Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Predicting Highway Concrete Pavement Damage using XGBoost

XGBoost를 활용한 고속도로 콘크리트 포장 파손 예측

  • Lee, Yongjun (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Sun, Jongwan (Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology)
  • 이용준 (한국건설기술연구원 인프라안전연구본부 도로관리통합센터) ;
  • 선종완 (한국건설기술연구원 인프라안전연구본부 도로관리통합센터)
  • Received : 2020.08.25
  • Accepted : 2020.09.17
  • Published : 2020.11.30
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Abstract

The maintenance cost for highway pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance Preventive maintenance requires the establishment of a strategic plan through accurate prediction old Highway pavement. herefore, in this study, the XGBoost among machine learning classification-based models was used to develop a highway pavement damage prediction model. First, we solved the imbalanced data issue through data sampling, then developed a predictive model using the XGBoost. This predictive model was evaluated through performance indicators such as accuracy and F1 score. As a result, the over-sampling method showed the best performance result. On the other hand, the main variables affecting road damage were calculated in the order of the number of years of service, ESAL, and the number of days below the minimum temperature -2 degrees Celsius. If the performance of the prediction model is improved through more data accumulation and detailed data pre-processing in the future, it is expected that more accurate prediction of maintenance-required sections will be possible. In addition, it is expected to be used as important basic information for estimating the highway pavement maintenance budget in the future.

도로연장의 지속적인 증가와 공용기간이 상당히 경과한 노후 노선이 늘어남에 따라 도로포장에 대한 유지관리비용은 점차 증가하고 있어, 예방적 유지관리를 통해 비용을 최소화 하는 방안에 대한 필요성이 제기되고 있다. 예방적 유지관리를 위해서는 도로포장의 정확한 파손 예측을 통한 전략적 유지관리 계획 수립이 필요하다. 이에 본 연구에서는 고속도로 콘크리트 포장 파손 예측 모델 개발을 위해 머신러닝 분류기반 모델 중 성능이 우수한 XGBoost 기법을 사용하였다. 먼저 데이터 샘플링을 통해 데이터 불균형 문제를 해결하고 샘플링된 데이터들에 XGBoost 기법을 활용하여 예측모델을 개발하고. F1 소코어를 통해 성능을 평가하였다. 분석 결과 오버 샘플링 기법이 가장 좋은 성능 결과를 보였으며, 도로파손에 영향을 주는 주요 변수로 공용년수, ESAL, 최저 평균 최저기온 -2도 이하 일수 순으로 산정되었다. 향후 더 많은 데이터 축적 및 세밀한 데이터 전처리 작업을 통해 예측모델의 성능이 향상된다면 보다 정확한 유지보수 필요 구간의 예측이 가능해질 것으로 판단되므로 장래 고속도로 포장 유지보수 예산의 추정에 중요한 기초정보로 활용될 수 있을 것이라 기대된다.

Keywords

References

  1. Batista, G.E.A.P.A., Ronaldo C. Prati and Maria Carolina Monard (2004). "A study of the behavior of several methods for balancing machine learning training data." SIGKDD Explorations, 6(1), pp. 20-29. https://doi.org/10.1145/1007730.1007735
  2. Dennis L., Wilson (1972). "Asymptotic properties of nearest neighbor rules using edited data." IEEE Transactions on Systems, Man, and Cybernetice, 3, pp. 408-421. https://doi.org/10.1109/TSMC.1972.4309137
  3. Do, M., Lee, Y., Lim, K., and Kwon, S. (2011). "Estimation of Performance and Pavement Life using National Highway Pavement Condition Index." KSCE Journal of Civil Engineering, 15(2), pp. 261-270. https://doi.org/10.1007/s12205-011-1065-4
  4. Gharaibeh, N., and Darter, M. (2003). "Probabilistic analysis of highway pavement life for Illinois." Transportation Research Record 1823, No. 03-4294, pp. 111-120.
  5. Gong, H., Sun, Y., and Huang, B. (2019). "Gradient Boosted Models for Enhancing Fatigue Cracking Prediction in Mechanistic-Empirical Pavement Design Guide." Journal of Transportation Engineering, Part B: Pavementsm, 145(2), 04019014-1-04019014-10. https://doi.org/10.1061/JPEODX.0000121
  6. Han, D., Do, M., and Kim, B. (2017). "Internal Property and Stochastic Deterioration Modeling of Total Pavement Condition Index for Transportation Asset Management." International Journal of Highway Engineering, 19(5), pp. 1-11. https://doi.org/10.7855/IJHE.2017.19.5.001
  7. Ju, Huyan (2019). "Development of Machine Learning Based Analytical Tools for Pavement Performance Assessment and Crack Detection." Doctoral Thesis.
  8. Ko, M. (2020). "Climate Changes and the Future of the Environment." Archives of 21st KIPA Public Leadership Seminar, pp. 1-58.
  9. Korea Expressway Corporation (2010). "Guideline for Exposure to Environment."
  10. Korea Expressway Corporation (2018). "2017 Investigation and analysis of highway pavement condition."
  11. Kwon, S., Jeong, K., and Sun, Y. (2012). "A Study on Decision Criteria of traffic volumes for Choosing of Modified Asphalt Pavement in Korea National Highway." International Journal of Highway Engineering, 4(3), pp. 25-33.
  12. Lee, Y. (2013). "A Study on the Method of Establishing Road Maintenance Strategy Considering the Forecasting Traffic Demand." Master Thesis.
  13. Lee, Y., and Lee, M. (2016). "A Study on Estimating of Probability Distribution and Mean Life of Bridge Member for Effective Maintenance of the Bridge." Korea Journal of Construction Engineering and Management, KICEM, 17(4), pp. 57-65. https://doi.org/10.6106/KJCEM.2016.17.4.057
  14. Lee, Y., Sun, J., and Lee, M. (2019). "Development of Deep Learning Based Deterioration Prediction Model for the Maintenance Planning of Highway Pavement." Korea Journal of Construction Engineering and Management, KICEM, 20(6), pp. 34-43.
  15. Lim, S., and Chi, S. (2019). "Xgboost application on bridge management systems for proactive damage estimation." Advanced Engineering Informatics, 41, pp. 1-14.
  16. Loizos, A., and Karlaftis, M.G. (2005). "Prediction of pavement crack initiation from in-service pavements: A duration model approach." Journal of the Transportation Research Board, 1940, TRB, pp. 38-42. https://doi.org/10.1177/0361198105194000105
  17. Moon, K., You, T., Kim, J., and Park, J. (2017). "An Application Study on Selecting Proper and Otimized Sections for Remodeling of Aged Pavement." Expressway & Transportation Research Institute, Report No. KECRI-2017-32-534.9607.
  18. Kohavi, R., and John, G.H. (1997). "Wrappers for feature subset selection." Artificial Intelligence, 97, Issues1-2, pp. 273-324. https://doi.org/10.1016/S0004-3702(97)00043-X
  19. Chen, T., and Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." KDD'16, pp. 785-794.
  20. Yang, J., Gunaratne, M., Lu, J.J., and Dietrich, B. (2005). "Use of recurrent Markov chains for modeling the crack performance of flexible pavements." Journal of Transportation Engineering, 131(11), pp. 861-872. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:11(861)
  21. You, P., and Lee, D. (2002). "Methodology of a Probabilistic Pavement Performance Prediction Model Based on the Markov Process." International Journal of Highway Engineering, 4(4), pp. 1-12.