Prediction of road properties of asphalt mixture subjected to three times aging-regeneration cycles based on a GA-BP neural network asphalt binder

Materials genome research has been rapidly evolved, aiming at the development of future pavement materials. It has been gradually applied to studying the properties of asphalt and asphalt mixtures. In this study, the prediction of the road properties of asphalt mixtures using asphalt binders subjected to multiple aging and regeneration cycles was systematically explored using various experimental tests. Additionally, various characterizations were carried out to analyse the variation law of road properties of the asphalt mixtures after three aging-regeneration cycles. Finally, a Genetic Algorithm-Back Propagation (GA-BP) neural network was adopted to establish a prediction model for the performance of asphalt mixtures based on asphalt binders subjected to multiple aging-regeneration cycles. Results showed that the penetration finally recovered to 80.7%, and the softening point ultimately reached 115% of that before aging. However, the road properties of the asphalt mixtures after the implementation of three aging-regeneration cycles presented a differentiated evolution. In terms of high-temperature performance, the dynamic stability reached 183.8% and the penetration strength rose to 150% with the increase times of regeneration. Regarding the low-temperature performance, although the flexural-tensile strength increased to 121%, the fracture energy and tensile strength gradually decreased, both remaining above 68% of those of unaged mixtures after the third regeneration. The material showed favorable water stability; specifically, its residual stability and freeze-thaw splitting strength ratio finally stabilized at over 90% and maintained this level. In terms of dynamic viscoelasticity, although three aging-regeneration cycles altered the viscoelastic balance of the mixture, the dynamic response characteristics similar to those of new mixtures were not eliminated. According to the grey correlation analysis of the performance of asphalt mixtures and asphalt, penetration, softening point, rotational viscosity, visco-toughness, and toughness, relatively high grey correlation degrees with the asphalt mixtures were shown. The established GA-BP neural network can effectively build a robust model for predicting the road properties of asphalt mixtures subjected to multiple aging-regeneration cycles, with small relative errors. Our work provides a valuable reference for systematically studying the materials genome of asphalt and asphalt mixtures.

1 Introduction

With the rapid development of the road engineering industry, hot recycling technology for asphalt pavements has been widely applied and promoted worldwide due to its excellent performance and significant resource-saving advantages (Qi, et al., 2024; Ge, et al., 2024; Zhang, et al., 2023). Currently, asphalt pavements are gradually entering a critical stage of large-scale maintenance and renovation. Whether asphalt mixtures can realize secondary or even multiple recycling (especially retaining recycling value after multiple aging cycles) has attracted wide attention from both the industrial and scientific communities (Xu, et al., 2024; Sun, et al., 2024).

During the aging process of asphalt, small molecules such as ketones, carboxyl groups, benzyl groups, and aliphatic side chains undergo polymerization reactions, triggering polar association between the asphalt molecules and leading to an increase in the content of large molecular weight components such as asphaltenes (O'Connell, et al., 2024; Rädle, et al., 2024). This process is specifically manifested as mass loss caused by the volatilization of light components. This effect leads to an increase in the asphalt molecular weight, and induces an oxidation of the chemical functional groups such as carbonyl and sulfoxide groups (Yu L et al., 2024; Ghafar, et al., 2024; Li, et al., 2024). Therefore, during asphalt regeneration, it is necessary to add rejuvenators to the aged asphalt. Their core role is to supplement various light components, such as aromatics and saturates, and reduce the proportion of heavy components such as resins and asphaltenes. Thereby, the regenerative performance of aged asphalt can be effectively restored. In addition, the large amount of aromatic hydrocarbons in rejuvenators can reduce the difference in the solubility parameters between the aged asphalt and rejuvenators, thereby promoting the compatibility between the modifiers and asphalt (Mei, et al., 2025; Carrión, et al., 2023).

In the research field of asphalt aging and regeneration performance, multi-dimensional explorations focusing on different aging methods and the mechanism of rejuvenators have been conducted. Guo et al. systematically compared the impact of typical aging methods, such as RTFOT (Rotary Thin-Film Oven Test), PAV (Pressure Aging Vessel), and UVI (ultraviolet aging) on the performance of neat asphalt. By testing the softening point, stiffness modulus, and complex modulus, a significant decreasing trend after aging was found for the above-mentioned indicators of asphalt, revealing the common degradation laws of different aging paths on asphalt viscoelastic properties (Guo, et al., 2023). Focusing on the particularity of multiple aging-regeneration cycles, Lu et al. studied the secondary aging and regeneration process. The authors found that by adding rejuvenators and new asphalt in combination, the generic fractions of asphalt after secondary aging-regeneration could basically recover to the level of primary aging-regeneration. However, the effective repair of various characteristic oxidized functional groups, such as carbonyl and sulfoxide groups, was difficult, which could directly lead to essential differences in the viscoelastic characteristics between the asphalt after secondary cycles and that after primary cycles (Lu, et al., 2024). In terms of the dosage effect of rejuvenators, Yao et al. took SBS-modified asphalt as the research object and analysed the influence of rejuvenator content on penetration, softening point, ductility, and viscosity of the primary aging-regeneration system. The results showed that with the increase in the rejuvenator content, the softening point and viscosity of regenerated asphalt showed a decreasing trend, while ductility and penetration gradually increased. These effects confirmed that the regulatory effect of rejuvenators on the performance of modified asphalt had a clear dose-effect relationship (Yao, et al., 2021). Zhang et al. further explored the synergistic effect of new asphalt and rejuvenators. It was found that the low-temperature crack resistance of asphalt was significantly deteriorated with the deepening of the aging degree. On the contrary, the light components (aromatics and saturates) could effectively restore most of the service performance of aged asphalt, providing a theoretical basis for rejuvenator compatibility schemes (Zhang, et al., 2024).

