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2018 Vol.20, Issue 4 Preview Page
Design of umbrella arch method based on adaptive SVM and reliability concept

Adaptive SVM 기법 및 신뢰성 개념을 적용한 강관다단공법의 설계기법 연구

Jun S. Lee1*
,
Myung Sagong1
,
Jeongjun Park2
,
Il Yoon Choi2
이 준석1*
,
사공 명1
,
박 정준2
,
최 일윤2
1Researcher, Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute
2Researcher, Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute
1정회원, 한국철도기술연구원 첨단궤도토목본부 연구원
2비회원, 한국철도기술연구원 첨단궤도토목본부 연구원
* Corresponding Author
31 July 2018. pp. 701-715
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Abstract

A reliability based design approach of the tunnel reinforcement with umbrella arch method was considered to better represent the uncertainties of the weak rock properties around the tunnel. For this, a machine learning approach called an Adaptive Support Vector Machine (ASVM) together with the limit equilibrium method were introduced to minimize the iteration numbers during the classification training of the tunnel stability. The proposed method was compared with the results of typical Monte Carlo simulations. It was concluded that the ASVM was very efficient and accurate to calculate the probability of failure having auxiliary umbrella arches and uncertain material properties of the tunnel. Future work will be concentrated on the refinement of the fast adaptation of the SVM classification so that the minimum number of numerical analyses can be used where the limit solution is not available.

Keywords
Umbrella arch method
Adaptive support vector machine
Machine learning
Reliability based design
Tunnel reinforcement

본 연구에서는 터널주변 원지반의 불확실성을 고려한 신뢰성기반 강관다단공법의 설계기법에 대하여 논의하였다. 이를 위하여 기계학습기법의 한 부류인 adaptive support vector machine과 시공 중인 터널의 한계평형해석기법을 도입한 후, 강관다단공법을 적용한 터널의 안전성 여부에 대한 훈련과정을 최소화할 수 있는 방안을 제안하였다. 제안한 기법은 전형적인 Monte Carlo 기법과의 비교를 통해 그 효과를 분석하였다. 이 결과, 제안한 신뢰성기반 ASVM 기법은 원지반의 불확실성을 감안하는 경우, 보조공법 적용에 따른 터널의 시공 중 파괴확률을 효율적으로 계산할 수 있음을 입증하였다. 이 결과를 바탕으로 향후에는 한계평형해석을 적용할 수 없는 경우 등을 감안하여 최소의 수치해석 결과를 바탕으로 파괴확률을 추론해 낼 수 있는 신속 ASVM 기법을 개발할 예정이다.

키워드
강관다단공법
조절형 써포트벡터머신
기계학습
신뢰성기반설계
터널보강
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Information
  • Publisher :Korean Tunneling and Underground Space Association
  • Publisher(Ko) :한국터널지하공간학회
  • Journal Title :Journal of Korean Tunnelling and Underground Space Association
  • Journal Title(Ko) :한국터널지하공간학회 논문집
  • Volume : 20
  • No :4
  • Pages :701-715
  • Received Date : 2018-05-30
  • Revised Date : 2018-06-28
  • Accepted Date : 2018-07-03
  • DOI :https://doi.org/10.9711/KTAJ.2018.20.4.701
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