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2016 Vol.32, Issue 2
A Basic Study on the Prediction of Collapse of Tunnels Using Artificial Neural Network

인공신경망 기법을 이용한 터널 붕괴 예측에 관한 기초 연구

Hong-Heum Kim1*
,
Heui-Dae Lim2
김 홍흠1*
,
임 희대2
1Deputy Director, Board of Audit and Inspection of the Republic of Korea
2Prof. Dept. of Civil Engrg., Chung Nam National Univ.
1감사원 기술서기관
2충남대학교 토목공학과 교수
*교신저자. *Corresponding Author. 
28 February 2016. pp. 5-17
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Abstract

Collapse of a tunnel can occur anytime, anywhere due to the special characteristics of tunnel structures and unexpected geological conditions during construction. Tunnel collapse will lead to economic losses and casualties. So various studies are continually being conducted to prevent economic losses, casualties and accidents. In this study, we analyzed data from 56 domestic construction tunnel collapse sites, and input factors to be applied to the artificial neural network were selected by the sensitivity analysis. And for the artificial neural network model design studies were carried out with the selected input factors and optimized ANN model to predict the type of tunnel collapse was determined. By using it, in 12 sites where tunnel collapse occurred applicability evaluation was conducted. Thus, the tunnel collapse type predictability was verified. These results will be able to be used as basic data for preventing and reinforcing collapse in the tunnel construction site.

Keywords
Collapse
Tunnel
ANN (Artificial neural network)
Toe quake
Sensitivity analysis
Training

터널에서의 붕괴는 터널 구조물의 특수성 및 예상치 못한 지반조건의 변화로 인해 언제 어디서든 발생될 수 있다. 그로 인한 경제적인 손실과 인명피해를 줄이기 위하여 사고를 미연에 방지하기 위한 방안에 대한 다양한 연구들이 계속 진행되고 있는 실정이다. 본 연구에서는 붕괴예측을 위하여 국내 터널 붕괴 현장 56개소의 시공데이터를 분석하고 인공신경망 기법에 적용할 입력인자를 민감도 분석으로 선정하였다. 또한 인공신경망 모델 설계는 선정된 입력인자로 학습을 수행하고 터널 붕괴 유형 예측에 최적화된 모델을 결정하였다. 이 모델을 이용하여 붕괴가 발생된 총 12개소에 적용성 평가를 실시하여 터널 붕괴 유형 예측 가능성을 검증하였다. 이러한 결과는 터널 시공 현장에서 붕괴 예방을 위한 기초 자료로서 활용 될 수 있을 것이다.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 32
  • No :2
  • Pages :5-17
  • Received Date : 2015-10-28
  • Revised Date : 2015-12-28
  • Accepted Date : 2016-01-30
  • DOI :https://doi.org/10.7843/kgs.2016.32.2.5
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