At the asphalt mixture, various works in the literature have focused on the interface characteristics of the regeneration and the evolution of road properties. In particular, Li et al. found that the fusion degree between the neat asphaltand primary aged asphalt had a decisive impact on the high-temperature performance, low-temperature performance, and water stability of regenerated mixtures. Moreover, the fusion time was strongly correlated with Reclaimed Asphalt Pavement (RAP) content, RAP aging degree, and mixture gradation, providing key references for optimizing the regeneration process parameters (Li, et al., 2025). Wu et al. evaluated the application effect of Styreneic Methyl Copolymer (SMC) rejuvenators. The authors found that for SBS-modified asphalt subjected to 0–20 h PAV aging, when the SMC rejuvenator content was 1.56%–6.91%, the road properties of the regenerated mixtures with 50%–60% RAP content could meet the specification requirements, verifying the engineering applicability of such rejuvenators (Jing, et al., 2023). Wang et al. compared the secondary aging behaviour of the new asphalt and primary aging-regeneration mixtures. It was demonstrated that the road properties of the former after aging were comprehensively better than those of the latter, with a 22.5% difference in the flow value attenuation amplitude and a 53.5% difference in fracture energy. These results revealed the performance shortcomings of the regenerated materials during long-term service (Wang, et al., 2023).

Considering the above-mentioned analysis and taking into account the actual service life and designed service life of asphalt pavements, it can be inferred that the majority of the reported works in the literature have mostly focused on once or twice aging and regeneration cycles of asphalt. However, systematic research on three times aging-regeneration cycles of asphalt and its mixtures is still scarce. In addition, among the existing achievements, the correlation analysis between the performance of aged-regenerated asphalt and the road properties of corresponding asphalt mixtures of the asphalt mixture genome is relatively insufficient, making it difficult to fully reveal the intrinsic connection between them. To effectively address these issues, in this work, 70# neat asphalt was first selected. The variation laws of various asphalt indicators, such as penetration, softening point, ductility, viscosity, visco-toughness, and toughness, as well as the variation laws of high-temperature, low-temperature, water stability, and viscoelastic properties of asphalt mixtures through multiple aging-regeneration cycles of asphalt and its mixtures were systematically investigated. Second, based on the grey correlation analysis theory, the correlation between the asphalt performance indicators and the asphalt mixture performance indicators was explored. Finally, a GA-BP neural network was used to establish a prediction model of the asphalt performance indicators for evaluating asphalt mixture road properties. The accuracy of the prediction model was also verified by comparing the predicted values with the measured values. The research flowchart of this study is shown in Figure 1.

Figure 1

Figure 1. Flowchart of this research.

2 Materials and methods

2.1 Materials

2.1.1 Asphalt binder and aggregates

In this work, 70# neat asphalt was selected for performing the aging and regeneration tests, and Table 1 presents the technical indicators of 70# neat asphalt and the rejuvenator.

Table 1

Table 1. Technical indicators of the 70# neat asphalt and rejuvenator.

The new aggregates used in this work were divided into four grades: 0–3 mm, 3–5 mm, 5–10 mm, and 10–15 mm. Among them, the 0–3 mm and 3–5 mm aggregates were limestone, while the 5–10 mm and 10–15 mm aggregates were basalt. The mineral powder was limestone mineral powder. The technical indicators of the aggregates and mineral powder are shown in Table 2.

Table 2

Table 2. Technical indicators of the aggregates.

2.1.2 Asphalt mixture

After conducting the indoor aging test of the recycled asphalt mixture, an extractor was used to conduct an extraction test on the aged asphalt mixture to separate the mineral aggregate from the asphalt. In parallel, a sieve analysis test was performed on the extracted mineral aggregate. In this work, the asphalt mixture was subjected to three aging and regeneration cycles. After each aging, the content of reclaimed asphalt pavement (RAP) material was 50%, and the content of new material was 50%. To compare the influence of the times of regeneration cycles of the asphalt mixture on its performance, the aggregate gradation design was basically kept consistent. The aggregate gradation and asphalt-aggregate ratio of the new AC-13 mixture, and the three regeneration mixtures (RAC-1, RAC-2, and RAC-3) are shown in Table 3.

Table 3

Table 3. Gradation and asphalt-aggregate ratio of AC-13 and RAC-13.

2.2 Methods

2.2.1 Preparation of reclaimed asphalt

The short-term aging test method of the SHRP program can be used to simulate the aging performance of asphalt mixtures after 5–7 years. Therefore, the aging and regeneration of asphalt and asphalt mixtures in this study is shown in Figure 2.

1. Asphalt mixture aging test. In the laboratory, the mixed asphalt mixture was evenly spread in an enamel pan with a loose paving weight of approximately 21–22 kg/m²; then, the asphalt mixture was placed in an oven at 160 °C and heated for 9 h under forced ventilation conditions.

2. The aged asphalt mixture and trichloroethylene were put into an asphalt extractor to obtain an aged asphalt containing solvent, and a pure aged asphalt was obtained by the Abson distillation method for asphalt recovery.

3. Regenerated asphalt was obtained by adding a rejuvenator accounting for 8% of the aged asphalt content to the aged asphalt (Li, et al., 2025).

Figure 2

Figure 2. Asphalt aging and regenerated processes.

In this work, the asphalt mixture was subjected to 3 times aging and regeneration cycles, with the above steps repeated in each cycle.

2.2.2 Asphalt test

In this work, various indicators, such as penetration, softening point, ductility, viscosity, visco-toughness, and toughness of asphalt after three aging and regeneration cycles were tested, and the instruments used are shown in Figure 3.

1. Penetration test. The test temperature was 25 °C.

2. Ductility test. The test temperature was 15 °C, and the stretching speed of the instrument was 5 cm/min.

3. Softening point test. During the test, the water temperature in the beaker was maintained at 5 °C.

4. Viscosity test. A Brookfield viscometer was used to test the asphalt viscosity. The test temperature was 135 °C, the rotor speed was 20 r/min, and the torque reading range was 10%–98%.

5. Visco-toughness and toughness test. The test temperature of the tester was 25 °C, and the stretching speed was 500 mm/min.

Figure 3

Figure 3. Penetration and softening point of the aged and reclaimed asphalt.

2.2.3 Asphalt mixture test

In this work, the high-temperature performance, low-temperature performance, water stability, and dynamic viscoelasticity of the multiple-recycled asphalt mixtures were tested.

