PURPOSES : In this study, the alkali aggregate reactivity and expansion characteristics of mortar mixed with waste glass (a recycled aggregate) were confirmed to verify the alkali-silica reaction (ASR) stability and review the appropriateness of the alkali aggregate reactivity test method following the replacement of recycled aggregate. METHODS : The alkali-aggregate reactivity of waste glass aggregates was measured using the chemical and physical methods described in KS F 2545 and ASTM C 1260, respectively. The reactivity was classified by comparing the results. Cement with a high-alkali content was used to simulate an environment that can induce ASR. Non-reactive fine aggregates, waste glass fine aggregates, reactive general aggregates, and Ferronickel slag aggregates were used as control groups. RESULTS : Waste glass fine aggregates were classified as reactive when applying the chemical method. In the physical method, they were classified as reactive at 100% and latent reactive at 1%, based on the mixing ratio. Additionally, we discovered that the reliability of the chemical method was low since the ASR of the aggregates was classified differently based on the evaluation method, while the results of the chemical and physical test methods were inconsistent. CONCLUSIONS : To determine the alkali reactivity of recycled aggregates, the complex use of chemical and physical methods and analysis based on the mixing ratio of the reactive aggregates are required. Small amounts of waste glass aggregate replacements affected the ASR. Because ASR reaction products can affect the long-term thermal expansion of the structure, further research is needed to use ASR aggregates in structures.

PURPOSES : The initial smoothness of concrete pavement surfaces must be secured to ensure better driving performance and user comfort. The roughness was measured after hardening the concrete pavement in Korea. When the initial roughness is poor, relatively large-scale repair works, such as milling or reconstruction must be performed. Hence, a method to measure the roughness of the concrete pavements in realtime during construction and immediately correct the abnormal roughness was developed in this study. METHODS : The profile of a concrete pavement section was measured at a construction site using sensors that were attached to the tinning equipment of the paver. The measured data included outliers and noise caused by the sensor and vibration of the paving equipment, respectively, which were further calibrated. Consequently, the calibrated data were input into the ProVAL program to calculate the roughness based on the international roughness index (IRI). Additionally, the profile of the section was re-measured using another method to verify the reliability of the calculated IRI. RESULTS : The profile data measured at the concrete pavement construction site were calibrated using methods, such as overlapped boxplot outlier removal and low-pass filtering. The outlier data from the global positioning system (GPS), which was installed to identify the construction distance, was also calibrated. The IRI was calculated using the ProVAL program by matching the measured profile and GPS data, and applying the moving average method. The calculated IRI was compared to that measured using another method, and the difference was within the tolerance. CONCLUSIONS : A method to measure the roughness of the concrete pavements in real time during construction was developed in this study. Hence, the performance of concrete pavements can be improved by enhancing the roughness of the pavement considerably using the aforementioned method.

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 : The purpose of this study is to confirm the thermal expansion characteristics of concrete mixed with 1% waste glass fine aggregates, which is the amount stipulated for recycled aggregates in the current quality standard. METHODS : The coefficient of thermal expansion was measured by applying AASHTOT 336-10 using a LVDT. The results measured were used as physical properties in a finite element analysis to confirm the change in tensile stress and the displacement of the right angle section of the upper slab of a concrete pavement due to admixture substitution. RESULTS : The thermal expansion coefficients of concrete based on the replacement rate of the admixture when the waste glass fine aggregates are replaced are within the range of the thermal expansion coefficients of concrete specified in the Federal Highway Administration report. As the replacement rate of the admixture increases, the thermal expansion coefficient of concrete decreases. As the thermal expansion coefficient decreases, the slab pavement curling displacement and the tensile stress of the center of the upper slab of concrete decrease. CONCLUSIONS : In the short term, the presence or absence of waste glass fine aggregates does not significantly affect the thermal expansion coefficient of concrete. However, in the long term, waste glass fine aggregates are reactive aggregates that causes ASR, which creates an expandable gel around the aggregates and results in concrete expansion. Therefore, the relationship between ASR and the thermal expansion coefficient must be analyzed in future studies.

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 : 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.

PURPOSES : The objective of this study is to develop regression models for surface distress (SD), rut depth (RD), and international roughness index (IRI) of Jeju Island local road by analyzing the correlations between the pavement performance and its influencing factors. METHODS : First, the differences between pavements in inland Korea and Jeju Island in terms of performance and influencing factors were investigated. Influencing factors were assigned to pavement sections on Jeju Island using the inverse distance weighting method, and the correlations between the pavement performance and influencing factors were analyzed. As a result, maximum temperature, heat wave days, annual temperature range, precipitation days, precipitation intensity, ESAL, etc. were determined as independent variables for the pavement performance prediction models. Multiple regression analysis was performed to develop the pavement performance models using the selected independent variables. RESULTS : The RD, maximum temperature, and precipitation days were determined to be the independent variables for the SD predictive model. The SD, maximum temperature, annual temperature range, heat wave days, and precipitation days were selected as independent variables of the RD prediction model. In addition, the RD, annual temperature range, heat wave days, precipitation days, and ESAL were selected as independent variables for the IRI prediction model. CONCLUSIONS : As a result of the study, an actual forecast model for SD, RD, and IRI was developed. Based on this model, it is possible to estimate the predictive value of the missing performance data in the studied interval. If the factors affecting performance are managed in terms of maintenance beyond a certain level, it can help those responsible for road maintenance to rationally select the maintenance method and timing.

PURPOSES : The surface distress of asphalt pavements is one of the major factors affecting the safety of road users. The aim of this study was to analyze the factors influencing the occurrence of surface distress and statistically predict its annual change to contribute to more reasonable asphalt pavement management using the data periodically collected by the national highway pavement data management system. METHODS : In this study, the factors that were expected to influence the surface distress were determined by reviewing the literature. The normality was secured by changing the forms of the variables to make the distribution of the variables got closer to normal distribution. In addition, min-max normalization was performed to minimize the effect of the unit and magnitude of the candidate independent variables on the dependent variable. The final candidate independent variables were determined by analyzing the correlation between the annual surface distress change and each candidate independent variable. In addition, a prediction model was developed by performing data grouping and multi-regression analysis. RESULTS : An annual surface distress change prediction model was developed using present surface distress, age, and below 0 ℃ days as the independent variables. As a result of sensitivity analysis, the surface distress affected the annual surface distress change the most. The positive correlation between the dependent variable and each independent variable demonstrated engineering and statistical meaningfulness of the prediction model. CONCLUSIONS : The surface distress in the future can be predicted by applying the annual surface distress prediction model to the national highway asphalt pavement sections with survey data. In addition, the prediction model can be applied to the national highway pavement condition index (NHPCI) evaluating the national highway asphalt pavement conditions to be used in the prediction of future NHPCI.