PURPOSES : The purpose of this study is to evaluate the performance characteristics of stone mastic asphalt (SMA) pavement by comparison with polymer modified asphalt (PMA) pavement and conventional asphalt pavement, to check the performance characteristics according to the pavement type, pavement materials, traffic volume, and environmental factors and to analyze the quality variation characteristics according to the pavement materials using data extracted from the database of the expressway long-term pavement performance. METHODS : Approximately 10% outlier data of pavement performance data were excluded in order to increase the reliability of the analysis results before evaluating the asphalt pavement performance. The performance model was developed through linear regression analysis by setting the performance period as the independent variable and the highway pavement condition index (HPCI) as the dependent variable. Descriptive statistic analysis of HPCI using the static package for social science (SPSS) tool and the analysis of variance was performed to identify the quality variation characteristics according to the pavement materials. The amount of de-icing agent and traffic level of service were classified as two levels in order to check the influence of traffic volume and environmental factors on the performance characteristics of the asphalt pavement. RESULTS : The tentative pavement performance lives were calculated at 19.3 years for new the SMA pavement (GPS-2), 14.3 years for the SMA overlay on the asphalt pavement (GPS-6), and 10.3 years for the SMA overlay on the concrete pavement (GPS-7). In case of the asphalt overlay, the tentative performance lives were calculated at 8.2 years for the PMA overlay on the asphalt pavement (GPS-6), 7.2 years for the PMA overlay on the concrete pavement (GPS-7), 7.2 years for the conventional asphalt overlay on the asphalt pavement (GPS-6), and 5.5 years for the conventional asphalt overlay on the concrete pavement (GPS-7). CONCLUSIONS : It was confirmed that the SMA pavement showed better performance and quality variation characteristics than the PMA and conventional asphalt pavement. The performance characteristics of the asphalt pavement (GPS-2) was better than the asphalt overlay pavement, and the asphalt overlay on the asphalt pavement (GPS-6) had better performance characteristics than the asphalt overlay on the concrete pavement (GPS-7). It was observed that the asphalt overlay on the asphalt pavement (GPS-6) was strongly influenced by the traffic volume and the asphalt overlay on concrete pavement (GPS-7) was strongly influenced by the traffic volume and de-icing agent.

PURPOSES : The adhesive bonding strength of the grid between asphalt pavements is critical in pavement performance. The study is to compare and evaluate the interlayered bonding strength of asphalt mixture specimens with fiber-glass grid (FG) reinforcement and different tack coating materials based on the test results of the shear bonding test. METHODS : Asphalt mixtures were molded with FG reinforcement using various tack coating materials namely RSC4 and D/B coat. The adhesive shear-bond strength was measured by inducing a monotonic shear loadnig rate of 5 mm/min at 20℃. RESULTS : As expected, the asphalt mixture with non-reinforced FG exhibited the highest adhesive shear-bond strength, followed by that of the FG with D/B coating. The ranking order of superiority is as follows: Control (RSC4) > D/B+FG > RSC4+FG. CONCLUSIONS : The results of this experimental study indicate that FG with RSC4 and D/B tack coats can be successfully used in asphalt concrete overlay construction with superior field performance. Based on the test results and literature review, the field bonding strength should exceed 300kPa in grid reinforced asphalt pavement.

PURPOSES : The purpose of this study is to evaluate the performance of an ultra-thin asphalt pavement as a preventive maintenance approach through laboratory tests. METHODS : An ultra-thin asphalt pavement of 2 cm wearing course thickness comprising modified asphalt and aggregate is a preventive maintenance method used for asphalt pavements. A mix design was carried out to determine the optimum aggregate gradation and asphalt contents. A dynamic immersion test was performed to evaluate the water-resistance of the ultra-thin asphalt pavement. A wet track abrasion test and a cohesion test were conducted to examine the applicability of the ultra-thin asphalt pavement in surface treatment. The performance of the ultra-thin asphalt pavement was evaluated through wheel loading tests, such as Hamburg wheel-tracking and third-scale model mobileloading simulator (MMLS-3). RESULTS : An optimum binder content of 4.9% was obtained in the ultra-thin asphalt mixture from the Marshall mix design. The waterresistance tests indicated a 70% dynamic immersion coverage rate of the ultra-thin asphalt pavement. The wet track abrasion test showed an abrasion rate of 0.0107 g/cm2, and the cohesion tests indicated a 19.0 kg·cm average cohesion at 30 min of operating time and 21.4 kg·cm average cohesion at 60 min of operating time. From the Hamburg wheel-tracking test, a 16.56 mm rut depth at 20,000 wheel passing was obtained. Finally, a 5.87 mm rut depth at 300,000 number of wheel passing was detected from the MMLS-3 test. CONCLUSIONS : The water-resistance of the ultra-thin asphalt pavement satisfied the recommended guidelines of the Korean Ministry of Land, Infrastructure and Transport. In addition, the applicability of the ultra-thin asphalt pavement as a surface treatment met the standard of the International Slurry Surfacing Association. Furthermore, the deformation performance of the ultra-thin asphalt pavement was 1.5 times better than that of the straight asphalt pavement, based on the results of the wheel-loading tests. Hence, it is estimated that an ultra-thin asphalt pavement has a high performance in the preventive maintenance of asphalt pavement, even though the cracking resistance was not evaluated in this study.

PURPOSES: Investigating road pavement conditions using an investigation vehicle is challenging especially if repeated driving is required on the by-lane, and the traffic in the investigation section is heavy. A technology used to investigate the road pavement conditions is studied herein using image data obtained by drone photography. METHODS : Flight plans were made for the survey areas, and ground control point measurements were performed. The research section was filmed using drones. The acquired image data were modeled using Pix4Dmapper. The images taken by the drones were used to investigate the road pavement cracks. A digital surface model was extracted from the Pix4Dmapper modeling results using the Global Mapper program to investigate plastic deformation and flatness. As regards plastic deformation, the elevation of each point was extracted at intervals of 50 cm and 10 cm in the longitudinal and lateral directions, respectively, for 20 m× 10 m of the entire road. In terms of flatness, the elevation values for each point were extracted at intervals of 5 cm and 10 cm for the wheel path and 20 m for the entire roadway. RESULTS: This study compared drone-captured images, which were consistent, and vehicle scan images and confirmed that the former can detect a large number of cracks on road surfaces. The results showing the difference in the elevation values of the road surface indicate that the section, wherein the plastic deformation occurs throughout the entire road surface, can be identified and evaluated. With regard to flatness, in future studies, the long-directional elevation value of the target segment extracted using Global Mapper is likely to be derived from the International roughness index, which is the international flatness index used in the ProVAL program developed and used by the Federal Highway Administration. CONCLUSIONS : The road pavement status investigation conducted herein by utilizing drone-acquired images showed that repeated driving in a section is not required, and various analyses can be made in a single shot. If technologies, such as artificial intelligence, big data, and Internet of Things, which are the key components of the Fourth Industrial Revolution, are adapted, they can be used to investigate road pavement conditions and inspect completely constructed road lines and major road facilities.