Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Prediction of Deep Excavation-induced Ground Surface Movements Using Artificial Neural Network

인공신경망기법을 이용한 깊은 굴착에 따른 지표변위 예측

  • 유충식 (성균관대학교 토목환경공학과) ;
  • 최병석 (성균관대학교 건축, 조경, 토목공학부 토목공학과)
  • Published : 2004.04.01
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Abstract

This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network(ANN) technique, which is of prime importance in the damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements, was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Artificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effective for a first-order prediction of ground movements associated with deep-excavation.

본 연구에서는, 굴착 공사로 인한 주변 건물 손상 평가의 일환으로 지표 변위유형의 효과적인 예측 방안을 제시하였다. 먼저 검증된 유한요소 모델을 국내에서 행해지는 다양한 경우의 굴착특성으로 해석한 결과를 통해 인접 지반의 거동에 대한 매개 변수 연구를 수행하였고, 인공신경망 엔진의 학습을 위한 데이터베이스를 구축하였다. 최적의 구조로 학습된 신경망 엔진은 간단한 굴착 특성으로 다양하게 나타나는 지표 변위 유형 예측에 효율적임을 검증하였다.

Keywords

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