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.

PURPOSES : This paper is aimed at suggesting a novel approach for determining the pavement condition rating based on the tire-surface friction noise using a machine learning algorithm as a low-end pavement condition monitoring system. METHODS : Vehicle on-board type noise measurement system according to the ISO11819-2, and the K-nearest neighbors with dynamic time warping algorithm were applied. The system and algorithm were empirically tested with a field study. RESULTS : The developed AI- and noise-based pavement condition monitoring system demonstrated significantly positive results with a precision 90.8%, recall 84.8%, and f1-score 86.1%. CONCLUSIONS: We herein confirmed that the acoustic property between the tire and road surface can be used for monitoring pavement conditions. It is believed this finding presented a new paradigm for monitoring pavement conditions based on visual information. However, extensive studies focused on the practical application of this method are required.

PURPOSES: In this study, algorithms were proposed for determining the crack condition of an asphalt pavement image using deep learning methods. METHODS: For the configuration of a deep learning network, the study used a Convolution Neural Network and You Only Look Once algorithms. To obtain input data for analysis, a camera was mounted on the bonnet of the vehicle to obtain images of asphalt pavement and to mark the ground-truth cracks in the asphalt pavement image. In addition, an algorithm suitable for the automatic determination function of Deep Learning was proposed in order to calculate the crack ratio and crack rating. RESULTS: The result of analysis showed that the recall rate of cracks in this system was higher from FPPW 5.0E-06 to 96.03%. Furthermore, the accuracy of the grading system was found to be 100%, enabling the determination of very accurate ratings. The rate of processing per image was 0.4448 seconds on average, and the real-time analysis of pavement images presented no problem because the assessment took place within a short time. CONCLUSIONS : Applying this system to the pavement management system is expected to reduce the time required in finishing work and to determine a quantitative crack rating.

Pavement Condition Index (PCI) is an important index to establish a proper maintenance and rehabilitation strategy of a road network. The index is calculated based on the present state of surface defects, deformation and cracking. The information is normally obtained by visual inspection and observation of road networks. Nowadays, various sensor-based visual inspection techniques are applied to obtain detailed information of a road network, and to automate the entire process of calculating PCI. Hyperspectral analysis is a technique to identify the spectral signature of a material in the electromagnetic spectrum. The technique is being applied to pavement condition evaluation. Some researchers have reported that Exposed Aggregate Index (EAI) has a relationship with the reflectance of a hyperspectral image of a road network. In this study, the possibility of using hyperspectral images for pavement condition evaluation is experimentally investigated and the relationship between EAI and PCI is addressed.

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS: Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS: The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

OBJECTIVES : This study develops an evaluation method, which is useful to inspect pavement condition of specific boroughs. This is because pavement condition is broadly divided into five grades via visual inspection, which does not consider the types of deteriorations, and is decided by an investigator having a subjective viewpoint. This visual inspection method is not a satisfactory method for accurate maintenance when various deteriorations occur. METHODS: The performance model considers several factors such as crack, rutting, and IRI. This method is also modified from borough SPI based on SPI (Seoul Pavement Index). Considering limited budget of borough, PI (prediction index) is suggested, which is related to the grade of pavement condition evaluation and type of materials. Practical correlation review is also conducted with statistical verification by using the Monte Carlo simulation. RESULTS: The results of the study show that modified criteria are reasonable. First, the comparison between the visual inspection result and the SPI result indicates that the R-square value is sufficiently high. Second, through the common section, each evaluation method could be compared, and the result shows considerable similarity, which increases when the range is modified. Finally, PI for predicting remaining life and the random number SPI have common parts, which means that each indicator would be adequate to be used as an evaluation method. CONCLUSIONS : Comparison and analysis results show that the developed evaluation method is reasonable for specific boroughs where financial support is inadequate for the evaluation process by using the newer equipment. Moreover, for more accurate evaluation method, previous visual inspection data should be utilized, and the database of inspection equipment have to be collected.