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Fatigue damage is a notable distress of asphalt pavements. However, monitoring the development process of such distresses in real-time remained challenges for researchers. In this paper, the self-developed sensing facility has been utilized to obtain aggregate kinematic response of semi-circular specimen during the fatigue loading process in the upper-middle (H/2), lower-middle (L/2) and lower-right (L/3) positions of the specimen. Angle and acceleration variation in the X, Y, and Z directions were analysed independently at three positions. The correlation between the kinematic characteristic of the aggregate and the vertical deformation of the asphalt mixture specimen is established. The results illustrated that the asphalt mixture viscoelasticity attenuation during fatigue test caused the angle accumulation and acceleration response change of the specimen. The angle accumulation in the X-axis direction and acceleration variation in Y-axis direction of the aggregate at the H/2 position exhibited a significant three-stage change. Angle-Accumulation Rate (AAR) of change is suggested as a long-term monitoring index for the fatigue development of the asphalt mixture. The proposal of AAR index creatively combines the long-term kinematic behaviour and mechanical behaviour of asphalt mixture, making long-term and real-time monitoring of the asphalt pavement health from the perspective of kinematics become possible.

Fatigue damage is a notable distress of asphalt pavements. However, monitoring the development process of such distresses in real-time remained challenges for researchers. In this paper, the self-developed sensing facility has been utilized to obtain aggregate kinematic response of semi-circular specimen during the fatigue loading process in the upper-middle (H/2), lower-middle (L/2) and lower-right (L/3) positions of the specimen. Angle and acceleration variation in the X, Y, and Z directions were analysed independently at three positions. The correlation between the kinematic characteristic of the aggregate and the vertical deformation of the asphalt mixture specimen is established. The results illustrated that the asphalt mixture viscoelasticity attenuation during fatigue test caused the angle accumulation and acceleration response change of the specimen. The angle accumulation in the X-axis direction and acceleration variation in Y-axis direction of the aggregate at the H/2 position exhibited a significant three-stage change. Angle-Accumulation Rate (AAR) of change is suggested as a long-term monitoring index for the fatigue development of the asphalt mixture. The proposal of AAR index creatively combines the long-term kinematic behaviour and mechanical behaviour of asphalt mixture, making long-term and real-time monitoring of the asphalt pavement health from the perspective of kinematics become possible.

Variations in pavement density have been widely monitored and investigated, both in laboratory and in field experiments, since the compaction of pavement is so critical to its long-term performance quality. In contrast to field testing, laboratory tests are simpler to produce but less accurate. Destructive drilled samples are used to conduct field testing; however, they are limited in their ability to assess density information at specific areas. The use of computationally aided approaches, such as the Finite Element Method (FEM) and the Discrete Element Method (DEM), in research involving asphalt mixtures is increasing, since these methods simulate and evaluate the characteristics of asphalt mixtures at macroscopic and microscopic scales. Individual particle behavior at the microscopic level cannot be fully represented using the FEM alone, and the computing cost of utilizing the DEM approach alone is prohibitively high. The objective of this work is to simulate the pre-compaction process by using the coupled FEM-DEM approach. In order to investigate the impact of the asphalt mixtures’ gradation, a dense-graded asphalt mixture (AC 11) and a gap graded asphalt mixture (PA 11) were simulated. Different paving speeds (4, 5, and 6 m/min) were applied on the preliminary compaction model of AC 11 to study the effect of the paving speeds on the compaction process. By comparing the angular velocity, which worked as a reference of compaction quality, it was demonstrated that the grade AC 11 asphalt mixtures performed better in the preliminary compaction process compared to the grade PA 11 asphalt mixtures. Moreover, since it has an effect on compaction, paving speed was carefully monitored and kept within a reasonable range in order to maximize both pavement quality and project efficiency.

Pavement compaction cannot be neglected during the motorway manufacture stage because it can determine pavement service quality and durability. Concerning the compaction scenario, the paving compaction is responsible for offering the preliminary strength of the pavement. Ignoring paving compaction quality control can lead to over compaction. This paper introduces an integral system to study and simulate the paving compaction of asphalt motorways in Discrete Element Model two-dimensional (DEM2D). This method includes the whole procedure from aggregate image acquisition database establishment to the DEM2D simulation of paving compaction. To this end, this study fulfils the creation of the aggregate database applied in DEM via the Aggregate Image Measuring System (AIMS) method. In addition, the artificial intelligent (AI) technology called Generative Adversarial Networks (GANs) method is proposed to expand the developed DEM aggregate database. Three different approaches are applied to calibrate the accuracy of the extended database. According to the aggregate database, the pavement paving compaction with different aggregate gradations can be simulated in DEM2D.

