Development of a window-shifting ANN training method for a quantitative rock classification in unsampled rock zone

미시추 구간의 정량적 지반 등급 분류를 위한 윈도우-쉬프팅 인공 신경망 학습 기법의 개발

  • 신휴성 (한국건설기술연구원 지반연구실) ;
  • 권영철 (한국건설기술연구원 지반연구실)
  • Published : 2009.06.30

Abstract

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

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