1. High-temperature performance. Rutting test and uniaxial penetration strength test were used to investigate the high-temperature performance of asphalt mixtures. For the rutting test, the temperature was 60 °C, the wheel pressure was 0.7 MPa, and the reciprocating rolling speed of the test wheel was 42 times per minute. The specimen size was 300 mm in length, 300 mm in width, and 50 mm in height. For the uniaxial penetration strength test, cylindrical specimens of φ150 × 105 mm were formed by using the gyratory compaction method. Then, they were cored and cut into cylindrical specimens of φ100 × 100 mm, with the air voids controlled at 7% ± 0.5%. The loading rate was 1 mm/min, the indenter diameter was 28.5 mm, and the test temperature was 60 °C.

2. Low-temperature performance. Low-temperature beam bending and semi-circular bending (SCB) tests were carried out to evaluate the low-temperature performance of asphalt mixtures. For the low-temperature beam bending test, the temperature was −10 °C, the loading rate was 50 mm/min, and the specimen size was 250 mm in length, 30 mm in width, and 35 mm in height. The SCB specimens were formed by a gyratory compactor: the asphalt mixture was gyratory compacted into cylindrical specimens of φ150 × 105 mm. Next, they were cut into semi-circular specimens with a diameter of 150 mm and a thickness of 50 mm, with the air voids controlled at 7% ± 0.5%. A pre-cut notch was made at the mid-span of the bottom of the semi-circular specimen to ensure that the mixture cracking initiates from the pre-cut notch, with the depth of the pre-cut notch being 15 mm. The loading rate was 1 mm/min, and the test temperature was −10 °C.

3. Water stability. Freeze-thaw splitting and immersed Marshall tests were conducted to evaluate the water damage resistance of asphalt mixtures. For the freeze-thaw splitting test, the specimen freezing temperature was −18 °C, the freezing duration was 16 h, the test temperature was 25 °C, and the loading speed was 50 mm/min. The specimen diameter was 101.6 mm, and the height was 63.5 mm.

4. Viscoelastic performance. The dynamic modulus test was used to evaluate the viscoelastic performance of the asphalt mixtures, i.e., the relationship between the stress and strain of the asphalt mixtures under dynamic load conditions. The test temperatures were 4 °C, 20 °C, and 40 °C; the load frequencies were 25 Hz, 10 Hz, 5 Hz, 1 Hz, 0.5 Hz, and 0.1 Hz; and the loading waveform was an offset sine wave. The specimen diameter was 100 mm, and the height was 150 mm.

3 Results and discussion

3.1 Property of aged and reclaimed asphalt

In this work, 70# neat asphalt was subjected to three times aging and regeneration treatments, and the resulting penetration and softening point data are presented in Figure 3. Penetration serves as a crucial indicator for evaluating asphalt consistency—i.e., the hardness and softness of asphalt at ambient temperature—as well as its viscosity. It is capable of characterizing the ability of asphalt to resist penetration by a needle-shaped object under specified conditions. Figure 3 shows that the penetration of asphalt decreased to varying degrees after each aging process, while it significantly rebounded after the addition of the asphalt rejuvenator. The first aging caused a decrease in the penetration by 54.6%, and it recovered by 71.6% after adding 8% rejuvenator; the second aging led to a 37.6% decrease in the penetration compared to that after the first regeneration, and it recovered by 56.9% after re-adding 8% rejuvenator; the third aging resulted in a 32.5% decrease in the penetration compared to that after the second regeneration, and it increased by 56.8% compared to that after the third aging when 8% rejuvenator was added. In addition, after the application of three aging and regeneration cycles, the penetration of asphalt recovered to 77.8%, 76.2%, and 80.7% of that before aging, respectively. This result indicates that the asphalt rejuvenator has a significant effect on restoring the performance of aged asphalt, and it can still maintain a high level of performance recovery even after multiple aging-regeneration cycles.

The softening point is an important indicator for evaluating the temperature susceptibility (i.e., high-temperature performance) of asphalt, which characterizes the temperature threshold at which asphalt materials transform from a solid or semi-solid state to a viscous fluid state when heated. As shown in Figure 3, the softening point increased to varying degrees after each aging process, while it tended to decrease after the addition of the asphalt rejuvenator. The first aging caused the softening point to increase by 17%, and it decreased by 5.2% after adding 8% rejuvenator. The second aging led to a 7.7% increase in the softening point compared to that after the first regeneration, and it decreased by 8.7% after re-adding 8% rejuvenator; the third aging resulted in a 13.1% increase in the softening point compared to that after the second regeneration, and it decreased by 6.8% compared to that after the third aging when 8% rejuvenator was added. In addition, after the implementation of three aging and regeneration cycles, the softening point of asphalt recovered to 110.9%, 109.2%, and 115% of that before aging, respectively.

The above-mentioned changes can be explained considering that during the aging process, light components in asphalt (such as aromatics and saturates) undergo oxidation and polymerization reactions, generating more macromolecular resins and asphaltenes (O'Connell, et al., 2024). These polymer substances cross-link with each other to form a denser spatial network structure, which increases the viscosity of asphalt and reduces its fluidity. As a result, the penetration decreases and the softening point significantly rises, reflecting the enhanced high-temperature performance of the asphalt rejuvenators. The latter are also rich in low-molecular substances such as aromatics, which can penetrate into the aged asphalt, dissolve the over-polymerized macromolecular structures, dilute the concentration of asphaltenes, and reduce the cohesion between molecules. This not only restores the fluidity of the asphalt but also supplements the light components lost during the aging process, enhancing the movement ability of the asphalt molecular segments. Consequently, the penetration increases and the softening point decreases.

Figure 4 shows the changes in the ductility and kinematic viscosity of 70# neat asphalt after applying three times aging and regeneration cycles. Ductility is an important indicator for evaluating the plasticity of asphalt, specifically referring to the length of asphalt material when stretched to fracture under specified temperature and stretching speed. It reflects the deformation ability and ductility of asphalt under stress loads. As can be seen from the figure, the ductility of asphalt significantly decreased after each aging process, while it showed an increasing trend after the addition of the asphalt rejuvenator. The first aging caused the ductility to decrease by 80.2%, and it recovered by 232.1% after adding 8% rejuvenator. The second aging led to a 64.5% decrease in ductility compared to that after the first regeneration, and it recovered by 135.6% after re-adding 8% rejuvenator. The third aging resulted in a 70.4% decrease in ductility compared to that after the second regeneration, and it increased by 162% compared to that after the third aging when 8% rejuvenator was added. In addition, after the application of three aging and regeneration cycles, the ductility of asphalt recovered to 65.6%, 54.9%, and 42.5% of that before aging, respectively.

