PURPOSES : The numeric-based Highway Pavement Management System (HPMS), along with an advanced three-dimensional pavement condition monitoring profiler vehicle (3DPM), in South Korea has presented remarkable advancements in pavement management since the early 2000. Based on these results, visual distress on pavement surfaces can be easily detected and analyzed. Additionally, the entire expressway pavement surface conditions in South Korea can be easily monitored using the current graphical user interface-based advanced information graphic (AIG) approach. Therefore, a critically negative pavement section can be detected and managed more easily and efficiently. However, the actual mechanical performance of the selected pavement layer still needs to be investigated in a more thorough manner not only to provide more accurate pavement performance results but also to verify the feasibility of the current 3DPM and AIG approaches. In this study, the low-temperature performance of the selected asphalt pavement layer section was evaluated to further verify and strengthen the feasibility of the current 3DPM and AIG approaches developed by the Korea Expressway Corporation. METHODS : Based on 3DPM and AIG approach, the positive and negative-riding-quality road sections were selected, respectively. The asphalt material cores were extracted from each section then bending beam rheometer mixture creep test was performed to measure their low-temperature properties. Based on the experimental results, thermal stress results were computed and visually compared. RESULTS : As expected, the asphalt material from the negative driving performance section presented a poorer low-temperature cracking resistance than that from the positive driving performance section. CONCLUSIONS : Current 3DPM equipment can successfully evaluate expressway surface conditions and the corresponding material performance quality. However, more extensive experimental studies are recommended to verify and strengthen the findings of this study

PURPOSES : The aim of this study is to evaluate the effects of air voids, binder content, and aggregate gradation on the indirect tensile strength (IDT) and cracking tolerance index (CTindex) of cored asphalt pavements. METHODS : Cored samples were obtained from roads in Incheon city, and several laboratory experiments were performed. First, the cored samples were first to cut into a size appropriate for the IDT test. Subsequently, the air voids of the samples were measured. The damaged sample from the IDT test was loose mixed at 150 ℃ before the binder content was determined, which was conducted via an asphalt extraction test. Finally, the clean aggregates obtained from asphalt extraction process were analyzed in the aggregate gradation test. RESULTS : The result shows that an increase in air voids from 4% to 8% decreases the IDT and cracking tolerance index (CTindex) by 30% and 28%, respectively. Incorporating a binder enhances the ductile behavior of the asphalt mixture, resulting in a higher CTindex. Finally, the contribution of the aggregate grade on the IDT and CTindex is negligible. CONCLUSIONS : The IDT and CTindex are primarily affected by the air voids and binder content. A higher percentage of air voids results in a lower IDT. In addition, a higher amount of binder increases the IDT and CTindex of the cored samples. Meanwhile, the aggregate grade does not affect the IDT.

PURPOSES : Local governments in Korea, including Incheon city, have introduced the pavement management system (PMS). However, the verification of the repair time and repair section of roads remains difficult owing to the non-existence of a systematic data acquisition system. Therefore, data refinement is performed using various techniques when analyzing statistical data in diverse fields. In this study, clustering is used to analyze PMS data, and correlation analysis is conducted between pavement performance and influencing factors. METHODS : First, the clustering type was selected. The representative clustering types include K-means, mean shift, and density-based spatial clustering of applications with noise (DBSCAN). In this study, data purification was performed using DBSCAN for clustering. Because of the difficulty in determining a threshold for high-dimensional data, multiple clustering, which is a type of DBSCAN, was applied, and the number of clustering was set up to two. Clustering for the surface distress (SD), rut depth (RD), and international roughness index (IRI) was performed twice using the number of frost days, the highest temperature, and the average temperature, respectively. RESULTS : The clustering result shows that the correlation between the SD and number of frost days improved significantly. The correlation between the maximum temperature factor and precipitation factor, which does not indicate multicollinearity, improved. Meanwhile, the correlation between the RD and highest temperature improved significantly. The correlation between the minimum temperature factor and precipitation factor, which does not exhibit multicollinearity, improved considerably. The correlation between the IRI and average temperature improved as well. The correlation between the low- and high-temperature precipitation factors, which does not indicate multicollinearity, improved. CONCLUSIONS : The result confirms the possibility of applying clustering to refine PMS data and that the correlation among the pavement performance factors improved. However, when applying clustering to PMS data refinement, the limitations must be identified and addressed. Furthermore, clustering may be applicable to the purification of PMS data using AI.

