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      최적 부분집합 회귀분석을 활용한 강원도 내 일반국도 표면결함 변화량 예측모형 개발 = Development of Annual Surface Distress Change Prediction Model for National Highway Asphalt Pavements in Gangwon-do Using Best Subset Regression Analysis

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      https://www.riss.kr/link?id=A108386014

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      다국어 초록 (Multilingual Abstract)

      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.
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      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 ...

      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.

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      참고문헌 (Reference)

      1 이재훈 ; 이재훈 ; 김연태 ; 이강훈 ; 엄병식 ; 우병찬 ; 정진훈, "제주도 지방도 포장의 공용성에 영향을 미치는 인자의 분석" 한국도로학회 24 (24): 39-51, 2022

      2 이재훈 ; 임재규 ; 최문규 ; 정진훈, "일반국도 아스팔트 포장 연간 소성변형 깊이 변화량 예측 모형 개발" 한국도로학회 23 (23): 11-19, 2021

      3 서영찬 ; 권상현 ; 정동혁 ; 정진훈 ; 강민수, "고속도로 PMS D/B를 활용한 콘크리트 포장 상태지수(HPCI) 예측모델 개발 연구" 한국도로학회 19 (19): 83-95, 2017

      4 Melesse, A. M, "Suspended Sediment Load Prediction of River Systems: AnArtificial Neural Network Approach" 98 : 855-866, 2011

      5 Freedman, D. R, "Statistics" WW Norton & Company 2007

      6 Kline, R. B, "Principles and Practice of Structural Equation Modeling" Guilford Press 2005

      7 Farrar, D. E, "Multicollinearity in Regression Analysis: The Problem Revisited" 49 (49): 92-107, 1967

      8 Ministry of Land, Transport and Maritime Affairs (MLTMA), "Manual of Pavement Structural Design" 2011

      9 Kock, N, "Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations" 13 (13): 546-580, 2012

      10 Özgan, E, "Investigation of certain engineering characteristics of asphalt concrete exposed to freeze–thaw cycles" 85 : 131-136, 2013

      1 이재훈 ; 이재훈 ; 김연태 ; 이강훈 ; 엄병식 ; 우병찬 ; 정진훈, "제주도 지방도 포장의 공용성에 영향을 미치는 인자의 분석" 한국도로학회 24 (24): 39-51, 2022

      2 이재훈 ; 임재규 ; 최문규 ; 정진훈, "일반국도 아스팔트 포장 연간 소성변형 깊이 변화량 예측 모형 개발" 한국도로학회 23 (23): 11-19, 2021

      3 서영찬 ; 권상현 ; 정동혁 ; 정진훈 ; 강민수, "고속도로 PMS D/B를 활용한 콘크리트 포장 상태지수(HPCI) 예측모델 개발 연구" 한국도로학회 19 (19): 83-95, 2017

      4 Melesse, A. M, "Suspended Sediment Load Prediction of River Systems: AnArtificial Neural Network Approach" 98 : 855-866, 2011

      5 Freedman, D. R, "Statistics" WW Norton & Company 2007

      6 Kline, R. B, "Principles and Practice of Structural Equation Modeling" Guilford Press 2005

      7 Farrar, D. E, "Multicollinearity in Regression Analysis: The Problem Revisited" 49 (49): 92-107, 1967

      8 Ministry of Land, Transport and Maritime Affairs (MLTMA), "Manual of Pavement Structural Design" 2011

      9 Kock, N, "Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations" 13 (13): 546-580, 2012

      10 Özgan, E, "Investigation of certain engineering characteristics of asphalt concrete exposed to freeze–thaw cycles" 85 : 131-136, 2013

      11 Ministry of Land, Infrastructure and Transport (MOLIT), "Guide for Road Pavement Structures Design" 2015

      12 Korea Meteorological Administration (KMA), "Gangwon-Do Climate Change Forecast Report" 2017

      13 Stephan, M, "Exploratory Data Analysis" 1 (1): 33-44, 2009

      14 Lim, J. G, "Development of Condition Predection Model for Asphalt Pavement Using National Highway PMS Database" Inha University 2021

      15 Ministry of Land, Infrastructure and Transport (MOLIT), "Detailed Guidelines for Safety and Maintenance of Facilities" 2020

      16 Kim, D. H, "A Study on the Strategy Establishment of Remodeling Repair for Expressway Asphalt Pavements based on Big Data Analysis" University of Inha 2022

      17 Fakhitah, R, "A Review on Data Cleansing Methods for Big Data" 161 : 731-738, 2019

      18 Ministry of Land, Infrastructure and Transport (MOLIT), "2019 PMS Final Report" 2019

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