PURPOSES : Rut depth of asphalt pavements is a major factor that affects the maintenance of pavements as well as the safety of drivers. The purpose of this study was to analyze the factors influencing rut depth, using data collected periodically on national highways by the pavement management system and, consequently, predict annual rut depth change, to contribute to improved asphalt pavement management. METHODS : The factors expected to influence rut depth were determined by reviewing relevant literature, and collecting the related data. Further, the correlations between the annual rut depth change and the influencing factors were analyzed. Subsequently, the annual rut depth change model was developed by performing regression analysis using age, present rut depth, and annual average maximum temperature as independent variables. RESULTS : From the sensitivity analysis of the developed model, it was found that age affected the annual rut depth change the most. Additionally, the relationship between the dependent and independent variables was statistically significant. The model developed in this study could reasonably predict the change in the rut depth of the national highway asphalt pavements. CONCLUSIONS : In summary, it was verified that the model developed in this study could be used to predict the change in the National Highway Pavement Condition Index (NHPCI), which represents comprehensive conditions of national highway pavements. Development of other models that predict changes in surface distress as well as international roughness index is required to predict the change in NHPCI, as they are the independent variables of the NHPCI prediction model.

PURPOSES : This study aims at evaluating the use of an electromagnetic density gauge (EDG) to measure the in situ density and air-void content of asphalt concrete (AC) pavement. METHODS : In situ AC pavement density and air-void readings were obtained from two sites (Daegu and Ulsan) using an EDG. Calibration of the EDG was conducted by first obtaining density values at three different positions, on each pavement where core samples were extracted afterward. The core samples were then tested to obtain laboratory density and air-void values. The density measured using the EDG was then subtracted from the laboratory values to obtain the offset calibration values, which were then adopted to calibrate the in situ measurements using the EDG. Moreover, to analyze the effect of moisture on the pavement surface, EDG measurements were conducted under dry and wet conditions to compare the in-situ readings. RESULTS : The in-situ density readings of AC tend to be higher in moist/wet conditions. By applying the calibration value to the EDG readings, the density error percentage was reduced from 0.61% to 0.096%, and 0.64% to 0.16% for Daegu and Ulsan sites, respectively. Consequently, the air-void content error percentage was reduced from 12.8% to 1.04%, and from 10.07% to 1.78% for Daegu and Ulsan sites, respectively. CONCLUSIONS : The electromagnetic density gauge (EDG) is an effective tool for the non-destructive measurement of in situ pavement density. By applying offset calibration values, the error in the field readings was reduced, and the accuracy of the EDG measurements was improved.

PURPOSES : The type and degree of structural conditions and influencing factors distributed across representative sections should be similar to those distributed across entire sections as the representative sections have been predominantly used for developing performance prediction models, which substitute entire sections of road pavement. Therefore, a logic that selects the representative sections with similar distributions of structural conditions and the influencing factors with those of entire expressway asphalt pavement sections requires development. METHODS : The logic developed in this study to select the representative sections of asphalt pavements comprised three steps. First, the data on the structural conditions of the pavement and the influencing climate conditions and pavement materials were collected and organized. Consequently, in the second step, the candidate sections were selected, with the severity of the structural conditions of the pavement distributed widely and evenly. Finally, in addition to the widely and evenly distributed pavement conditions, the representative sections with climatic conditions and pavement materials were selected. RESULTS : A total of 6,352 ordinary asphalt pavement sections and 596 composite asphalt pavement sections were selected as entire expressway asphalt pavement sections and the data were collected and organized according to the logic developed in this study. Three times the representation sections were selected as candidate sections and, finally, 85 sections were selected as representative sections. The distribution of structural conditions and influencing climate conditions and pavement materials in the representative sections were similar to those in the entire sections. In addition, the representative sections were spread evenly across the country. CONCLUSIONS : The sections presenting similar distributions of structural conditions and the influencing factors of entire expressway asphalt pavement sections could be selected in this study. Using the representative sections selected in this study, a remodeling index model will be developed for predicting the asphalt pavement sections that require large-scale repair.

