Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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A Study on the Quality Control Method for Geotechnical Information Using AI

AI를 이용한 지반정보 품질관리 방안에 관한 연구

  • Park, Ka-Hyun (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology) ;
  • Kim, Jongkwan (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology) ;
  • Lee, Seokhyung (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology) ;
  • Kim, Min-Ki (Metalogos) ;
  • Lee, Kyung-Ryoon (Metalogos) ;
  • Han, Jin-Tae (Geotechnical Engrg. Research Dept., Korea Institute of Civil and Building Technology)
  • 박가현 (한국건설기술연구원 지반연구본부) ;
  • 김종관 (한국건설기술연구원 지반연구본부) ;
  • 이석형 (한국건설기술연구원 지반연구본부) ;
  • 김민기 (메타로고스 주식회사) ;
  • 이경륜 (메타로고스 주식회사) ;
  • 한진태 (한국건설기술연구원 지반연구본부)
  • Received : 2022.10.24
  • Accepted : 2022.11.07
  • Published : 2022.11.30
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Abstract

The geotechnical information constructed in the National Geotechnical Information DB System has been extensively used in design, construction, underground safety management, and disaster assessment. However, it is necessary to refine the geotechnical information because it has nearly 300,000 established cases containing a lot of missing or incorrect information. This research proposes a method for automatic quality control of geotechnical information using a fully connected neural network. Significantly, the anomalies in geotechnical information were detected using a database combining the standard penetration test results and strata information of Seoul. Consequently, the misclassification rate for the verification data is confirmed as 5.4%. Overall, the studied algorithm is expected to detect outliers of geotechnical information effectively.

국토지반정보 포털시스템이 구축된 지반정보는 최근 설계, 시공, 지하안전관리, 재해재난 평가 등 다양한 분야에서 활용되고 있다. 그러나 전국적으로 기 구축된 약 30여만공의 지반정보는 누락되거나 잘못된 정보를 다수 포함하고 있어 데이터 활용시 신뢰도를 확보하기가 어렵다. 따라서 분석 데이터의 신뢰도를 확보하기 위해서는 지반정보를 활용하기 전 단계에서 지반정보의 정제(품질관리)가 반드시 필요하다. 본 연구에서는 딥러닝 기법 중 하나인 인공신경망 기법을 활용하여 지반정보를 자동으로 품질관리 하는 방안에 대하여 제안하였다. 특히, 가장 일반적으로 사용되는 정보인 표준관입시험 결과와 지층정보를 이용하여 지반정보의 이상치를 탐지하였다. 서울시 지반정보 데이터를 이용하여 분석하였으며, 검증데이터에 대한 오분류 비율은 5.4%로 확인되었다. 신경망 모델에서 이상치 분류된 데이터만을 추후에 검사함으로써 효율적으로 이상치를 탐지할 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 한국건설기술연구원 연구운영비지원(주요사업)사업으로 수행되었습니다(과제번호 20220173-001, (22주요-대1-목적)지반분야 재난 재해 대응과 미래 건설산업 신성장을 위한 지반 기술 연구(2/2)).

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