Pavement Performance Research Articles

Innovative use of nanomaterials for improving performance of asphalt binder and asphaltic concrete: a state-of-the-art review

ABSTRACT Rising costs of constructing and maintaining asphaltic concrete pavements present a challenge requiring an alternative solution. Nanomaterials can be cost-effectively integrated with asphalt binder to provide beneficial effects for asphaltic concrete mixture. This paper investigated seven different types of nanomaterials, namely nanoclay, carbon nanotube, nanosilica, nano-titanium dioxide, nano-zinc oxide, graphene oxide, and carbon nanofiber by examining their production methods, benefits, applications, and limitations based on the data available in published literature. Challenges and limitations discussed include economic, production, and blending problems, some of which are due to the lack of research on the topic. This study provides a framework from which the pavement engineering community can conduct experimental research on nanomaterials for applications in asphaltic concrete pavements. The review of previous studies reveals that new asphalt binders and asphaltic concrete mixtures incorporating nanomaterials can be developed for improved performance of flexible pavements. It is expected that further research can be devoted to overcoming the current challenges faced by aging transportation infrastructure through use of nanomaterials in asphalt binder and asphaltic concrete. Above all, research gaps in the present state of knowledge have been identified and certain recommendations are given for future investigations.

Development of superpave asphalt binder specifications to meet climate conditions in the UAE

ABSTRACT Asphalt binder's performance is highly sensitive to temperature fluctuations and climatic conditions. The selection of an inappropriate asphalt binder leads to considerable damage and impacts roads' longevity. The current selection practice of asphalt binder in the United Arab Emirates (UAE) is mainly according to the penetration grading system, which does not consider local temperature conditions. The Superpave Performance Grade (PG) system considers extreme pavement temperatures to define appropriate asphalt binder PG. This study aimed at developing the Superpave asphalt binder PG map for the UAE. The Strategic Highway Research Program (SHRP) and Long-Term Pavement Performance Program (LTPP) Superpave models were used to calculate the pavement temperatures employing air temperature records collected from 20 weather stations across the UAE. The result of the Superpave PG map highlights three distinct asphalt binder grades at 98% reliability: PG 76-10, PG 70-10, and PG 64-10. The PG 76 -10 asphalt binder is the most prevalent PG, covering more than 85% of the UAE map. However, the current construction practice utilizes penetration grades 40/50 and 60/70, through both asphalt binders are equivalent to PG 64-xx. This necessitates the use of different modifier technologies to achieve the Superpave requirements of PG 76-10 and PG 70-10.

UPDAPS-Flood: a mechanistic-empirical flexible pavement analysis tool to evaluate the effect of flooding events on flexible pavement performance

ABSTRACT Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels.

Imporved model for pavement performance prediction based on recurrent neural network using LTPP database

Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments, enabling the achievement of better pavement performance with limited financial resources. However, due to the intricate influence of numerous factors on pavement performance deterioration, improving the accuracy of pavement performance prediction poses a challenge for conventional models. Therefore, the aim of this study is to establish a machine learning-based model pavement performance prediction model. First, this study considers five factors that affect pavement performance, including pavement initial performance indicators, traffic loads, weather, pavement structure, and maintenance measures, and identifies 15 specific indicators that affect pavement performance based on these five factors. Then, based on the Long-Term Pavement Performance (LTPP) database, the study screens and summarizes these indicators, obtaining 2464 high-quality pavement performance data for Pavement Conditions Index (PCI) prediction and 3238 high-quality pavement performance data for IRI prediction. Finally, three distinct prediction models are established, namely, the Fully Connected Neural Network (FCNN) model, the Long Short-Term Memory (LSTM) model, and the combined LSTM-Attention model. The study shows that the LSTM-Attention model performs significantly better than the FCNN and LSTM models, with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI. The innovation of this paper is that the authors have introduced the Attention mechanism on the basic of LSTM model, which makes the fitting accuracy of the prediction model further improved.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

