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최적 부분집합 회귀분석을 활용한 강원도 내 일반국도 표면결함 변화량 예측모형 개발
우병찬,이수형,나계주,정진훈 한국도로학회 2022 한국도로학회논문집 Vol.24 No.6
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.
CODARTS 방법론을 지원하는 실시간 S / W 설계 지원 시스템의 설계
우병찬(Woo Byung-Chan),김규년(Kim Kyoo-Nyun) 한국정보과학회 1998 한국정보과학회 학술발표논문집 Vol.25 No.2Ⅰ
COncurrent Design Approach for Real-Time System(이하 CODARTS)방법론은 Gomaa가 제안한 실시간 설계 방법론으로서 Real-Time Structured Analysis(이하 RTSA)또는 Concurrent Object-Based Real-Time Analysis(이하 COBRA)방법론을 이용하여 Control and Data Flow Diagram(이하 C&DFD)를 구성하고 이것에 병렬 태스크 구조화 지침, 정보 은닉 모듈 구조화 지침을 적용하여 Task Architecture Diagram(이하 TAD), Information Hiding Module(이하 IHM)을 구성하고 나서 이 둘을 결합하여 Software Architecture Diagram(이하 SAD)를 구성하게 된다. 본 논문에서는 CODARTS 방법론의 적용과정을 테이블을 구성하여 적용함으로써 실시간 S/W 설계 지원 시스템을 설계하였다.
지역 특성에 따른 강원도 내 일반국도 아스팔트 포장의 평탄성 변화
이재훈,이재훈,우병찬,이수형,김연태,정진훈 한국도로학회 2023 한국도로학회논문집 Vol.25 No.2
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.