen height during rotating compaction can be used to indicate the mixed compaction state. In the initial compaction phase, the voids between the mixes are large, the interlocking forces between the aggregates are weak, and the height of the specimen falls faster under external loads. After approximately 70 rotational compaction cycles, the height variation of the specimen stability and the rate of stress increase monitored by the SmartRock gradually plateaus, indicating that the voids between the aggregates are reduced at this point and the mixture is in a relatively stable state of compaction.Figure 10
(a) Contact force and specimen height change during compaction. (b) Temperature changes during compaction. (c) Relative rotation changes during compaction.
(a)(b)(c)As shown in Figure10(b), the rotation of SmartRock around the x and y axes follows a similar trend to that of the mixture height, with its relative rotation being larger at the initial compaction stage and decreasing as the number of compaction cycles increases until it reaches a relatively small plateau, at which point the rotation angle is low and stable, indicating that the mixture has reached a stable compaction state. It also further shows that the displacement and rotation of the aggregate in the vertical direction is one of the main reasons for the increase in compaction degree.In this study, the SmartRock sensor is embedded in the asphalt mixture to record the temperature changes inside the mixture during the gyratory compaction process in real time. Because the outer packing of the SmartRock sensor is a high temperature resistant coating, there is a slow heat conduction process when the mixture is initially embedded. Meanwhile, the temperature changes of the same asphalt mixture are collected in real time by digital thermometer in this test. As shown in Figure10(c), during the initial compaction phase, the temperature monitored by the sensor rises continuously and is the same as that recorded by an electronic thermometer at 60 compaction times, which realistically reflects the temperature change of the mixture during subsequent compaction.Figure11 shows the change in rotation of SmartRock in the x, y, and z directions at different temperatures. As the number of compaction cycles increases, the angle of rotation gradually smooths out. A stable and small value of the angle of rotation is one of the signs of a stable asphalt mixture compaction. In particular, the relative turning angles of the SmartRock in the x and y directions show a similar trend, changing from a relatively high turning angle to a stable plateau period, showing a consistent pattern with the change in specimen height during compaction. By analyzing the number of compaction cycles at different temperature conditions when the SmartRock’s rotation into the plateau phase, the number of compaction times required for SmartRock to research the plateau phase gradually decreases as the temperature increases, with a minimum value occurring at 150°C, followed by a significant increase, as shown in Figure 12. Higher temperatures increase the mobility of the aggregates in the asphalt, and under the action of external loads, the aggregates are more likely to be displaced and rotated to form a well-embedded skeletal structure, i.e., it is easier to form a compacted and dense mixture; when the temperature is low, the viscosity of the asphalt is greater, increasing the cohesion between the aggregates, and the compaction process needs more compaction energy to overcome the shear force; secondly, the viscosity of the binding material is poorly mobile and cannot form a complete asphalt film on the surface of the mineral particles, weakening the lubricating effect of the asphalt, which is not conducive to the displacement and rotation of the aggregates.Figure 11
Relative rotation changes under different compaction temperature.
(a)(b)(c)Figure 12
Number of gyratory compaction cycles required to reach plateau.


## 4. Conclusions

In this paper, the effect of temperature on asphalt mixture compaction and its uniformity was investigated by IC field tests, and the mechanism of the effect of temperature on asphalt mixture compaction was studied on a mesoscale using SmartRock sensors, with the following main findings:(1)
By analyzing the spatial distribution characteristics of CMV, semivariation function is introduced to describe and evaluate the homogeneity of asphalt mixture during compaction. It is found that the degree and uniformity of compaction are not always positively correlated with the number of compaction cycles; excessive compaction will result in the decrease of compaction and uniformity(2)
The change characteristics of compactness and homogeneity of asphalt mixtures in different temperature ranges during field compaction are studied, and it is found that the change of temperature will cause significant differences in compaction characteristics of asphalt mixtures. During the compaction of asphalt mixture with higher temperature, the degree of compaction increases rapidly and the final compaction uniformity is higher(3)
The stress and rotation characteristics of aggregates at different compaction temperatures are investigated on a mesoscale using SmartRock sensor and gyratory compactor to reveal the effect of temperature changes on aggregate movement and rotation. It is found that the asphalt mixtures are not easily compacted at higher temperatures, but only at the appropriate temperature range



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*Source: 2224322-2023-02-09.xml*