Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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The statistical factors affecting the freezing of the road pavement

도로포장체의 동결에 영향을 미치는 통계적 요인

  • Kim, Hyun-Ji (Department of Statistics, Yeungnam University) ;
  • Lee, Jea-Young (Department of Statistics, Yeungnam University) ;
  • Kim, Byung-Doo (Department of Liberal Arts in Engineering, Kyungil University) ;
  • Cho, Gyu-Tae (Department of Civil Engineering, Yeungnam University)
  • 김현지 (영남대학교 통계학과) ;
  • 이제영 (영남대학교 통계학과) ;
  • 김병두 (경일대학교 자연계열자율전공학과) ;
  • 조규태 (영남대학교 건설시스템공학과)
  • Received : 2015.11.09
  • Accepted : 2016.01.04
  • Published : 2016.01.31
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Abstract

Due to the character of the climate of Korea, the pavement of a road is Influenced by freezing in winter season and thawing in thawing season. In the last few years, several articles have been devoted to the study to minimize the damage of freezing and thawing action. The purpose of this paper is to identify appropriacy of factors that influence road pavement thickness. We conduct the decision tree analysis on the field data of road pavement. The target variable is 'Frost penetration'. This value was calculated from the temperature data. The input variables are 'Region', 'Type of road pavement', 'Anti-frost layer', 'Month' and 'Air temperature'. The region was divided into 9 regions by freezing index $350{\sim}450^{\circ}C{\cdot}day$, $450{\sim}550^{\circ}C{\cdot}day$, $550{\sim}650^{\circ}C{\cdot}day$. The type of road pavement has three-section such as area of cutting, boundary area of cutting and bankin, lower area of banking. As the result, the variables that influence 'Frost penetration' are Month, followed by anti-frost layer, air temperature and region.

우리나라의 기후적 특성으로 토지가 동절기에는 동결작용을 받고 해빙기에는 융해작용을 받는다. 이러한 동결융해작용의 피해를 최소화하기 위하여 도로 포장 분야에서는 도로 포장 두께에 관한 많은 연구들이 진행 중에 있다. 본 연구는 도로포장 현장계측 온도데이터를 이용하여, 도로 포장 두께를 위해 사용되는 변수들의 적절성을 확인하기 위해 엔트로피 지수를 이용한 의사결정나무 분석을 시행한다. 각 층 온도를 이용하여 생성한 최저동결층 (순서형 변수)을 목표변수로 분석하며, 분석에 사용된 독립변수는 동결지수에 따라 $350{\sim}450^{\circ}C{\cdot}$일, $450{\sim}550^{\circ}C{\cdot}$일, $550{\sim}650^{\circ}C{\cdot}$일로 구분된 지역 변수, 절토부, 절성경계부, 2m 이하 성토부 구간으로 이루어진 단면 변수 그리고 동상방지층 설치 유무이다. 또한, 월과 대기온도도 독립변수에 포함시킨다. 그 결과, 월, 동상방지층 유무, 대기온도, 지역 순으로 최저동결층에 영향을 주는 것으로 나타났다.

Keywords

References

  1. Berson, A., Smith, S. and Thearling, K. (2000). Building data mining applications for CRM, McGraw-Hill, New York.
  2. Freund, Y. and Mason, L. (1999). The alternating decision tree learning algorithm. In ICML, 99, 121-133.
  3. Heo, M. H. and Lee, Y. G. (2008). Data mining modeling and example, Hannarae, Seoul.
  4. Kwon, G. B. (2003). Bearing capacity estimation of subgrade for frost heaving effect, Incheon National University, Incheon.
  5. Kim, N. S., Nam, Y. K., Cho, G. T. and Lee, B. W. (2011). An establishment of database for effective design of anti-frost heave layer using field data. Korean Society of Hazard Mitigation, 11, 43-47. https://doi.org/10.9798/KOSHAM.2011.11.3.043
  6. Kim, Y. J., Yu, J. and Kim, H. M. (1999). A study on the frost penetration depth and insulation methods in pavement, Korea Institute of Civil Engineering and Building Technology, Goyang.
  7. Lee, J. Y. and Kim, H. J. (2014). Identification of major risk factors association with respiratory diseases by data mining. Journal of the Korean Data & Information Science Society, 25, 373-384. https://doi.org/10.7465/jkdi.2014.25.2.373
  8. Lee, M. S., Heo, T. Y., Park, H. M. and Kim, B. I. (2012). Development of model for structural evaluation of anti-freezing layer. Journal of the Korean Society of Road Engineers, 14, 25-32.
  9. Quinlan, J. R. (1993). C4.5: Programs for machine learning, Morgan-Kaufmann Publishers, San Mateo, CA.
  10. Shin, E. C., Lee, J. S. and Cho, G. T. (2011). A study on the frost penetration depth of pavement with field temperature data. Journal of the Korean Society of Road Engineers, 13, 21-32.
  11. Nam, Y. K., Park, C. B., Cho, G. T. and Jin, J. H. (2002). The field data on behavior characteristics of anti-frost heave layer. International Journal of Highway Engineering, 4, 19-23.