Journal of the Korean GEO-environmental Society (한국지반환경공학회 논문집)

Korean Geo-Environmental Society (한국지반환경공학회)
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Comparison of Machine Learning Models to Predict the Occurrence of Ground Subsidence According to the Characteristics of Sewer

하수관로 특성에 따른 지반함몰 발생 예측을 위한 기계학습 모델 비교

  • Lee, Sungyeol (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Jinyoung (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kang, Jaemo (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Baek, Wonjin (Department of Rural and Biosystems Engineering, Chonnam National University)
  • Received : 2022.01.11
  • Accepted : 2022.03.17
  • Published : 2022.04.01
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Abstract

Recently, ground subsidence has been continuously occurring in downtown areas, threatening the safety of citizens. Various underground facilities such as water and sewage pipelines and communication pipelines are buried under the road. It is reported that the cause of ground subsidence is the deterioration of various facilities and the reckless development of the underground. In particular, it is known that the biggest cause of ground subsidence is the aging of sewage pipelines. As an existing study related to this, several representative factors of sewage pipelines were selected and a study to predict the risk of ground subsidence through statistical analysis has been conducted. In this study, a data SET was constructed using the characteristics of OO city's sewage pipe characteristics and ground subsidence data, The data set constructed from the characteristics of the sewage pipe of OO city and the location of the ground subsidence was used. The goal of this study was to present a classification model for the occurrence of ground subsidence according to the characteristics of sewage pipes through machine learning. In addition, the importance of each sewage pipe characteristic affecting the ground subsidence was calculated.

최근 도심지에서는 지반침하가 지속적으로 발생하여 시민의 안전을 위협하고 있다. 상하수도관, 통신관 등 각종 지하시설물이 도로 밑에 매설되어 있다. 지반침하의 원인으로는 도심지에 매설되어 있는 각종 시설물의 노후화와 급격한 도시화로 인한 지하 난개발로 인한 것으로 보고되고 있다. 특히 지반침하의 가장 큰 원인은 하수관로의 노후화로 알려져 있다. 이와 관련된 기존 연구로는 하수관로의 대표적인 몇 가지 요인을 선정하여 통계분석을 통해 지반침하 위험을 예측하는 연구가 진행되었다. 본 연구에서는 OO시의 하수관 특성과 지반침하 데이터를 이용하여 데이터셋을 구축하고, OO시의 하수관 특성과 지반함몰 발생 위치 데이터로 구축된 데이터셋으로 기계학습을 통한 하수관 특성에 따른 지반함몰 발생 분류 모델들을 비교하여 적절한 모델을 선정하고자 하였으며, 선정된 모델에서 도출된 지반함몰에 영향을 미치는 하수관 특성별 중요도를 산정하고자 하였다.

Keywords

Acknowledgement

본 연구는 (22주요-대1-임무) 지하공간 정보 정확도 개선 및 매설관 안전관리 기술개발(3/3) 지원으로 수행되었으며, 이에 깊은 감사를 드립니다.

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