Figure 4

Figure 4. Ductility and rotation viscosity of the aged and reclaimed asphalt.

Kinematic viscosity refers to the magnitude of internal frictional resistance when asphalt flows under the action of gravity at a certain temperature. Its value can be defined as the ratio of dynamic viscosity to the density of asphalt at the same temperature. A higher viscosity indicates that the asphalt is more viscous. This effect induces a greater resistance when flowing, and consequently the fluidity is deteriorated. As shown in Figure 4, the kinematic viscosity of asphalt increased to varying degrees after each aging process, while it tended to decrease after the addition of the asphalt rejuvenator. The first aging caused the kinematic viscosity to increase by 65.5%, and it decreased by 24.9% after adding 8% rejuvenator. The second aging led to a 31% increase in kinematic viscosity compared to that after the first regeneration, and it decreased by 29.3% after re-adding 8% rejuvenator. The third aging resulted in a 58.6% increase in the kinematic viscosity compared to that after the second regeneration, and it decreased by 26% compared to that after the third aging when 8% rejuvenator was added. In addition, after the application of three aging and regeneration cycles, the kinematic viscosity of asphalt recovered to 124.3%, 115.1%, and 135.1% of that before aging, respectively.

The reasons for these effects lie in the influence of aging and regeneration on the chemical composition and molecular structure of asphalt. During the aging process of asphalt, light components (such as oil and resin) are gradually converted into gums and asphaltenes, leading to an increase in the asphaltene content. As a result, the intermolecular forces are enhanced, and the asphalt becomes hard and brittle, which is manifested as a decrease in ductility and an increase in kinematic viscosity. After adding asphalt rejuvenator, its rich light components can dissolve the macromolecular asphaltenes in the aged asphalt, dilute the excessively high viscosity, enhance the flexibility of asphalt molecular chains, and improve fluidity. Thereby, the ductility is increased and the kinematic viscosity is reduced (Liu M. F et al., 2024). However, due to the irreversibility of chemical changes during aging, although the asphalt performance was recovered to some extent after each aging and regeneration cycle, it still cannot be completely returned to the state before aging, resulting in a gradual decrease in the recovery rate of ductility and kinematic viscosity.

Figure 5 shows the changes in the visco-toughness and toughness of asphalt after the application of three aging and regeneration cycles. Asphalt visco-toughness and toughness characterize the material’s deformation ability, energy absorption characteristics, and viscoelastic balance under stress by quantifying the synergistic effect of its viscosity and toughness. As can be seen from Figure 5, the visco-toughness of asphalt increased by 13.1% after the first aging, and then decreased by 8.1% after adding 8% rejuvenator. After the second aging, the visco-toughness of asphalt increased by 29.6%, and decreased by 1% after adding 8% rejuvenator. After the third aging, the visco-toughness of asphalt decreased by 15.6%, and further decreased by 3.1% after adding 8% rejuvenator. In addition, after the implementation of three aging and regeneration cycles, the visco-toughness recovered to 104%, 121.3%, and 105.6% of that before aging, respectively.

Figure 5

Figure 5. Viscosity toughness and toughness of aged and reclaimed asphalt.

The toughness of asphalt decreased by 22.7% after the first aging, and increased by 4% after adding 8% rejuvenator. After the second aging, the toughness of asphalt increased by 0.9%, and further increased by 4.4% after adding 8% rejuvenator. After the third aging, the toughness of asphalt decreased by 18.4%, and increased by 7.5% after adding 8% rejuvenator. In addition, after the application of three aging and regeneration cycles, the toughness of asphalt recovered to 80.4%, 84.6%, and 74.2% of that before aging, respectively.

The irregular changes in the visco-toughness and toughness of asphalt after multiple aging and regeneration cycles mainly stem from the complexity and interaction of the aging and regeneration processes. During multiple aging, an excessive loss of light components leads to a decrease in visco-toughness and toughness (Guo, et al., 2024). However, the effect of rejuvenator varies with the degree of aging, and its repair ability for deeply aged asphalt is limited. In addition, visco-toughness and toughness are dominated by different structural factors: visco-toughness depends on molecular cross-linking and viscosity, while toughness is determined by the flexibility of molecular chains. Their responses during aging and regeneration are not synchronized. These factors collectively result in the irregular characteristics of the asphalt performance changes.

3.2 Performance of the asphalt mixture

In this work, the road properties of the asphalt mixtures after three regenerations times, such as high-temperature performance, low-temperature performance, water stability, and dynamic modulus, were thoroughly investigated.

3.2.1 High-temperature performance

3.2.1.1 Rutting test

As an important method to characterize the deformation resistance of asphalt mixtures, the rutting test was conducted to measure the dynamic stability and deformation of unaged, three-time aged, and regenerated asphalt mixtures, with the results shown in Figure 6. As can be seen, with the increase in the number of regenerations, the dynamic stability of the asphalt mixture gradually increased, while the deformation accordingly decreased. A good positive linear correlation between the number of the aging-regeneration cycles and dynamic stability, combined with a good negative linear correlation with the deformation at 45 min and 60 min was observed. Compared with unaged specimens, after the application of three aging-regeneration cycles, the 45-min deformation of the asphalt mixture decreased to 90.1%, 89.5%, and 88.1% of that of the unaged mixture, respectively; the 60-min deformation decreased to 86.5%, 85.2%, and 83.8%, respectively; and the dynamic stability reached 160.2%, 176.3%, and 183.8% of that of the unaged mixture, respectively.

Figure 6

Figure 6. Dynamic stability and deformation of the asphalt mixture after three recycling.