The investigation of rheological properties of rejuvenated asphalt binder with waste cooking oil as rejuvenator

Asphalt binder plays a significant role in the performance of asphalt pavement. However, due to the limited availability of crude oil and the increasing cost of asphalt, alternatives are being investigated. Reclaimed asphalt pavements (RAP) provide a sustainable solution for the increasing demand of asphalt. However, the amount of RAP which can be utilized in asphalt mixtures is limited due to production and performance issues. Incorporation of high RAP content calls for rejuvenators, which rejuvenate the aged binder and increases the degree of blending. Among various rejuvenators, waste cooking oil (WCO) is gaining increased attention due to its availability and concern towards environmental pollution. When combined in proper proportions with RAP, WCO is a potential rejuvenator, which restores the properties of aged binder to that of unaged asphalt binder. This addresses two problems simultaneously and can greatly benefit the economy and environment. This study focuses on the viscoelastic and rheological properties of the rejuvenated binder with WCO and RAP. The rheological properties are evaluated using frequency sweep test by assessing the complex shear modulus and rutting parameter of different binders. The viscoelastic properties are studied using multiple stress creep recovery (MSCR) test. The data of the MSCR test is analysed with the Burger model to understand the viscoelastic properties of the rejuvenated RAP binder. For each of 75, 60 and 45% RAP, three different oil proportions are selected, and the optimum WCO content is identified. The study concludes that higher RAP content results in lower viscous strain and higher elastic strain, whereas after mixing of higher WCO, the viscous strain increases, and elastic strain decreases. Hence, it is crucial to mix WCO and RAP in the optimum ratio to obtain the desired rheological and viscoelastic properties.

Comparative Study on Selection of Suitable Pavement Performance Analysis Technique by Using FWD Measurement

Comparative Study on Selection of Suitable Pavement Performance Analysis Technique by Using FWD Measurement

Laboratory evaluation of synergistic blending with SBS-modified bitumen and rejuvenator to enhance the performance of recycled bitumen with a high content of RAP materials

Reclaimed asphalt pavement (RAP) stands as a pivotal construction material widely used in asphalt pavement rehabilitation. The prevailing focus and trajectory revolve around increasing the proportion and utility of RAP materials. However, the potential for temperature and fatigue cracking increases with the dosage of RAP materials. In this study, SBS modified bitumen (SBSMB) and four rejuvenators were combined synergistically with aged asphalt to enhance the performances of the recycled bitumen with high RAP content and consequently augment the value of the RAP material, all while ensuring compatibility between the SBSMB and aged base bitumen. Initially, the base bitumen was aged using a pressure aging vessel (PAV) test. Subsequently, a selection of virgin bitumen (VB), SBSMB, and four rejuvenators were utilized to revitalize the aged bitumen. A comprehensive battery of tests, encompassing conventional tests, temperature sweep, frequency sweep, multiple stress creep recovery (MSCR), bending beam rheometer (BBR), and linear amplitude sweep (LAS), were carried out to characterize the rheological properties. Finally, Fourier transform infrared spectroscopy (FTIR) was employed, along with the determination of polystyrene index (IPS) and polybutadiene index (IPB), to evaluate the compatibility between SBSMB and aged base bitumen. The results underscore that the incorporation of SBSMB and rejuvenator comprehensively enhances high-temperature performance and fatigue cracking resistance while effectively reinstating low-temperature cracking resistance. Furthermore, the values of IPS, IPB, and IPS/IPB exhibit an increase with the addition of a rejuvenator, indicating its potential to improve the compatibility between SBSMB and aged base bitumen. Thus, this research proposes a more cost-effective and environmentally sustainable approach to bolstering conventional asphalt pavement performance and grade.