PURPOSES : In this study, surface distress (SD), rutting depth (RD), and international roughness index (IRI) prediction models are developed based on the zones of Incheon and road classes using regression analysis. Regression analysis is conducted based on a correlation analysis between the pavement performance and influencing factors. METHODS : First, Incheon was categorized by zone such as industrial, port, and residential areas, and the roads were categorized into major and sub-major roads. A weather station triangle network for Incheon was developed using the Delaunay triangulation based on the position of the weather station to match the road sections in Incheon and environmental factors. The influencing factors of the road sections were matched Based on the developed triangular network. Meanwhile, based on the matched influencing factors, a model of the current performance of the road pavement in Incheon was developed by performing multiple regression analysis. Sensitivity analysis was conducted using the developed model to determine the influencing factor that affected each performance factor the most significantly. RESULTS : For the SD model, frost days, daily temperature range, rainy days, tropical nights, and minimum temperatures are used as independent variables. Meanwhile, the truck ratio, freeze–thaw days, precipitation days, annual temperature range, and average temperatures are used for the RD model. For the IRI model, the maximum temperature, freeze–thaw days, average temperature, annual precipitation, and wet days are used. Results from the sensitivity analysis show that frost days for the SD model, precipitation days and freeze–thaw days for the RD model, and wet days for the IRI model impose the most significant effects. CONCLUSIONS : We developed a road pavement performance prediction model using multiple regression analysis based on zones in Incheon and road classes. The developed model allows the influencing factors and circumstances to be predicted, thus facilitating road management.

PURPOSES : In this study, emissions from asphalt mixture production and construction processes are calculated and used to estimate the emission from each asphalt pavement layer. The calculated emissions for the processes are used as fundamental data to estimate the total emission from the entire life cycle of pavement engineering in South Korea. METHODS : A design proposal and the Korean standard, which provide quantitative information for activities, were used to estimate the amount of construction materials and energy consumption. Subsequently, the LCI DB from NAPA and the LCIA DB from EPA were utilized in conjunction with the estimated quantity to assess the effect of the emissions to determine their environmental impact categories. RESULTS : Calculation results show that 5.84 million ton of CO2eq is discharged from production and construction processes, whereas 3.24 million ton of CO2eq is discharged from operation processes in the pavement engineering sector. The total GHG emission, i.e., 9.08 million ton of CO2eq, is approximately 1.25% of the national GHG emission in 2018. The asphalt mixture production process results in the highest GHG emission in the life cycle of asphalt pavements. CONCLUSIONS : An LCI DB that accounts for the industrial characteristics of South Korea must be established to provide more reliable emission data to be used for national GHG reduction plans, including those for the pavement engineering sector.

PURPOSES : A mechanistic-empirical (ME) predictive design logic that can compute the reflective cracking life of hot-mix asphalt (HMA) overlaid on top of a composite pavement is proposed herein. METHODS : The overlay thickness design and analysis logic of the HMA were formulated based on the ME concept of reflection crack propagation. Climate data, traffic load data, the pavement material properties, and the thickness of each layer of the pavement are the main inputs for the ME-Reflective Cracking Rate (RCR) prediction algorithm. An Microsoft Excel Virtual Basic for Application (VBA) program was created to aid designers in assessing the expected performance of an HMA overlay design. Calibration was done using data from the Long-Term Pavement Performance (LTPP) sections. Sensitivity analysis was conducted to compare the results yielded by the program and data from a report by the Texas Transportation Institute. RESULTS : The predictive model performance effectively generates the dynamic and relaxation modulus curves. The correlation value of the calibration factors, R2, is 0.79. The calibration factors used for the Asphalt Overlay Thickness Design (AOTD) program and the sensitivity analysis, i.e., k1, k2,, and k3,, are set to 5, 5, and 150, respectively. The sensitivity of the AOTD program affords reasonable results. Additionally, the program yields results similar to the trends presented in a report by the Federal Highway Administration. CONCLUSIONS : The proposed ME design logic is successfully translated into an Excel VBA program, AOTD, which can perform routine assessments of laboratory tests for HMA overlays. The program can effectively perform numerous iterations and computations to predict an HMA overlay. The predictive model can generate reasonable dynamic modulus and relaxation modulus curves for the characterization of HMA overlays. Under the same asphalt binder grade and HMA type, doubling the HMA overlay thickness yields three times the expected reflective cracking service life.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.