The decrease in the deformation in conjunction with the initial increase and the subsequent decrease in the dynamic stability of the asphalt mixture after three aging-regeneration cycles are mainly due to the comprehensive effect of aging and regeneration on asphalt performance. During the aging process, light components of asphalt volatilize, oxidize, and polymerize, leading to an increase in asphaltene content and a transformation of the colloidal structure to a gel type. This result increases the viscosity of asphalt and enhances its deformation resistance, thereby reducing the deformation of the mixture. On the other hand, the rejuvenator supplements the light components lost during the aging process, reshapes the asphalt colloidal structure, and restores its viscoelastic properties to a certain extent (Yu J. M et al., 2024). During the first two aging-regeneration cycles, the increase in the stiffness caused by aging plays a dominant role, and the rejuvenator moderately restores toughness, thus increasing the dynamic stability. However, after the third aging cycle, the asphalt structure was severely deteriorated, and the rejuvenator was not able to fully repair it, possibly resulting in ‘over-regeneration’ or ‘insufficient regeneration’. This result leads to an imbalance in the colloidal structure and a decrease in the bonding force at the aggregate interface, ultimately causing a reduction in dynamic stability.

3.2.1.2 Penetration strength test

Penetration strength is an indicator for evaluating the deformation resistance of asphalt mixtures (El-Gabry, et al., 2025). The penetration depth and penetration strength of the asphalt mixtures after three aging and regeneration cycles are shown in Figure 7. As can be seen, there is a good linear relationship between the times of aging-regeneration cycles of the asphalt mixtures and both the penetration depth and penetration strength. With the increase in the number of the aging-regeneration cycles, the penetration depth of the asphalt mixture gradually decreased, while the penetration strength gradually increased. Compared with the unaged specimens, after the application of three aging-regeneration cycles, the penetration depth of the asphalt mixture decreased to 83.7%, 79.2%, and 68.5% of that of the unaged mixture, respectively, and the penetration strength reached 127.5%, 135%, and 150% of that of the unaged mixture, respectively.

Figure 7

Figure 7. Penetration depth and penetration strength of the asphalt mixture after three recycling.

The rejuvenator added during the regeneration process can effectively restore the viscoelastic properties of aged asphalt, supplement the light components lost due to aging, and improve the viscosity and stiffness of the asphalt mortar. Thereby, the integrity and deformation resistance of the internal structure of the asphalt mixture can be enhanced. At the same time, the aging-regeneration process may promote the optimization of the interaction between the asphalt and mineral aggregate interfaces, forming a more stable spatial network structure. This yields a further improvement in the ability of the mixture to resist the intrusion of external loads, manifested as a decrease in the penetration depth and a significant increase in the penetration strength.

3.2.2 Low-temperature performance

3.2.2.1 Beam bending test at low temperature

The low-temperature performance of multiple recycled asphalt mixtures can be evaluated by measuring the flexural-tensile strength and bending stiffness modulus of the mixture using the low-temperature beam bending method (Bai, et al., 2024; Jin, et al., 2024). In this work, the asphalt mixture was subjected to three aging and regeneration cycles, and its flexural-tensile strength and bending stiffness modulus were tested, with the results shown in Figure 8. As can be seen, there is a linear correlation between the number of the aging and regeneration cycles of the asphalt mixture and both flexural-tensile strength and bending stiffness modulus. With the increase in the number of the aging-regeneration cycles, the flexural-tensile strength and bending stiffness modulus accordingly increased. Compared with the unaged specimens, after the application of three times aging-regeneration cycles, the flexural-tensile strength of the asphalt mixture increased to 106.2%, 112.3%, and 121% of that of the unaged mixture, respectively, and the bending stiffness modulus increased to 110.7%, 117.9%, and 146.4% of that of the unaged mixture, respectively.

Figure 8

Figure 8. Bending tensile strength and bending stiffness modules of the asphalt mixture after three recycling.

During the aging process of asphalt mixtures, the content of asphaltenes increased, and the components were transformed into macromolecules, making the asphalt hard and brittle and increasing its viscosity. This effect improved the overall rigidity of the mixture, resulting in an increase in flexural-tensile strength and bending stiffness modulus. In the regeneration process, the rejuvenator can supplement the light components missing in the aged asphalt, improve the rheological properties of the asphalt, and restore part of its flexibility and adhesiveness. At the same time, the regeneration process plays a certain role in repairing and optimizing the internal structure of the mixture, making the internal structure of the mixture more compact and stable, and further enhancing its ability to resist deformation and damage. The alternating effect of aging and regeneration promotes the continuous adjustment and optimization of the internal structure of the mixture, ultimately leading to an upward trend in flexural-tensile strength and bending stiffness modulus with the increase in the number of the aging-regeneration cycles (Wang, et al., 2024).

3.2.2.2 Semi-circular bending test (SCB)

The SCB test can be used to evaluate the cracking performance of asphalt mixtures (Fan, et al., 2025). The fracture energy and tensile strength of asphalt mixtures after three aging-regeneration cycles are shown in Figure 9. As can be seen, with the increase of times of aging-regeneration cycles of the asphalt mixture, both the fracture energy and tensile strength gradually decreased. In addition, a linear relationship between the number of the aging-regeneration cycles and the fracture energy and tensile strength of the asphalt mixture was extracted. Compared with unaged specimens, after the application of three aging-regeneration cycles, the flexural-tensile strength of the asphalt mixture decreased to 98.6%, 76.4%, and 68.2% of that of the unaged mixture, respectively, and the bending stiffness modulus decreased to 92.4%, 80.5%, and 77.3% of that of the unaged mixture, respectively.

Figure 9

Figure 9. Fracture energy and tensile strength of the asphalt mixture after three recycling.

During the repeated aging process of asphalt mixtures, the light components in the asphalt continuously volatilize, the aging degree intensifies, the asphalt becomes hard and brittle, and internal microcracks gradually expand and multiply, resulting in a decrease in the toughness of the mixture and a weakening of its ability to resist deformation. Although the rejuvenator can supplement some light components, after the application of multiple aging-regeneration cycles, the internal structure of the mixture was damaged, the regeneration effect gradually weakened, and it was difficult to fully restore the original performance of the asphalt mixture. Moreover, with the increase of times of the aging-regeneration cycles, the structural damage continues to accumulate, leading to a gradual decrease in the fracture energy and tensile strength of the asphalt mixture.