Prediction of the fundamental viscoelasticity of asphalt mixtures using ML algorithms

The complex modulus (E*) and phase angle (δ) are fundamental viscoelastic (VE) properties of asphalt material, crucial for pavement performance analysis and modeling. This study presents a novel approach to predict the fundamental VE properties of asphalt mixtures using 20 study mixes with disparate mix design information including different Performance Graded (PG grade) binders, recycled asphalt (RAP) contents, with/without polymer modifications, and mix volumetric properties. By employing the XGBoost model for feature importance analysis, integrating the Bailey method to streamline gradation information, and utilizing master curve shape parameters for modeling, this study has effectively reduced data redundancy and dimensionality. Among four machine learning (ML) models compared, the Multi-Hidden-Layer Backpropagation Neural Network (Multi BP) model demonstrates superior applicability to the asphalt mixture dataset, providing accurate predictions for VE properties at any temperature and frequency combinations. Notably, this predictive model relies solely on the mixture design information as inputs, minimizing the need for laborious laboratory experiments. The developed model can be integrated with Mechanistic-Empirical (ME) design, predicting the variations in rutting and cracking performance of asphalt mixtures over the entire service life. This provides essential foundational input support for long-lasting pavement design.

Study on Basic Pavement Performance of High-Elasticity Asphalt Concrete.

In order to improve the basic pavement performance of high-elastic asphalt concrete filled in the expansion longitudinal joints of seamless bridges, rubber particles and polyester fibers were added to optimize the mix proportion of elastic asphalt concrete, and the optimal asphalt-aggregate ratio was determined. The influence of rubber particles and polyester fibers on the basic pavement performance of high-elastic asphalt concrete was studied. The results show that when the dosage of polyester fiber is not more than 0.6%, the optimal asphalt-aggregate ratio is 1:5, and when it exceeds 0.6%, the optimal asphalt-aggregate ratio is 1:4. The incorporation of rubber particles reduces the compressive strength of high-elastic asphalt concrete but enhances its high-temperature stability, fracture performance, and deformation recovery ability. The incorporation of polyester fibers improves its compressive strength, high-temperature stability, fracture performance, and deformation recovery ability. In addition, the incorporation of rubber granules and polyester fibers promotes the use of green building materials and provides strong support for sustainable building practices.

Evaluating the Anti-Skid Performance of Asphalt Pavements with Basalt and Limestone Composite Aggregates: Testing and Prediction

Evaluating the Anti-Skid Performance of Asphalt Pavements with Basalt and Limestone Composite Aggregates: Testing and Prediction

Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates

Highway state agencies incur significant budget savings through optimal allocation of pavement Maintenance, Rehabilitation, and Reconstruction (MR&R) activities. These activities require robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have been questionable in terms of efficiency and accuracy, are based on a limited number of explanatory variables, and are designated to predict short-term (up to five years) pavement conditions. Therefore, the goal of this study was to propose a machine learning-based technology that can predict the field performance by up to 11 years of Asphalt Concrete (AC) overlays placed on asphalt pavements in Southern states in the US based on key project conditions. The proposed technology resulted from assessing the prediction accuracy of machine learning algorithms, including Decision-Tree (DT), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and ensemble-learning method, in forecasting the Pavement Condition Index (PCI) as the pavement performance indicator. For each algorithm, six models were developed sequentially based on historical pavement condition data collected from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. The six models learned from 892 log miles of randomly placed AC overlay sections in Louisiana. The output of these models was the future PCI of AC overlays at a biannual rate from one to 11 years. The findings showed that XGBoost and ensemble learning showed similar performance during model training and were further evaluated using the testing dataset. During model testing, the ensemble learning method yielded higher prediction accuracy than other algorithms, with R2 values decreasing from 0.77 at age 1 to 0.67 at age 11, Root Mean Square Error (RMSE) values increasing from ±1.65 to ±4.74, and Mean Absolute Error (MAE) increasing from 1.24 to 4.59. The resulting models can be used by state agencies to replace traditional prediction techniques.

