The purpose of this study is to explore the applicability of satellite-based synthetic aperture radar (SAR) data combined with pavement management system (PMS) indicators for effective road condition monitoring on mountainous local roads. Field survey data, including the International Roughness Index (IRI) and rutting measurements, were used as the ground truth, whereas Sentinel-1 and COSMO-SkyMed SAR images were processed using the time-series InSAR analysis to detect surface displacement and pavement deformations. In addition, a deep learning framework integrating PMS data and SAR imagery was developed, consisting of a swine transformer and CNN–LSTM networks for the classification and localization of pavement defects. The results demonstrated that X-band SAR backscatter values were correlated with IRI variations and that the proposed hybrid two-stage approach (CNN for surface damage and LSTM for rutting) enhanced the accuracy of defect detection compared with conventional single-model approaches. These findings highlight the potential of combining remote sensing and AI-based analysis with existing PMS datasets to provide a cost-effective and scalable solution for road asset management and maintenance prioritization.

본 연구는 도로 보수 후 새롭게 도입되거나 개선된 도로안전시설 중 노면표시와, 안전운전에 간접적인 영향을 미치는 도로 노면 상 태에 대한 이용자 만족도를 연령 및 업종별로 분석하고자 수행되었다. 운전자 430명을 대상으로 5점 리커트 척도 설문을 시행하고, SPSS 27.0을 이용해 일원배치 분산분석(ANOVA)과 사후 검정을 실시하였다. 그 결과, 노면표시(크기·선명도·야간시인성)는 업종별(관 리·사무·전문직, 운전직, 학생 등)로 유의미한 차이가 있었고, 연령대별로는 10대가 노면표시 크기에 가장 만족도가 높았으며 고령층으 로 갈수록 낮아졌다. 또한 운전직의 만족도가 전반적으로 낮아 노면표시가 실제 주행에 크게 영향을 주는 것으로 나타났다. 본 연구 결과는 연령별·업종별 특성을 고려한 도로환경 및 정책 개선에 기초 자료로 활용될 수 있을 것이다.

PURPOSES : The purpose of this study is to provide basic data to improve the service life of asphalt pavement using basalt aggregate in Jeju Island by evaluating the performance of asphalt pavement through analysis of material and structural aspects. METHODS : To evaluate the performance of Jeju Island's asphalt pavement, cracks, permanent deformation, and longitudinal roughness were analyzed for the Aejo-ro road, which has high traffic and frequent premature damage. Cores were collected from Aejo-ro sections in good condition and damaged condition, and the physical properties of each layer were compared and analyzed. In addition, plate cores were collected from two sections with severe damage and the cause of pavement damage was analyzed in detail. RESULTS : About 45% of the collected cores suffered damage such as layer separation and damage to the lower layer. The asphalt content of surface layer in the damaged section was found to be 1.1% lower on average than that in the good condition section, and the mix gradations generally satisfied the standards. The density difference between the cores of each layer was found to be quite large, and the air voids was found to be at a high level. CONCLUSIONS : Test results on the cores showed that, considering the high absorption ratio of basalt aggregate, the asphalt content was generally low, and the high air voids of the pavement was believed to have had a significant impact on damage. High air voids in asphalt pavement can be caused by poor mixture itself, poor construction management, or a combination of the two factors. Additionally, the separation of each layer is believed to be the cause of premature failure of asphalt pavement.

