Fatigue cracking is one of the major distresses in asphalt pavements. Engineers have used many methods, from sophisticated numerical simulations to experience-based knowledge, to address fatigue cracking and deliver pavements that perform well. One strategy that has become increasingly widespread is the use of a fatigue index parameter that identifies the cracking potential of asphalt mixtures independent of the pavement structure. The use of such indices allows pavement engineers to make quick and targeted decisions with regard to mix design, mix acceptance, and mix quality assurance. This paper proposes a new fatigue index parameter, S_app, which represents the damage capacity of a mixture and is based on the viscoelastic continuum damage theory. Unlike other indices, this new index considers the effects of both material toughness and stiffness. S_app was employed in this study to evaluate 105 different asphalt mixtures with varied design factors. The results indicate that S_app is able to distinguish the fatigue resistance of asphalt mixtures with varied properties, including different binder contents, binder performance grades, reclaimed asphalt pavement contents, types of binder modifier, air void contents, aggregate gradations, and aging levels. This paper also presents a set of recommended S_app threshold values for different expected traffic volumes.

Accurate prediction of asphalt pavement condition is important to guide pavement maintenance practices. The existing models for pavement condition predictions are predominantly based on linear regressions or simple machine learning techniques. However, additional work on these models is needed to improve their basic assumptions, training efficiency, and interpretability. To this end, a new modeling approach is proposed in this manuscript, which includes a ThunderGBM-based ensemble learning model, coupled with the Shapley Additive Explanation (SHAP) method, to predict the International Roughness Index (IRI) of asphalt pavements. The SHAP method was applied to interpret the underlying influencing factors and their interactions. Twenty features were initially identified as the model inputs, and 2,699 observations were extracted from the Long-Term Pavement Performance (LTPP) database. Three benchmark models, namely the Mechanistic-Empirical Pavement Design Guide (MEPDG) model, the ANN model and the RF model, were used for comparison. The results showed that the developed model achieved a satisfactory result with a R-squared (R² value of 0.88 and Root Mean Square Error (RMSE) of 0.08, both better than three benchmark models. It ran 86 times and 2.3 times faster than the ANN and RF model, respectively. Feature interpretation was performed to identify the top influencing factors of IRI. The 20-feature model was further simplified based on the analysis result. The simplified model only required six features to efficiently and effectively predict IRI using the proposed ThunderGBM-based approach, which can reduce the workload in data collection and management for pavement engineers.

The prediction of fatigue cracking is one of the critical items to assess the service life of flexible pavements. Accordingly, researchers have developed several test methods and index parameters to predict pavement performance based on fatigue damage. Recently, researchers at North Carolina State University developed a new index parameter, referred to as apparent damage capacity ($S_{\mathrm{app}}$ ), which is based on the simplified viscoelastic continuum damage (S-VECD) model. This index parameter is obtained by performing cyclic tension tests and using the simplified viscoelastic continuum damage model with a dissipated pseudoenergy concept (the so-called $D^R$ failure criterion). This paper presents $D^R$ and $S_{\mathrm{app}}$ values for 10 different asphalt mixtures commonly used in Georgia. The relationships among $D^R$ , $S_{\mathrm{app}}$ , and mix design factors such as nominal maximum aggregate size (NMAS), asphalt binder type, and binder content were investigated to determine ways that these factors affect $D^R$ and $S_{\mathrm{app}}$ . The $S_{\mathrm{app}}$ parameter was found to have a strong relationship with the polymer modification and NMAS of the asphalt mixtures. Based on the experimental data and the Georgia Department of Transportation’s practical guidelines for specific mixtures, this study developed $S_{\mathrm{app}}$ threshold values for different traffic levels. Finally, the experimental data were used to run pavement performance analyses using AASHTOWare Pavement ME Design software and FlexPAVE; the latter uses the S-VECD model for cracking evaluation. Top-down and bottom-up cracking performance was determined from a 10.2 cm (4 in.) thick single-layer pavement and a 15.2 cm (6 in.) thick 2-layer pavement. The results are used to discuss the different ways the two programs evaluate cracking.

This paper presents the results of a study to evaluate the fatigue performance of pavements that contain RAP materials. The study is based on two different accelerated loading tests conducted in the United States. Each of these two projects has corresponding pavements with and without RAP mixtures that were constructed over the same aggregate base and subgrade and subjected to the same traffic loading and environmental conditions. The primary reason for selecting these two projects is that they exhibited opposite trends in a comparison of the fatigue cracking performance of their pavements with and without RAP. The asphalt mixtures in the test pavements were characterized using the simplified viscoelastic continuum damage model. The fatigue cracking performance of the pavements was then modeled using the characterized material properties, in situ pavement structures and traffic loads, and climate conditions in FlexPAVE™, a three-dimensional layered viscoelastic finite element program with moving load analysis. The resultant fatigue cracking performance predictions matched the field observations well. Both the simulation results and the field observations also showed that the fatigue cracking performance of the asphalt mixtures that

Highly polymerized asphalt binders (HPABs) such as PG 64-40 have been increasingly used in cold regions such as Alaska in recent years. However, there are currently considerable data gaps when it comes to the characterizations of the HPABs, especially their fatigue behaviors. The objective of this study is to apply the linear amplitude sweep (LAS) test with the viscoelastic continuum damage (VECD) model to assess the fatigue resistance of HPABs. Three HPABs (PG 52-40, PG 64-40, and PG 52-46) and one unmodified binder (PG 52-28) were used in the study. The fatigue failure during LAS tests on HPABs was defined, and an analysis protocol using the VECD model was proposed based on the experimental results. The analysis protocol was validated by the agreements of the predicted and measured number of cycles to failure in the time sweep (TS) test. The results indicated that the LAS test with the selected analysis protocol can effectively predict the fatigue lives of HPABs. Both the LAS and TS test results showed that the HPABs had higher fatigue resistance than the unmodified asphalt binder. Among the four binders, the PG 64-40 binder exhibited the longest fatigue lives under repeated loadings.

Incorporating phase change material (PCM) into paving materials can regulate the pavement temperature, improve the pavement durability, and mitigate the heat-island effects. In this research, porous fillers were used as the PCM carrier, and the thermal and rheological behaviors of the asphalt mastic with the PCM were evaluated. Two different carrier materials (diatomite and expanded perlite) and four types of PCMs were used in the study. The candidate filler, PCM, and proper blending ratios were determined based on the results of scanning electron microscope image analysis, the filter paper test, and the temperature sweep test. The thermal and rheological behaviors of the mastics with PCMs were further evaluated with different filler replacement ratios. Thermal analysis through a differential scanning calorimetry test, thermal conductivity and volumetric heat capacity test, and real-time temperature performance test were performed on the asphalt mastics. Rheological tests, including the complex shear modulus test, the bending beam rheometer test, and the linear amplitude sweep test, were also conducted. The modified mastics were found to have high heat capacity with the latent heat storage ability. The rheological analyses showed that with the addition of polyethylene glycol, while the low-temperature performance of the asphalt mastics was improved, the performance at intermediate and high temperatures was not adversely affected by the PCM.