Diffusion mechanism explorations on the sustainable warm mix asphalt and synchronous rejuvenated SBS-modified asphalt binder using the free volume theory

The blending degree, dominated by the diffusion process between virgin and rejuvenated asphalt, profoundly affects the performance of recycled mixtures. However, research on the blending degree in Warm Mix Asphalt (WMA) containing Styrene-Butadiene-Styrene-modified asphalt (SBSMA) mixtures remains relatively underexplored despite its significant impact on pavement performance. To this concern, a predictive model for characterizing the diffusion behavior of the WMA modified SBSMA and synchronous rejuvenated SBSMA was developed based on the free volume theory, which was validated by the pulsed field gradient nuclear magnetic resonance test. Based on the predicted diffusion coefficients, the influences of WMA additives, rejuvenator types, and temperatures on diffusion behavior were systematically investigated. Results indicated that the diffusion coefficients were affected by the interaction of WMA additives, rejuvenator types, and temperatures. The presence of the WMA additive and the increase in diffusion temperature were able to improve the blending degree by increasing the diffusion coefficient. Rebalancing the unstable colloidal structure of SBSMA can enhance the diffusion coefficient of the WMA modified SBSMA twice than that of untreated one. After further reconstruction of small SBS fragments into the triblock copolymers, the blending between the WMA modified SBSMA and synchronous rejuvenated SBSMA was completed in the form of mutual diffusion, showing the highest diffusion coefficients (10−7).

Mechanistic Pavement Modeling of Typical Brazilian Pavements: Cohesive Zone Fracture Modeling and Continuum Damage Modeling

Pavement performance evaluation with respect to fatigue cracking has seen a major shift toward more rigorous mechanistic models that can capture the complex damage response of the mixtures more accurately. This study utilized two mechanistic methods (cohesive zone [CZ]-based fracture modeling and continuum damage [CD] modeling) to study the cracking behavior of asphalt mixtures and subsequently predict the damage-associated performance of pavements. A Superpave mixture designed for Brazilian highway conditions was selected to evaluate the two modeling approaches for the performance of the mixture when utilized as a surface course within typical Brazilian highway pavements. The mixture was first evaluated for its linear viscoelastic properties, and then the damage characteristics of the mixture were obtained through two different experiments: the semi-circular bending (SCB) beam test and the cyclic fatigue (CF) test. The SCB tests performed at different loading rates were used to obtain the model parameters of the nonlinear viscoelastic CZ model with a Gaussian damage evolution criterion, while the CF tests were performed at different strain amplitudes, and the mixture-specific damage characteristic curve and the fatigue failure criterion were obtained using the simplified-viscoelastic continuum damage model. From a design perspective, three pavement structures that varied in geometry (pavement layer thickness) and underlying layer properties were selected while retaining the same asphalt mixture. The three pavement scenarios were evaluated for the fatigue cracking performance from each of the mechanistic modeling methods. The results indicate that both methods rank the performance of the pavement structures in a similar manner, while the damage initiation and progression were seen to be different because of the different mechanics in modeling cracks.

Characteristics of life-cycle carbon dioxide emissions of arterial highway maintenance and the influencing factors