3.2.3 Water stability

The water stability of the asphalt mixtures refers to their ability to resist damage such as asphalt film stripping, particle loss, loosening, and potholes caused by water erosion (He, et al., 2024; Xiao, et al., 2024). In this work, freeze-thaw splitting tests and immersed Marshall tests were conducted on asphalt mixtures after the implementation of three times of aging-regeneration cycles, and the results are shown in Figure 10. With the increase in the number of the aging-regeneration cycles of the asphalt mixture, both the residual stability and freeze-thaw splitting strength ratio showed irregular changes of first decreasing and then slowly increasing. After three aging-regeneration cycles, the residual stability and freeze-thaw splitting strength ratio of the asphalt mixture stabilized at approximately 90% and 77%, respectively. Compared with the unaged specimens, after three aging-regeneration cycles, the residual stability of the asphalt mixture increased to 91%, 91.7%, and 92% of that of the unaged mixture, respectively, and the freeze-thaw splitting strength ratio increased to 95.7%, 93.7%, and 97.4% of that of the unaged mixture, respectively.

Figure 10

Figure 10. Residual stability ratio and freeze thaw splitting strength ratio of the asphalt mixture after three recycling.

In the early stage of aging, the volatilization and oxidative condensation of the light components in asphalt lead to weakened adhesion, increased air voids, and decreased water stability. During the first regeneration, there is a lag effect in the performance recovery of the aged asphalt by the rejuvenator. With the increase in the number of aging-regeneration cycles, the rejuvenator continuously supplements the light components missing in the asphalt, enhances the chemical adsorption between the asphalt and aggregates, and improves the fluidity and wettability of asphalt (Kumbargeri, et al., 2025). In addition, the aging-regeneration cycle promotes asphalt to reach a dynamic balance between the oxidation and reduction mechanism, forming a more stable microstructure, which makes the chemical adhesion and structural stability exceed the original state, thus achieving an improvement in water stability.

3.2.4 Dynamic viscoelastic properties of AC and RAC

Dynamic viscoelastic properties refer to the comprehensive mechanical behaviour of the materials that simultaneously exhibit elasticity (energy storage) and viscosity (energy dissipation) under alternating loads (such as periodic stress or strain). In this work, the dynamic modulus and phase angle of multiple recycled asphalt mixtures were tested, and the effects of the test temperature and load frequency on the dynamic viscoelastic properties of recycled mixtures were analysed (Liang, et al., 2025).

3.2.4.1 Dynamic modulus

The variation of the dynamic modulus of asphalt mixtures with the loading frequency under different temperature conditions is shown in Figure 11. As can be seen, for both the new and recycled asphalt mixtures, as the test temperature increased, the dynamic modulus of the recycled mixtures and the new materials increased with the frequency. Nonetheless, the increased amplitude gradually decreased, which is independent of the number of regenerations. It is evident that, similar to new materials, the multiple recycled mixtures exhibit sufficient viscous behaviour under high-temperature conditions, and their dynamic modulus is less affected by the loading frequency. In addition, after the first aging and regeneration, the dynamic modulus of the asphalt mixture was basically close to that of the new material; when the number of aging-regeneration cycles exceeded two, under the same test temperature and loading frequency conditions, its dynamic modulus showed an obvious upward trend, and the dynamic modulus at 4 °C was higher than that at 20 °C and 40 °C.

Figure 11

Figure 11. Variation of the dynamic modulus of multiply recycled mixture with different loading frequencies. (a) Virgin asphalt mixture. (b) Recycled mixture for first cycle. (c) Recycled mixture for second cycle. (d) Recycled mixture for third cycle.

3.2.4.2 Phase angle

The phase angle describes the relationship between the elasticity and viscosity exhibited by a material in response to external stimuli, characterizing the viscoelastic deformation process of asphalt mixtures. Figure 12 illustrates the influence of the loading frequency on the phase angle of asphalt mixtures subjected to multiple aging-regeneration cycles. As can be observed, the first recycled mixture and the new mixture showed the same temperature-dependent trend. At a temperature of 4 °C, both the multiple recycled mixtures and the new mixture exhibited an identical trend in phase angle, which gradually decreased as the load frequency increased. At 20 °C, with the increase in the load frequency, the phase angles of the first recycled mixture and the new mixture first increase and then decrease. At 40 °C, due to the high temperature, the polymer substances in the mixture exhibited significant viscosity; within a certain range of load frequencies, the phase angle increased as the load frequency rose. In addition, with the increase in the number of regenerations, the second and third recycled mixtures no longer exhibited a peak phase angle, and all their phase angles decreased as the load frequency increased.

Figure 12

Figure 12. Variation of the phase angle of the recycled mixture with different loading frequencies. (a) Virgin mixture. (b) Recycled mixture for first cycle. (c) Recycled mixture for second cycle. (d) Recycled mixture for third cycle.

4 Grey relational analysis

Grey relational analysis is used to evaluate the strength of influence between various factors by comparing the similarity of curve shapes between the reference and the comparison sequences. This method is mainly applicable to the correlation analysis of small-sample and poor-information data (Liu S. F et al., 2024). In this work, the pavement performance indicators of new asphalt mixtures and those subjected to three times aging-regeneration cycles were selected as the reference sequence, including Dynamic stability, Penetration strength, Bending stiffness modulus, Tensile strength, Residual stability ratio, Freeze-thaw splitting strength ratio, Dynamic modulus (10 Hz, 20 °C), and Phase angle (10 Hz, 20 °C). Meanwhile, the performance indicators of the new neat asphalt and asphalt after three aging-regeneration cycles were chosen as the comparison sequence, including Penetration, Softening point, Ductility, Rotational viscosity, Viscosity toughness, and Toughness.

The steps of grey relational analysis are as follows: first, dimensionless processing on the reference sequence and the comparison sequence were conducted (see Formulas 1, 2); then, the absolute difference between the reference sequence and the comparison sequence at each moment was calculated (see Formula 3); finally, the correlation degree between the reference sequence and the comparison sequence was calculated (see Formula 4). A larger correlation coefficient indicates a stronger correlation between the reference sequence and the comparison sequence (Zheng, et al., 2025; Hu and Xie, 2025; Liu, et al., 2025).

