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2023 Vol.19, Issue 2 Preview Page

Original Article

Verification of Ground Subsidence Risk Map Based on Underground Cavity Data Using DNN Technique

DNN 기법을 활용한 지하공동 데이터기반의 지반침하 위험 지도 작성

김한응1ㆍ김창헌2ㆍ김태건3ㆍ박정준4*

Han Eung Kim1, Chang Hun Kim2, Tae Geon Kim3, Jeong Jun Park4*

1CEO, Geotechnical Korea Engineering Co., Ltd., Anyang, Republic of Korea
2Director, Research Institute, Geotechnical Korea Engineering Co., Ltd., Anyang, Republic of Korea
3Senior Research Engineer, Research Institute, Geotechnical Korea Engineering Co., Ltd., Anyang, Republic of Korea
4Research Professor, Incheon Disaster Prevention Research Center, Incheon National University, Incheon, Republic of Korea
30 June 2023. pp. 334-343
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Abstract
Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Saha-gu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.
Keywords
Deep Neural Network
Data Preprocessing
Underground Facilities
Cavity
Ground Subsidence Risk Map
연구목적: 본 연구에서는 지반공동탐사로 발견된 공동자료를 지하시설물과의 원인별 상관관계로 분석하고, AI 알고리즘 기반으로 지반침하 예측지도를 검증하여 시민에게 안전한 도로 환경을 제공하고자 한다. 연구방법: 위험도 평가 관련 데이터조사와 빅데이터 수집, AI분석을 위한 데이터 전처리, 그리고 AI 알고리즘을 이용하여 지반침하 위험도 예측지도 검증 등 3가지 단계로 연구를 수행하였다. 연구결과: 작성한 지반침하 위험 예측지도를 분석하여 부산시 부산진구와 사하구에 대해 긴급, 우선, 일반 3단계의 공동관리 위험등급 분포를 확인 할 수 있었다. 또한, 지반침하 위험 등급 예측 값을 도로노선의 구간별로 정리하여 긴급 등급이 포함된 도로가 부산진구는 총 61개구간 중 3개소, 사하구는 총 68개구간 중 7개소임을 확인하였으며 각 도로노선별 지반침하 위험 예측 순위를 파악하였다. 결론: 도출된 지반침하 위험 예측지도를 바탕으로 효율적으로 탐사구간을 설정하여 우선 조사, 선제 조치함으로써 시민들의 불안을 해소하고 효율적인 도로유지관리 및 보수, 제도의 개선 등의 부수적인 효과를 얻을 수 있다.
키워드
심층 신경망
데이터 전처리
지하시설물
공동
지반침하 위험 지도
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Information
  • Publisher :The Korean Society of Disaster Information
  • Publisher(Ko) :한국재난정보학회
  • Journal Title :Journal of the Society of Disaster Information
  • Journal Title(Ko) :한국재난정보학회논문집
  • Volume : 19
  • No :2
  • Pages :334-343
  • DOI :https://doi.org/10.15683/kosdi.2023.6.30.334
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