With the focus of highway development transitioning from construction to maintenance, a comprehensive understanding of the characteristics and influencing factors of carbon dioxide (CO2) emissions from highway maintenance activities is crucial for formulating effective strategies to promote the low-carbon development of road infrastructure. However, the quantitative relationships between CO2 emissions from highway maintenance schemes and factors such as pavement deterioration, traffic volume, and road grade remain unclear owing to a lack of comprehensive, multi-category, and real data. Using real maintenance data from 340 arterial highway segments in China, this study conducts the life cycle assessment (LCA) to estimate CO2 emissions from maintenance activities and examines the primary emission sources among various structural layers and materials. Furthermore, multiple linear regression (MLR) analysis is conducted to investigate the impact of traffic volume, road grade, and pavement deterioration on CO2 emissions from maintenance projects, and factors influencing the early-stage degradation of pavement performance. The results demonstrate that average CO2 emissions from heavy rehabilitation projects are 6.97 times higher than those from medium rehabilitation projects. Emissions from heavy rehabilitation projects exhibit a significantly negative linear relationship with the riding quality index (RQI) before maintenance (p < 0.05), and emissions from medium rehabilitation projects show a significant negative linear relationship with the pavement condition index (PCI) before maintenance (p < 0.05). Emissions from heavy and medium rehabilitation projects are significantly positively correlated with heavy vehicle traffic volume before maintenance (p < 0.05). Moreover, the early-stage degradation of PCI after heavy rehabilitation and RQI after medium rehabilitation exhibit significantly negative linear relationships with their respective indicators before maintenance (p < 0.05). The early-stage degradation of RQI after heavy rehabilitation is significantly positively correlated with CO2 emissions from the base course and cushion layers (p < 0.05). The findings emphasize that timely maintenance and reduction of CO2 emissions from asphalt mixing equipment are essential for mitigating emissions from road maintenance. This study offers valuable insights for advancing the low-carbon development of highways in temperate regions.

Evaluation of degree of blending and interaction between aged and virgin asphalt binders using rheological and fracture measurements

Evaluating the blending and interaction processes between aged and virgin asphalt binders is significant for enhancing the performance and longevity of asphalt pavements, particularly when using Reclaimed Asphalt Pavement (RAP). The primary aim of this research is to investigate the optimal conditioning time for diffusion process between virgin and aged binders. To simulate age-hardened RAP binder, asphalt binders were artificially aged in a forced draft oven at 163 °C for five hours, followed by additional aging at 85 °C and 110 °C over periods of 5, 7, and 10 days. The research assessed the degree of blending and interaction between aged and virgin binders in two phases using rheological and fracture measurements. In the first phase, the study explored the effects of various conditioning temperatures and durations (135 °C and 165 °C for 15, 30, and 60 minutes) on the diffusion and blending of the asphalt binders. Results indicated that the interaction between aged and virgin binders depends on the temperature and duration of conditioning; notably, complete blending was achieved at 165 °C after 60 minutes. The second phase investigated the impact of aged binder content on the rheological and fracture characteristics of the blended binders. Findings showed that fracture energy and peak load decrease with an increase in aged binder content. Specifically, increasing the aged binder content from 10 % to 30 % resulted in an approximate 23 % reduction in fracture-related parameters, while increasing the aged binder content from 50 % to 70 % led to an approximate 51 % reduction. Additionally, analysis of the complex shear modulus across different blends revealed an increase in modulus and high-temperature performance proportional to the percentage of aged binder, suggesting a hardening effect in the blended binder. Overall, this study underscores the significance of understanding the interactions between aged and virgin binders to optimize the diffusion and blending processes, ultimately enhancing the performance of asphalt pavements containing recycled materials.

Airfield pavement performance prediction using Clustered Markov Chain Models

This study pairs unsupervised learning methods of clustering and classification with discrete-time Markov Chain models for airport pavement performance prediction, with the goal of allowing the clustering algorithm to take the place of typical pre-analysis grouping of pavement sections. The influences of cluster method, number of clusters, input years, interval time span, and training data on prediction results are analysed. Results indicate that clustering using the deterioration profiles of Pavement Condition Index (PCI) before applying the Markov Chain is sufficient to reduce the error by half in predicted pavement conditions for most cases. The initial pavement degradation is a reliable source for clustering and future condition prediction using the appropriately matched Markov Chain. This approach minimises the data required to predict pavement condition and proves the early deterioration rate of a pavement section can be used to predict its overall lifespan.