$X_0=\left\{\frac{X_0\left(1\right)}{\overline{X_0}},\frac{X_0\left(2\right)}{\overline{X_0}},\cdots ,\frac{X_0\left(\mathrm{n}\right)}{\overline{X_0}}\right\}(1)$

$X_i=\left\{\frac{X_i\left(1\right)}{\overline{X_i}},\frac{X_i\left(2\right)}{\overline{X_i}},\cdots ,\frac{X_i\left(\mathrm{n}\right)}{\overline{X_i}}\right\}(2)$

In the formula, X₀ is the reference sequence, $X_0=\left\{x_0\left(1\right),x_0\left(2\right),\cdots ,x_0\left(n\right)\right\}$, X_i is the comparison sequence, $X_i=\left\{x_i\left(1\right),x_i\left(2\right),\cdots ,x_i\left(n\right)\right\}$ (i = 1, 2, … … , m); $\overline{x_0}=\frac{1}{n}{\displaystyle {\sum }_{k=1}^n}{X}_0\left(k\right)$; $\overline{x_i}=\frac{1}{n}{\displaystyle {\sum }_{k=1}^n}{x}_i\left(k\right)$.

${\xi }_i\left(k\right)=\frac{\underset{i}{\min }\left({\Delta }_i\left(\min \right)\right)+0.5\underset{i}{\max }\left({\Delta }_i\left(\max \right)\right)}{\left|x_0\left(k\right)-x_i\left(k\right)\right|+0.5\underset{i}{\max }\left({\Delta }_i\left(\max \right)\right)}(3)$

$r_i=\frac{1}{N}{\displaystyle \sum _{k=1}^N}{\xi }_i\left(k\right)(4)$

The correlation analysis results of this work are shown in Table 4. As can be seen, among the high-temperature performance indicators of asphalt mixtures, the penetration strength exhibited a stronger correlation with asphalt performance indicators. Specifically, the dynamic stability had a stronger correlation with rotational viscosity, and the penetration strength presented a stronger correlation with softening point, rotational viscosity, and viscosity toughness. Among the low-temperature performance of asphalt mixtures, the bending stiffness modulus had a stronger correlation with the asphalt performance indicators. Among them, the bending stiffness modulus exhibited a higher correlation with the softening point, rotational viscosity, and viscosity toughness of asphalt, and the tensile strength had a higher correlation with penetration and toughness. Among the water stability performance of the asphalt mixtures, the freeze-thaw splitting strength ratio presented a stronger correlation with asphalt performance indicators. Specifically, the freeze-thaw splitting strength ratio had a higher correlation with penetration, softening point, viscosity toughness, and toughness, and the residual stability ratio had a higher correlation with penetration, viscosity toughness, and toughness. Among the dynamic viscoelastic properties of asphalt mixtures, the dynamic modulus had a higher correlation with the softening point, rotational viscosity, and viscosity toughness of asphalt. Moreover, the phase angle exhibited a higher correlation with asphalt penetration, softening point, viscosity toughness, and toughness.

Table 4

Table 4. Grey correlation.

The comprehensive analysis of the correlation between the asphalt mixture and asphalt performance indicators showed that ductility had the lowest correlation with the road properties indicators of asphalt mixtures. On the contrary, penetration, softening point, rotational viscosity, viscosity toughness, and toughness exhibited relatively higher correlations with the performance indicators of the asphalt mixtures.

5 Genetic algorithm-back propagation (GA-BP) neural network

The results from the grey relational analysis in Section 4 indicate that in this work, asphalt indicators such as penetration, softening point, rotational viscosity, viscosity toughness, and toughness can be used to predict the road properties of asphalt mixtures.

A BP neural network is a supervised learning model based on a multi-layer feed forward structure, which mainly consists of an input layer, hidden layers (which can be multiple), and an output layer. The input layer receives raw feature data, the hidden layers extract abstract features of the data through non-linear activation functions, and the output layer outputs prediction results (such as classification labels or regression values) (Wu, et al., 2025; Deng, et al., 2024).

The GA (Genetic Algorithm) has global optimization capabilities, which can make up for the defects of BP neural networks, such as being prone to falling into local optimal solutions and having a slow convergence speed (Ardelean and Udrescu, 2024; Sohrabi, et al., 2024; Solar, et al., 2024). The combination of the two, forming GA-BP (Figure 13), conducts a global search for better initial parameter combinations in the solution space, and then uses these optimized parameters as the initial values of the BP neural network for traditional back propagation training. This makes it easier for the neural network to find the global optimal solution during the training process, thereby ensuring that the error of the output values of the trained network meets the accuracy requirements (Ye, et al., 2024; Guo, et al., 2025). The specific steps of its modeling are shown in Formulas 5–11 (Yang, et al., 2024; Cui, et al., 2025; Zhang, et al., 2025):

Figure 13

Figure 13. GA-BP neural network algorithm flow.

5.1 Data normalization

${\mathrm{x}}^{\prime }=\frac{\mathrm{x}-{\mathrm{x}}_{\min }}{{\mathrm{x}}_{\max }-{\mathrm{x}}_{\min }}(5)$

In the formula, ${\mathrm{x}}^{\prime }$ represents the normalized data, x denotes the original data, ${\mathrm{x}}_{\max }$ is the maximum value, and ${\mathrm{x}}_{\min }$ is the minimum value.

5.2 The fitness function in the GA (Genetic Algorithm)

$\text{fitness}=\frac{1}{1+\text{MSE}}(6)$

$\text{MSE}=\frac{1}{\mathrm{p}}{\displaystyle \sum _{\mathrm{t}=1}^{\mathrm{p}}}{\left({\mathrm{y}}_{\mathrm{a}}-{\mathrm{y}}_{\mathrm{t}}\right)}^2(7)$

In the formula, fitness is the fitness value, MSE is the mean square error, p is the number of samples, ${\mathrm{y}}_{\mathrm{a}}$ is the predicted value, and ${\mathrm{y}}_{\mathrm{t}}$ is the true value.

5.3 Forward propagation in the BP neural network

${\mathrm{a}}_1=\mathrm{\sigma }\left({\mathrm{W}}_1\mathrm{x}+{\mathrm{b}}_1\right)(8)$

$\mathrm{y}=\mathrm{\sigma }\left({\mathrm{W}}_2{\mathrm{a}}_1+{\mathrm{b}}_2\right)(9)$

In the formula, ${\mathrm{W}}_1$ and ${\mathrm{W}}_2$ are the weights from the input layer to the hidden layer and from the hidden layer to the output layer, respectively; ${\mathrm{b}}_1$ and ${\mathrm{b}}_2$ are the corresponding biases; $\mathrm{\sigma }$ (·) is the activation function; ${\mathrm{a}}_1$ is the output of the hidden layer; and y is the predicted output of the network.

