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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Development of Deterioration Model for Cracks in Asphalt Pavement Using Deep Learning-Based Road Asset Monitoring System

딥러닝 기반의 도로자산 모니터링 시스템을 활용한 아스팔트 도로포장 균열률 파손모델 개발

  • 박정권 (한국토지주택공사 경기지역본부) ;
  • 김창학 (경상국립대학교 토목공학과) ;
  • 최승현 (한밭대학교 도시공학과) ;
  • 도명식 (한밭대학교 도시공학과)
  • Received : 2022.09.05
  • Accepted : 2022.10.04
  • Published : 2022.10.31
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Abstract

In this study, a road pavement crack deterioration model was developed for a pavement road sections of the Sejong-city. Data required for model development were acquired using a deep learning-based road asset monitoring system. Road pavement monitoring was conducted on the same sections in 2021 and 2022. The developed model was analyzed by dividing it into a method for estimating the annual average amount of deterioration and a method based on Bayesian Markov Mixture Hazard model. As a result of the analysis, it was found that an analysis results similar to the crack deterioration model developed based on the data acquired from the Automatic pavement investigation equipmen was derived. The results of this study are expected to be used as basic data by local governments to establish road management plans.

본 연구에서는 세종시의 도로포장 구간을 대상으로 도로포장의 균열률 파손모델을 개발하였다. 파손모델개발에 필요한 모니터링 데이터는 딥러닝 기반의 도로자산 모니터링 시스템을 활용하여 취득하였다. 모니터링 조사는 동일 구간을 대상으로 2021년도와 2022년도에 수행하였다. 도로포장 파손모델은 연평균 파손량을 추정하기 위한 방법론과 계층적 베이지안 마르코프 혼합 해저드 (Bayesian Markov Mixture Hazard) 모델을 활용한 방법론으로 구분하여 분석을 수행하였다. 분석결과, 기존 전문조사장비에서 취득된 데이터를 기반으로 개발된 균열률 파손모델과 유사한 분석 값을 도출할 수 있었다. 본 연구의 결과는 향후 지자체의 도로관리계획수립을 위한 기초자료로 활용될 것으로 기대된다.

Keywords

References

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