DOI QR코드

DOI QR Code

Prediction of Life Expectancy of Asphalt Road Pavement by Region

아스팔트 도로포장의 균열률에 대한 지역별 기대수명 추정

  • 송현엽 (국립한밭대학교 도시공학과 대학원) ;
  • 최승현 (국립한밭대학교 SOC자산관리센터) ;
  • 한대석 (한국건설기술연구원 인프라안전연구본부 노후인프라센터) ;
  • 도명식 (국립한밭대학교 도시공학과)
  • Received : 2020.12.01
  • Accepted : 2021.01.14
  • Published : 2021.08.01

Abstract

Since future maintenance cost estimation of infrastructure involves uncertainty, it is important to make use of a failure prediction model. However, it is difficult for local governments to develop accurate failure prediction models applicable to infrastructure due to a lack of budget and expertise. Therefore, this study estimated the life expectancy of asphalt road pavement of national highways using the Bayesian Markov Mixture Hazard model. In addition, in order to accurately estimate life expectancy, environmental variables such as traffic volume, ESAL (Equivalent Single Axle Loads), SNP (Structural Number of Pavement), meteorological conditions, and de-icing material usage were applied to retain reliability of the estimation results. As a result, life expectancy was estimated from at least 13.09 to 19.61 years by region. By using this approach, it is expected that it will be possible to estimate future maintenance cost considering local failure characteristics.

사회기반시설의 장래유지 관리비용 추정은 불확실한 미래를 다루기 때문에 신뢰성 높은 파손예측모델의 구축이 매우 중요하다. 하지만, 지자체에서는 예산, 인력, 파손예측모델의 필요성 등에 대한 인식부족으로 인해 기반시설의 정확한 파손예측모델 개발이 어려운 실정이다. 따라서 본 연구에서는 베이지안 마르코프 혼합해저드 모델을 활용하여 일반국도 아스팔트 도로포장의 균열률에 대한 지역별 기대수명을 추정하였다. 또한 정확한 기대수명 추정을 위하여 교통량, 축하중, 포장강도, 기후, 제설제사용량 등의 환경변수를 적용하여 추정결과에 대한 신뢰성을 확보하고자 하였다. 분석결과, 지역별로 최소 13.09년에서 최대 19.61년의 기대수명이 추정되었다. 본 연구결과를 활용할 경우, 지역적 파손특성이 고려된 신뢰성 높은 장래 유지관리비용의 추정이 가능할 것으로 기대된다.

Keywords

Acknowledgement

본 논문은 2020 CONVENTION 논문을 수정·보완하여 작성되었습니다.

References

  1. Bayes, T. and Price, R. (1763). "An essay towards solving a problem in the doctrine of chance." By the late Revision. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S., Philosophical Transactions of the Royal Society of London, Vol. 53, pp. 370-418. https://doi.org/10.1098/rstl.1763.0053
  2. Choi, S. H., Do, M. S., Han, D. S., Sim, H. J. and Chae, C. D. (2019). "Estimation of road pavements life expectancy by bayesian markov mixture hazard model." International Journal of Highway Engineering, Vol. 21, No. 6, pp. 57-67 (in Korean). https://doi.org/10.7855/ijhe.2019.21.6.057
  3. Do, M. S. (2011). "Comparative analysis on mean life reliability with functionally classified pavement sections." KSCE Journal of Civil Engineering, KSCE, Vol. 15, No. 2, pp. 261-270. https://doi.org/10.1007/s12205-011-1065-4
  4. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments, Handbook of Bayesian Statistics 4, eds. Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M., Clarendon Press, Oxford, UK, pp. 169-193.
  5. Han, D. S. and Do, M. S. (2016). "Evaluation of Socio-environmental effects considering road service levels for transportation asset management." Journal of Testing and Evaluation, Vol. 44, No. 1, pp. 679-691.
  6. Han, D. S., Kaito, K. and Kobayashi, K. (2014). "Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management." KSCE Journal of Civil Engineering, KSCE, Vol. 18, No. 7, pp. 2107-2119. https://doi.org/10.1007/s12205-012-0070-6
  7. Han, D. S., Kaito, K., Kobayashi, K. and Aoki, K. (2016). "Performance evaluation of advanced pavement materials by bayesian markov mixture hazard model." KSCE Journal of Civil Engineering, KSCE, Vol. 20, No. 2, pp. 729-737. https://doi.org/10.1007/s12205-015-0375-3
  8. Han, D. S., Kobayashi, K. and Do, M. S. (2013). "Section-based multifunctional calibration method for pavement deterioration forecasting model." KSCE Journal of Civil Engineering, KSCE, Vol. 17, No. 2, pp. 386-394. https://doi.org/10.1007/s12205-013-1934-0
  9. Hastings, W. K. (1970). "Monte Carlo sampling methods using Markov chains and their applications." Biometrika, Vol. 57, No. 1, pp. 97-109. https://doi.org/10.1093/biomet/57.1.97
  10. Kaito, K., Kobayashi, K., Aoki, K. and Matsuoka, K. (2012). "Hierarchical Bayesian estimation of mixed hazard models." Journal of Civil Engineering, JSCE, Vol. 68, No. 4, pp. 255-271 (in Japanese).
  11. Kim, S. H. and Kim, N. S. (2006). "Development of performance prediction models in flexible pavement using regression analysis method." KSCE Journal of Civil Engineering, KSCE, Vol. 10, No. 2, pp. 91-96. https://doi.org/10.1007/BF02823926
  12. Kobayashi, K., Kaito, K. and Nam, L. T. (2012). "A bayesian estimation method to improve deterioration prediction for infrastructure system with Markov chain model." International Journal of Architecture, Engineering and Construction, Vol. 1, No. 1, pp. 1-13. https://doi.org/10.7492/IJAEC.2012.001
  13. Lancaster, T. (1990). The econometric analysis of transition data, Cambridge University Press, N.Y., USA.
  14. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. and Teller, H. (1953). "Equations of state calculations by fast computing machines." Journal of Chemical Physics, Vol. 21, No. 6, pp. 1087-1091. https://doi.org/10.1063/1.1699114
  15. Obama, K., Okada, K., Kaito, K. and Kobayashi, K. (2008). "Disaggregated hazard rates evaluation and bench-marking." Journal of Civil Engineering, JSCE, Vol. 64, No. 4, pp. 857-874. (in Japanese).
  16. Prozzi, J. A. and Madanat, S. M. (2004). "Development of pavement performance models by combining experimental and field data." Journal of Infrastructure Systems, Vol. 10, No. 1, pp. 9-22. https://doi.org/10.1061/(ASCE)1076-0342(2004)10:1(9)
  17. Train, K. E. (2009). Discrete choice methods with simulation (Second edition), Cambridge University Press, N.Y., USA.
  18. Tsuda, Y., Kaito, K., Aoki, K. and Kobayashi, K. (2006). "Estimating markovian transition probabilities for bridge deterioration forecasting." Journal of Structural Engineering and Earthquake Engineering, JSCE, Vol. 23, No. 2, pp. 241-256.