Multiscale evaluation of aging susceptibility of asphalt binders modified with recycled rubber and plastic pyrolytic oil composites

Changes in rheological performance, migration of chemical components, and microstructure transformation are some of the processes that occur as asphalt binders age across various stages throughout their lifecycle. Aging-related changes influence pavement performance, typically increasing its susceptibility to issues like fatigue and thermal cracking. Thus, comprehending the role of aging in the performance of asphalt binders is vital for devising effective pavement design and maintenance strategies. The use of plastic pyrolysis oil and recycled non-tire automobile rubber to create oil-rubber modified asphalt binders is becoming quite popular as a sustainable and environmentally friendly method for asphalt pavements. This study aimed to assess the aging behavior of asphalt binders modified with plastic pyrolysis oil and recycled non-tire automotive rubber, both separately and in composite modification. Binders underwent testing under four aging conditions: unaged, short-term aged, long-term aged, and extended long-term aged. The study focused on a multiscale assessment of aging characteristics, encompassing macroscale, mesoscale, microscale, and nanoscale analyses. At the macroscale, the binders were tested for rheological parameters using multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and Glover-Rowe (G-R) parameter tests, while the mesoscale analysis was conducted using Fourier-transform infrared (FTIR) spectroscopy. Additionally, microstructural analysis was performed using atomic force microscopy (AFM), and nanoscale evaluation was carried out using nuclear magnetic resonance (NMR). Finally, a comprehensive assessment index that included all the parameters under study was generated using radar graph analysis. It was observed that modifying an asphalt binder with a heat-preheated composite of plastic pyrolysis oil and recycled non-tire automotive rubber showed the lowest CAI values, thus helping to mitigate the effects of aging and achieving enhanced binder performance.

Multi-Step Relaxation Characterization and Viscoelastic Modeling to Predict the Long-Term Behavior of Bitumen-Free Road Pavements Based on Polymeric Resin and Thixotropic Filler.

Asphalt pavements are fundamental to modern transportation infrastructure, requiring elasticity, firmness, and longevity. However, traditional asphalt, based on bitumen, faces several limitations. To improve pavement performance, polymer resins are being used to substitute bitumen and improve requirements. Therefore, a deep understanding of the material behavior is required. This study presents the analysis of the relaxation behavior of a poly(methyl methacrylate)-based pavement and the influence of mineral fillers. An approach using a linear elastic-viscoelastic material model was selected based on evidence and validated across the linear and nonlinear deformation range. The results reveal no influence of the mineral fillers on the relaxation behavior. The presented modification of the linear elastic and viscoelastic modeling reveals accurate results to predict long-term pavement performance. This approach offers a practical method for forecasting asphalt behavior. Further research is needed to incorporate deformation behavior into the model.

Phase-Change/Salt-Based Slow-Release Composite Material for Anti-Icing and Snow-Melting

Currently, self-desiccating asphalt mixtures on roads mainly incorporate phase-change materials or salt-based slow-release agents individually for de-icing. However, pure phase-change material mixtures have limited anti-freezing efficiency and short heat-release duration, making them impractical for large-scale snow melting; meanwhile, salt-based slow-release agents suffer from rapid deterioration in de-icing performance. To address these issues encountered, herein, we introduce the phase-change/salt-based slow-release composite materials via the integration of these two materials and investigate their pavement and de-icing performance with the asphalt mixture. For the pavement performance, the optimal asphalt–aggregate ratio for the anti-icing asphalt mixture was found to be 5.1% For anti-bonding and de-icing performance, the electrical conductivity tests, bonding pull-off tests, and interfacial contact melting experiments were conducted. The results indicate that the latent heat of the TH-ME5 (phase-change material) can delay the decrease in environmental temperature and inhibit salt release from T-SEN (salt-based slow-release material), thereby extending the lifespan of the anti-icing asphalt mixture. These results demonstrate that the synergistic effect between the two components of the composite material not only enhance the snow-melting and de-icing performance of the asphalt pavement but also prolong the snow-melting time of the pavement in a low-temperature environment.