PURPOSES : Graphene nanoplates, which have recently been in the spotlight in various fields, are a layer of graphite used in pencil leads, with carbon arranged in hexagonal honeycomb shapes. The graphene is 0.2 nanometers thick, and it possesses high physical and chemical stability, high strength, and conductivity. These graphene nanoplates have been studied for application in various devices such as semiconductors and batteries, and in the construction sector, where they are used as additives to improve the durability of cement concrete. The purpose of this study was to investigate the physical, and functional properties of graphene-modified asphalt mixtures. METHODS : In this study, the graphene input content of asphalt mixture samples was determined using an asphalt performance grade (PG) test. Based on the results of the test, their strength, stiffness, thermal properties, and electrical conductivity were evaluated. Indirect tensile strength test and dynamic modulus (DM) test were conducted to evaluate the strength and stiffness, and thermal conductivity tests and electrical conductivity evaluations were conducted for determining the functionality of the graphene-modified asphalt mixtures. The thermal conduction test was used to measure the external temperature change over time by placing a general heated asphalt mixture and graphene-modified asphalt with the same raw material-specific mixing ratio inside the temperature chamber in order to measure the heat conductivity. The electrical conductivity was evaluated using a digital multimeter to measure the resistance of DC voltage and DC current via a 4-probe method. RESULTS : The performance grade (PG) test results showed that, for a dynamic shear rheometer (DSR), both tests met the baseline and that physical changes in the binder did not appear evident with graphene addition. Furthermore, each content met the baseline for the bending beam rheometer (BBR). The increasing ratio of flexural creep stiffness approached the maximum when 7.5% graphene was used. In indirect tensile strength test, an average of thrice the indirect tensile strength for graphene-modified asphalt was 0.92 N/mm2, which was approximately 0.04 N/mm2 higher than the average measured three times that of hot mix asphalt mixture, with the same raw material mixing ratio. In the thermal conduction tests, the temperature and the rate of change of temperature of the graphene-modified asphalt mixture were higher than those of the hot-mix asphalt mixture. Lastly, the results of the electric conductivity test using the 4-probe method showed that the electrical conductivity increased slightly as the graphene content increased, but overall, it showed very low electrical conductivity. CONCLUSIONS : In this study, the potential for enhancing the physical and functional performance of graphene nanoplates applied to asphalt mixtures was demonstrated. However, it is practically difficult to arrange graphene particles continuously within an asphalt mixture, which is believed to have very low electrical conductivity.

PURPOSES : In this study, we aimed to investigate the heat transfer characteristics of asphalt mixtures by water saturation. METHODS : On the basis of the literature review, the heat transfer characteristics of the samples were analyzed using a thermal accumulation experiment. The types of samples used were WC2 (dense asphalt mixture), ReWC2 (used 30% recycled aggregate), and PA13 (drainage asphalt mixture). The samples were compacted using a gyratory compactor. An infrared lamp simulating insolation was used to continuously heat the asphalt sample. Through this experiment, the upper and lower temperatures and heat flux of the specimen according to its thickness and condition were measured, and the change in its thermal conductivity was analyzed. RESULTS : The results of the laboratory experiment indicated that the dry sample showed lower thermal conductivity than the saturated sample. The amount of evaporation varied depending on the internal pores of the sample. Additionally, the amount of evaporation changed the heat transfer characteristics of the specimen. CONCLUSIONS : An asphalt mixture with high porosity decreased the degree of increase in thermal conductivity, compared to mixtures with low porosity, under semi-saturated conditions; this was attributed to the difference in thermal conductivity between air and water during saturation. The results of this study on the heat transfer characteristics of asphalt pavements could be used as basic data for thermal energy harvesting of asphalt pavements.

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.