Pavement compaction cannot be neglected during the motorway manufacture stage because it can determine pavement service quality and durability. Concerning the compaction scenario, the paving compaction is responsible for offering the preliminary strength of the pavement. Ignoring paving compaction quality control can lead to over compaction. This paper introduces an integral system to study and simulate the paving compaction of asphalt motorways in Discrete Element Model two-dimensional (DEM2D). This method includes the whole procedure from aggregate image acquisition database establishment to the DEM2D simulation of paving compaction. To this end, this study fulfils the creation of the aggregate database applied in DEM via the Aggregate Image Measuring System (AIMS) method. In addition, the artificial intelligent (AI) technology called Generative Adversarial Networks (GANs) method is proposed to expand the developed DEM aggregate database. Three different approaches are applied to calibrate the accuracy of the extended database. According to the aggregate database, the pavement paving compaction with different aggregate gradations can be simulated in DEM2D.

Variations in pavement density have been widely monitored and investigated, both in laboratory and in field experiments, since the compaction of pavement is so critical to its long-term performance quality. In contrast to field testing, laboratory tests are simpler to produce but less accurate. Destructive drilled samples are used to conduct field testing; however, they are limited in their ability to assess density information at specific areas. The use of computationally aided approaches, such as the Finite Element Method (FEM) and the Discrete Element Method (DEM), in research involving asphalt mixtures is increasing, since these methods simulate and evaluate the characteristics of asphalt mixtures at macroscopic and microscopic scales. Individual particle behavior at the microscopic level cannot be fully represented using the FEM alone, and the computing cost of utilizing the DEM approach alone is prohibitively high. The objective of this work is to simulate the pre-compaction process by using the coupled FEM-DEM approach. In order to investigate the impact of the asphalt mixtures’ gradation, a dense-graded asphalt mixture (AC 11) and a gap graded asphalt mixture (PA 11) were simulated. Different paving speeds (4, 5, and 6 m/min) were applied on the preliminary compaction model of AC 11 to study the effect of the paving speeds on the compaction process. By comparing the angular velocity, which worked as a reference of compaction quality, it was demonstrated that the grade AC 11 asphalt mixtures performed better in the preliminary compaction process compared to the grade PA 11 asphalt mixtures. Moreover, since it has an effect on compaction, paving speed was carefully monitored and kept within a reasonable range in order to maximize both pavement quality and project efficiency.

Asphalt pavement compaction is important, and it can determine the service quality as well as durability of pavement. In recent years, numerical methods have been extensively used to simulate and study the construction process of asphalt pavement and mechanical properties of asphalt mixtures. In the following sections, the compaction process, considering the interaction between the materials and the equipment, is simulated, and the influence of different compaction methods on the mechanical performance of asphalt mixtures is investigated. To achieve this goal, a pre-compaction model is developed using the Discrete Element Method (DEM), and the models of both materials and the paving machine are generated separately. After the pre-compaction simulation, the theory of bounding surface plasticity is combined with the theory of Finite Element Method (FEM) as well as with a kinematic model of a roller drum to simulate the asphalt mixture behavior during a roller pass. In order to ensure consistency both in the laboratory compaction and in-situ compaction, the Aachen compactor has been developed. The effect of different compaction methods (Field, Aachen and Marshall Compactions) on the asphalt specimens is compared and evaluated using the microscale FEM.

Asphalt pavement compaction is important, and it can determine the service quality as well as durability of pavement. In recent years, numerical methods have been extensively used to simulate and study the construction process of asphalt pavement and mechanical properties of asphalt mixtures. In the following sections, the compaction process, considering the interaction between the materials and the equipment, is simulated, and the influence of different compaction methods on the mechanical performance of asphalt mixtures is investigated. To achieve this goal, a pre-compaction model is developed using the Discrete Element Method (DEM), and the models of both materials and the paving machine are generated separately. After the pre-compaction simulation, the theory of bounding surface plasticity is combined with the theory of Finite Element Method (FEM) as well as with a kinematic model of a roller drum to simulate the asphalt mixture behavior during a roller pass. In order to ensure consistency both in the laboratory compaction and in-situ compaction, the Aachen compactor has been developed. The effect of different compaction methods (Field, Aachen and Marshall Compactions) on the asphalt specimens is compared and evaluated using the microscale FEM.