The experiment aim to breed an early-maturing variety of Italian ryegrass (Lolium multiflorum Lam.). It was conducted at the Grassland and Forage Crops Division, National Institute of Animal Science, RDA, Cheonan, Korea, from 2022 to 2023. The new variety named “Oasis” is a diploid with green leaves and has a semi-erect growth habit in late fall, and an erect growth habit in early spring. With a heading date of May 10, Oasis is categorized as an early-maturing variety. Compared with the “Florida80” as a control variety, Oasis’s flag leaf was 1 mm wider and 2 cm shorter, while the plant length was 7.8 cm longer. Additionally, the ear of Oasis was 1.2 cm longer than that of Florida80, and it showed lodging resistance. The dry matter yield of Oasis (8,805 kg/ha) was higher than that of Florida80. The total digestible nutrient and crude protein contents of Oasis were 64.5%, and 9.3%, respectively, which were 1.1% and 0.6% higher than those of Florida80, respectively. The neutral and acid detergent fiber contents in Oasis were 54.2% and 30.8%, respectively, which was 1.9% and 1.4% lower than those of Florida80, respectively. These results indicate that Oasis has good dry matter yield in the most of region and especially, middle and northern region of South Korea.

PURPOSES : The purpose of this study was to improve the performance of concrete pavements by measuring the unit-water content with an Internet-of-Things (IoT)-based unit-water content measurement device at an increased precision compared with that of existing measuring equipment. METHODS : We calculated the properties of concrete that varied according to variations in the unit-water content. To confirm the change in the performance of concrete pavements, we compared and analyzed the fatigue cracking rate and international roughness index of concrete pavements at the 20-year point of public use according to the changes in properties using the Korea Pavement Research Program(KPRP). RESULTS : We confirmed that IoT-based measurement equipment can improve the performance of pavements without changing their mixing designs or specifications. We confirmed that the performance of the concrete pavements changed significantly, even with unit-water content variations within the range of quality standards. According to IoT-based unit-water content management, we confirmed that the performance of the concrete pavement (fatigue cracking rate and international roughness index) improved without changing the mixing design. CONCLUSIONS : We confirmed that by using IoT-based unit-water content management, pavement performance can be improved. It is necessary to consider whether the application of this concept to other concrete property management items, such as the chloride content, is possible. Considering the changes in concrete properties according to the unit-water content based on laboratory tests and considerations of various load conditions will be necessary for future research.

PURPOSES : For most local governments, including that of Gangwon-do, the establishment of an organized pavement management system is insufficient, resulting in problems such as inefficient distribution and use of maintenance budgets for deteriorated road pavements. In this study, we aimed to contribute to the establishment of a more reasonable road maintenance strategy by developing a model for predicting the annual international roughness index (IRI) change for national highway asphalt pavements in Gangwon-do based on big data analysis. METHODS : Data on independent and dependent variables used for model development were collected. The collected data were subjected to exploratory data analysis (EDA) and data preprocessing. Independent variable candidates were selected to reduce multicollinearity through correlation analysis and specific conditions. A final model was selected, and sensitivity analysis was performed. RESULTS : The final model that predicts annual IRI change uses independent variables such as annual temperature range, minimum temperature, freeze-thaw days, IRI, surface distress (SD), and freezing days. The sensitivity analysis confirmed that the annual IRI change was affected in the order of annual temperature range, minimum temperature, freeze-thaw days, IRI, SD, and freezing days. CONCLUSIONS : Road maintenance can be performed rationally by predicting future pavement conditions using the model developed in this study. The accuracy of the prediction model can be improved if additional data, such as material properties and pavement thickness, are obtained in future studies.

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 : 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.

최근 AI 기술은 하루가 다르게 빠르게 발전하고 있고, AI기술은 각 분야에서 다양하게 사용되어지고 있다. 본 논문은 예술분야에서 AI기술의 활용으로 COVID-19 상황에서 인간관계, 개인적인 이유로 지친 마음을 위 로해주는 힐링 게임을 제작하였다. 제작한 힐링게임에서는 주로 Self-help-therapy의 효과를 얻을 수 있어, 치 료자의 도움없이 이용자가 힐링게임을 통하여 일상적 이용과정에서 치유적 효과를 얻을 수 있는 것을 기대 하고 있다. 게임 리뷰 데이터를 통계 분석하여 힐링게임으로 대중들이 요구하는 부분을 수용하여 힐링게임 이 제작되었으며, 사용자는 게임 시작 전 간단한 스토리라인과 AI와 상호작용할 수 있는 간단한 대화를 통 화여 Self-help-therapy 효과를 얻을 수 있었다.

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.

PURPOSES : On a thin epoxy overlay pavement, epoxy is placed on the existing bridge deck pavement, followed by the spraying of aggregates on it. The bond strength between the existing pavement and overlay pavement is an important factor representing the performance of the thin epoxy overlay pavement, in addition to the skid resistance and roughness. Therefore, the bond strength, skid resistance, and roughness of a thin epoxy overlay pavement constructed for field tests under various field conditions are examined in this study. METHODS : The usability of epoxy and aggregates on a thin epoxy overlay pavement is identified by testing their material properties in a laboratory. A construction test is performed using the pretreatment conditions of the existing pavement surface and the number of layers of overlay pavement as variables. The bond strength, skid resistance, and roughness are analyzed 3 d after constructing the test pavement, and immediately before and after applying repetitive traffic loadings at 6 months. RESULTS : When the existing pavement is in good condition, as in this study, the bond strength of the thin epoxy overlay pavement is affected more significantly by the existing pavement condition than the material properties of epoxy, in which destruction is indicated in the existing pavement. The skid resistance is affected primarily by the condition of the aggregates sprayed on the epoxy. The pavement on which the aggregates are well sprayed indicate a high skid resistance. The roughness is not affected by any variables, such as the pretreatment conditions, number of thin pavement layers, and repetitive traffic loadings. CONCLUSIONS : A long-term evaluation of the bond strength, skid resistance, and roughness will be conducted on a test pavement. In addition, another construction test will be performed to investigate the performance of a thin epoxy pavement overlaid on a bridge deck pavement under inferior conditions.

PURPOSES : Rut depth of asphalt pavements is a major factor that affects the maintenance of pavements as well as the safety of drivers. The purpose of this study was to analyze the factors influencing rut depth, using data collected periodically on national highways by the pavement management system and, consequently, predict annual rut depth change, to contribute to improved asphalt pavement management. METHODS : The factors expected to influence rut depth were determined by reviewing relevant literature, and collecting the related data. Further, the correlations between the annual rut depth change and the influencing factors were analyzed. Subsequently, the annual rut depth change model was developed by performing regression analysis using age, present rut depth, and annual average maximum temperature as independent variables. RESULTS : From the sensitivity analysis of the developed model, it was found that age affected the annual rut depth change the most. Additionally, the relationship between the dependent and independent variables was statistically significant. The model developed in this study could reasonably predict the change in the rut depth of the national highway asphalt pavements. CONCLUSIONS : In summary, it was verified that the model developed in this study could be used to predict the change in the National Highway Pavement Condition Index (NHPCI), which represents comprehensive conditions of national highway pavements. Development of other models that predict changes in surface distress as well as international roughness index is required to predict the change in NHPCI, as they are the independent variables of the NHPCI prediction model.