5.4 Back propagation is the core process of the BP neural network leaning

$\mathrm{W}=\mathrm{W}-\mathrm{\eta }·\frac{\mathrm{\partial }\mathrm{L}}{\mathrm{\partial }\mathrm{W}}(10)$

$\mathrm{b}=\mathrm{b}-\mathrm{\eta }·\frac{\mathrm{\partial }\mathrm{L}}{\mathrm{\partial }\mathrm{b}}(11)$

In the formula, $\mathrm{\eta }$ is the learning rate, L is the loss function, and $\frac{\mathrm{\partial }\mathrm{L}}{\mathrm{\partial }\mathrm{W}}$ and $\frac{\mathrm{\partial }\mathrm{L}}{\mathrm{\partial }\mathrm{b}}$ are the gradients of the loss with respect to the weights and biases, respectively.

Based on the GA-BP neural network algorithm, various asphalt indicators, such as penetration, softening point, rotational viscosity, viscosity toughness, and toughness were used to predict the road properties of asphalt mixtures. The results are shown in Figure 14, and the relative errors are presented in Table 5. As can be seen from Figures 14–17 and Table 5, the relative errors of the prediction of high and low temperature rheological properties of high polymer modified asphalt by functional groups established by the GA-BP neural network model were small, the prediction effect was good, and it had certain reference significance.

Table 5

Table 5. The mean relative errors of the predicted indicators.

Figure 14

Figure 14. The predicted and measured values of the high-temperature stability of asphalt mixtures. (a) Dynamic stability. (b) Penetration strength.

Figure 15

Figure 15. The comparison between the predicted and actual values of the low-temperature crack resistance of asphalt mixtures. (a) Bending stiffness modules. (b) Tensile strength.

Figure 16

Figure 16. The comparison between the predicted and actual values of the water stability of asphalt mixtures. (a) Residual stability ratio. (b) Freeze thaw splitting strength ratio.

Figure 17

Figure 17. The comparison between the predicted and actual values of the dynamic viscoelastic properties of asphalt mixtures. (a) Dynamic modulus. (b) Phase angle.

6 Conclusion

The variation patterns of asphalt performance indicators (including penetration, softening point, ductility, rotational viscosity, viscosity toughness, and toughness) were systematically investigated in this work by applying three cycles of aging and regeneration of asphalt. The changes in high-temperature stability, low-temperature crack resistance, water stability, and dynamic viscoelastic properties of asphalt mixtures after three aging-regeneration cycles were then studied using various methods, such as rutting test, penetration test, low-temperature beam bending test, semi-circular bending test, freeze-thaw splitting strength test, residual stability test, and dynamic modulus test. Second, a grey relational analysis was used to select asphalt performance indicators with high correlation with the road properties of asphalt mixtures. Finally, a GA-BP neural network was employed to establish a prediction model between the asphalt performance indicators and the road properties of asphalt mixtures. The following conclusions can be drawn:

1. After the application of three cycles of aging and regeneration, the key performance indicators of 70# neat asphalt showed regular changes with significant recovery effects. Specifically, the penetration decreased by 54.6%, 37.6%, and 32.5% after each aging cycle, respectively, and recovered by 71.6%, 56.9%, and 56.8% after adding 8%rejuvenator, eventually restoring to 77.8%, 76.2%, and 80.7% of the unaged level. The softening point increased by 17%, 7.7%, and 13.1% after each aging cycle, respectively, and decreased by 5.2%, 8.7%, and 6.8% after regeneration, finally reaching 110.9%, 109.2%, and 115% of the unaged level. These results indicate that the rejuvenator can effectively reverse the deterioration of the asphalt performance caused by aging, and maintain a high recovery level even after three cycles.

2. The road properties of the asphalt mixtures showed differentiated evolution after three aging-regeneration cycles. In terms of high-temperature stability, the dynamic stability increased to 160.2%, 176.3%, and 183.8% of the unaged level with the increase in the regeneration cycles, while the penetration strength increased to 127.5%, 135%, and 150%. For low-temperature crack resistance, the flexural-tensile strength increased to 106.2%, 112.3%, and 121%, but the fracture energy and tensile strength gradually decreased, reaching 68.2% and 77.3% of the unaged level after the third regeneration, respectively. The water stability remained overall stable, with the residual stability and freeze-thaw splitting strength ratio finally stabilizing at approximately 92% and 97.4%, respectively.

3. The dynamic viscoelastic properties of the asphalt mixtures after multiple regenerations showed specific variation patterns. When the number of regenerations exceeded two, the dynamic modulus increased significantly at the same temperature and load frequency, and the dynamic modulus at 4 °C was significantly higher than that at 20 °C and 40 °C. The phase angle was significantly affected by the temperature and frequency; for example, at 40 °C, the phase angle of the mixture after the first regeneration increased with the increase in frequency, while the phase angles of the mixtures after the second and third regenerations continued to decrease with the increase in frequency without showing peaks. These results indicate that although three aging-regeneration cycles changed the viscoelastic balance of the mixture, it did not lose the dynamic response characteristics similar to those of new mixtures.

4. The grey relational analysis between the asphalt mixture and asphalt performance indicators showed that ductility had the lowest correlation with the road properties indicators of asphalt mixtures. On the contrary, penetration, softening point, rotational viscosity, viscosity toughness, and toughness exhibited relatively higher correlations with the performance indicators of the asphalt mixtures.

5. The GA-BP neural network established in this work can be used to effectively build prediction models for the high-temperature, low-temperature, water stability, and dynamic viscoelastic properties of asphalt mixtures after multiple aging-regeneration cycles based on asphalt indicators, with small relative errors. Our approach can be used as a valuable reference for the research on the material genome of asphalt and asphalt mixtures.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

RL: Methodology, Writing – original draft, Writing – review and editing. JW: Conceptualization, Funding acquisition, Investigation, Writing – review and editing. JX: Data curation, Investigation, Writing – review and editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This project was jointly supported by the Special Program for Basic Scientific Research Operating Funds of Central Government-level Public Welfare Research Institutes (Grant No. 2025-9013A).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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