The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Infrastructure Health Monitoring and Economic Analysis for Road Asset Management : Focused on Sejong City

도로 자산관리를 위한 상태 모니터링 및 경제성 분석 : 세종시를 중심으로

  • 최승현 (한밭대학교 도시공학과) ;
  • 박정권 (한국토지주택공사 경기지역본부 지역균형재생처) ;
  • 도명식 (한밭대학교 도시공학과)
  • Received : 2021.07.28
  • Accepted : 2021.08.10
  • Published : 2021.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

본 연구에서는 세종시 포장도로 구간을 대상으로 모바일매핑시스템(MMS)과 딥러닝 균열 감지시스템을 활용한 도로포장 모니터링 방안을 제시하고 경제성 분석을 통한 최적 유지보수 공법을 제시하였다. 나아가, 기존 대부분의 연구에서는 도로포장 조사 차량에 의해 취득된 데이터를 활용한 사례가 대부분이었으나 본 연구에서는 직접 모니터링 조사를 통해 취득한 도로 포장 상태등급을 기준으로 경제성 분석을 실시하였다. 도로포장 상태 모니터링 결과 일반국도, 시도, 지방도의 순서대로 도로포장 상태가 양호한 것을 확인하였다. 또한 포장파손모델을 활용한 경제성 분석 결과, 예방적 유지보수공법인 마이크로서페이싱 공법을 적용하는 것이 유지보수비용과 이용자 편익 측면에서 가장 경제적인 것으로 나타났다. 본 연구의 성과는 지자체의 기반시설 관리계획 수립을 위한 기초적 자료로 활용될 것으로 기대된다.

Keywords

References

  1. Ai D., Jiang G., Kei L. and Li C.(2018), "Automatic Pixel-Level Pavement Crack Detection Using Information of Multi-Scale Neighborhoods," IEEE Access, vol. 6, pp.24452-24463. https://doi.org/10.1109/ACCESS.2018.2829347
  2. Cha Y., Choi W. and Buyukozturk O.(2017), "Deep learning based crack damage detection using convolutional neural networks," Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 5, pp.361-378. https://doi.org/10.1111/mice.12263
  3. Choi S., Do M., Han D., Sim H. and Chae C.(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. https://doi.org/10.7855/ijhe.2019.21.6.057
  4. Choi S., Do M., You S. and Cho C.(2018), "Determination of Visual Based Asphalt pavement Crack Condition Using Deep Learning," International Journal of Highway Engineering, vol. 20, no. 5, pp.75-83. https://doi.org/10.7855/IJHE.2018.20.5.075
  5. Do M., Kwon S. and Choi S.(2013), "Methodology for Benefit Evaluation according to Maintenance Method and Timing of National Highway Pavement Section," International Journal of Highway Engineering, vol. 15, no. 5, pp.91-99. https://doi.org/10.7855/IJHE.2013.15.5.091
  6. Do M., Kwon S., Lee S. and Kim Y.(2014), "Development of Decision-Making System for National Highway Pavement Management System," J. of the Korean Society of Civil Engineers, vol. 34, no. 2, pp.645-654. https://doi.org/10.12652/Ksce.2014.34.2.0645
  7. Ha J., Park K. and Kim M.(2021), "A Development of Road Crack Detection System Using Deep Learning-based Segmentation and Object Detection," The Journal of Society for e-Business Studies, vol. 26, no. 1, pp.93-106.
  8. Jo Y. and Ryu S.(2016), "Real Time Pothole Detection System based on Video Data for Automatic Maintenance of Road Surface Distress," KIISE Transactions on Computing Practices, vol. 22, no. 1, pp.8-19. https://doi.org/10.5626/KTCP.2016.22.1.8
  9. Jung K. and Park J.(2021), "Roughness Analysis of Paved Road using Drone LiDAR and Images," Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, vol. 39, no. 1, pp.55-63. https://doi.org/10.7848/KSGPC.2021.39.1.55
  10. Kim D., Jung S., Ga H. and Mun S.(2020), "Investigating Asphalt Pavement Surface Condition Using Drones," International Journal of Highway Engineering, vol. 22, no. 1, pp.1-8. https://doi.org/10.7855/ijhe.2020.22.1.001
  11. Kim J., Hyeon S., Chae J. and Do M.(2019), "Road Crack Detection based on Object Detection Algorithm using Unmanned Aerial Vehicle Image," The Journal of the Korea Institute of Intelligent Transportation Systems, vol. 18, no. 6, pp.155-163. https://doi.org/10.12815/kits.2019.18.6.155
  12. Kobayashi K., Kaito K. and Nam L.(2012), "A bayesian estimation method to improve deterioration prediction for infrastructure system with Markov chain model," International J. of Architecture, Engineering and Construction, vol. 1, no. 1, pp.1-13. https://doi.org/10.7492/IJAEC.2012.001
  13. Lemer A.(2004), Public benefit of highway system preservation and maintenance, NCHRP Synthesis 330, TRB, pp.3-13.
  14. Ministry of Land, Infrastructure and Transport(MOLIT)(2017), 6th Investment Evaluation Guide for Transportation Facilities.
  15. Ministry of Land, Infrastructure and Transport(MOLIT)(2021), Annual Research report of the national highway pavement management system 2020.
  16. Peshkin D., Hoerner T. and Zimmerman K.(2004), Optimal timing of pavement preventive maintenance treatment applications, NCHRP Report 523, TRB.
  17. Yang Y., Wu C., Hsu T., Yang H., Lu H. and Chang, C.(2018), "Image analysis method for crack distribution and width estimation for reinforced concrete structures," Automation in Construction, vol. 91, pp.120-132. https://doi.org/10.1016/j.autcon.2018.03.012