PURPOSES : The evaluation of the low-temperature performance of an asphalt mixture is crucial for mitigating transverse thermal cracking and preventing traffic accidents on expressways. Engineers in pavement agencies must identify and verify the pavement sections that require urgent management. In early 2000, the research division of the Korea Expressway Corporation developed a three-dimensional (3D) pavement condition monitoring profiler vehicle (3DPM) and an advanced infographic (AIG) highway pavement management system computer program. Owing to these efforts, the management of the entire expressway network has become more precise, effective, and efficient. However, current 3DPM and AIG technologies focus only on the pavement surface and not on the entire pavement layer. Over the years, along with monitoring, further strengthening and verification of the feasibility of current 3DPM and AIG technologies by performing extensive mechanical tests and data analyses have been recommended. METHODS : First, the pavement section that required urgent care was selected using the 3DPM and AIG approaches. Second, asphalt mixture cores were acquired from the specified section, and a low-temperature fracture test, semi- circular bending (SCB) test, was performed. The mechanical parameters, energy-release rate, and fracture toughness were computed and compared. RESULTS : As expected, the asphalt mixture cores acquired from the specified pavement section ( poor condition – bad section) exhibited negative fracture performances compared to the control section (good section). CONCLUSIONS : The current 3DPM and AIG approaches in KEC can successfully evaluate and analyze selected pavement conditions. However, more extensive experimental studies and mathematical analyses are required to further strengthen and upgrade current pavement analysis approaches.

PURPOSES : The objective of this study is to develop the data driven pavement condition index by considering the traffic and climatic characteristics in Incheon city. METHODS : The Incheon pavement condition index (IPCI) was proposed using the weighted sum concept with standardization and coefficient of variation for measured pavement performance data, such as crack rate, rut depth, and International Roughness Index (IRI). A correlation study between the National Highway Pavement Condition Index (NHPCI) and Seoul Pavement Condition Index (SPI) was conducted to validate the accuracy of the IPCI. RESULTS : The equation for determining the IPCI was developed using standardization and the coefficient of variation for the crack rate, rut depth, and IRI collected in the field. It was found from the statistical analysis that the weight factors of the IPCI for the crack rate were twice as high as those for the rut depth and IRI. It was also observed that IPCI had a close correlation with the NHPCI and SPI, albeit with some degree of scattering. This correlation study between the NHPCI and SPI indicates that the existing pavement condition index does not consider the asymmetry of the original measured data. CONCLUSIONS : The proposed pavement condition provides an index value that considers the characteristics of the original raw data measured in the field. The developed pavement condition index is extensively used to determine the timing and method of pavement repair, and to establish pavement maintenance and rehabilitation strategies in Incheon.

PURPOSES : The purpose of this study was to develop the evaluation methodologies for spraying amount and sprayed condition of curing compound based on IoT technology when concrete pavements are constructed. METHODS : To measure the spraying amount of curing compound, a turbine type flowmeter was selected and a number of laboratory experiments were performed to verify the applicability of the selected sensor. To evaluate the uniformity of the sprayed curing compound on the concrete pavement surface, image process technologies were examined using pictures taken from the actual construction sites and from the test specimens. RESULTS : By performing experiments using water and curing compound, the selected flowmeter was verified to properly be applied to measure the spraying amount of curing compound with an acceptable accuracy. By conducting image processing using pictures of the sprayed curing compound on the concrete pavement surface, it was found that the 8 color analysis method was the best to evaluate the uniformity of the sprayed curing compound. CONCLUSIONS : From this study, it was concluded that the spraying amount of curing compound could be accurately measured using a turbine type flowmeter and the uniformity of the sprayed curing compound on the concrete pavement surface could be properly evaluated using an image processing technology.

PURPOSES : The purpose of this study is to enhance the reliability of artificial intelligence for a noise-based pavement condition rating system (to a target performance of 95 %). METHODS : By comparing four types of pattern recognition artificial intelligence, this work acquires high-quality learning data and optimizes data learning through analysis of error characteristics. RESULTS : The system reliability improved up to 97 % (82 % in a prior study). In addition, 100 % was achieved for the E(F) condition grade, which has a direct impact on maintenance decision making. CONCLUSIONS : KNN-DTW (K-nearest neighbor dynamic time warping) is judged to be the most suitable type of artificial intelligence for a noise-based pavement condition rating system; a 4-grade system is the most suitable for classifying pavement condition.

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.