PURPOSES : This study aims to reduce the urban heat island phenomenon via utilization of porous asphalt pavements. METHODS : One of the many known functions of porous asphalt is that it reduces the urban heat island phenomenon. Indoor experiments were conducted to compare the surface temperature of sprinkled dense-graded and porous asphalt and outdoor experiments were conducted to verify the difference between the two asphalt pavements under external conditions. RESULTS : The results of the indoor experiment demonstrated that the temperatures of the two pavements were similar and that the porous asphalt pavement exhibited low temperature when sprinkled; the temperature of the porous asphalt was approximately 2 °C lower than that of the dense-graded asphalt pavement. The results of the outdoor experiment showed that the peak temperatures of the two pavements were approximately the same as usual. However, it was confirmed that the surface temperature of the porous asphalt pavement at night after sunset was lower than that of the dense-graded asphalt pavement and that the peak temperature dropped for approximately 1~2 days after the rainfall.. CONCLUSIONS : Porous asphalt pavement has a lower surface temperature than normal dense-graded asphalt pavement, under the presence of moisture in the pavement. In addition, it was confirmed that the lower surface temperature of the porous asphalt pavement is due to the low heat emission of the pavement at night. Accordingly, it is believed that the application of the porous asphalt pavement will not only have known effects but also significant impacts on the reduction of urban heat island phenomena.

PURPOSES : The objective of this study is to address various problems, such as an increase in material cost and premature failure (e.g., cracks and potholes) of porous pavements, and to develop multifunctional asphalt and asphalt mixtures to ensure the long-term commonality of porous asphalt pavements. METHODS : A basic quality test of two types of porous asphalt mixtures was performed. One type consisted of the existing porous asphalt mixture, using domestically presented grading, and the other a porous asphalt mixture using high-viscosity modified asphalt with enhanced low-temperature properties, aimed at improving strain resistance and developed by applying the grading suggested by the Federal Highway Administration (FHWA). RESULTS : The cantabros loss rate was 19.62 % for conventional modified asphalt (PG 82-22) and 5.95 % for the developed highviscosity modified asphalt (PG 88-28), indicating that both mixtures passed the criteria. Regarding the drain-down loss rate, mixtures using both types of asphalt were found to pass all quality standards. The average permeability coefficients for each porous asphalt mixture were 0.023 and 0.018 and both types of porous asphalt mixtures satisfied the quality standard of 0.01 cm/s, as given by the Asphalt Concrete Pavement Guidelines of the Ministry of Land, Infrastructure, and Transport. CONCLUSIONS : As a result of the mix design of the two porous asphalt mixtures, the mixture developed in this study was found to be superior to the conventional porous asphalt mixture using conventional porous asphalt grading and modified asphalt.

PURPOSES : This study aims to determine the type (e.g., melting point, freezing point, latent heat fusion) and optimal content of phase change material (PCM) based on the numerical and experimental analyses evaluating the effects of heat transfer in PCM-modified asphalt pavement systems. METHODS : The effect of PCM on the thermophysical properties of PCM-modified asphalt concrete can be taken as an effective volumetric heat capacity. The volumetric fraction of PCM was calculated using an iterative method. The numerical model was established and computed using the MATLAB 2020 software. The optimum PCM design tool was developed to select the type and contents of the PCM. The PCM was chosen based on the following criteria: black-ice-formation delay time, minimize temperature increase, and increase temperature area. To validate the numerical model, asphalt mixtures were modified with varying PCM contents, and the temperature response of the PCMmodified asphalt samples was examined via temperature test. RESULTS : The numerical results showed that incorporating PCM into the asphalt mixture can slow the cooling rate of the pavement system. The predicted results from the optimum PCM design tool were highly consistent with the measured values from the laboratory temperature test. CONCLUSIONS : The temperature of PCM-modified asphalt pavement can be predicted via numerical method. The effect of PCM on the thermophysical properties can be considered as effective volumetric heat capacity; while the volume fraction of PCM can be calculated via an iterative method. The accuracy of the numerical model was confirmed by a high agreement between the measured and predicted values.