Journal of the Korean Geosynthetics Society (한국지반신소재학회논문집)

Korean Geosynthetics Society (한국지반신소재학회)
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A study on Development of Artificial Neural Network (ANN) for Preliminary Design of Urban Deep Ex cavation and Tunnelling

도심지 지하굴착 및 터널시공 예비설계를 위한 인공신경망 개발에 관한 연구

  • Yoo, Chungsik (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan Univ. Natural Sciences Campus) ;
  • Yang, Jaewon (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan Univ. Natural Sciences Campus)
  • Received : 2019.03.30
  • Accepted : 2020.01.20
  • Published : 2020.03.30
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Abstract

In this paper development artificial neural networks (ANN) for preliminary design and prediction of urban tunnelling and deep excavation-induced ground settlement was presented. In order to form training and validation data sets for the ANN development, field design and measured data were collected for various tunnelling and deep-excavation sites. The field data were then used as a database for the ANN training. The developed ANN was validated against a testing set and the unused field data in terms of statistical parameters such as R2, RMSE, and MAE. The practical use of ANN was demonstrated by applying the developed ANN to hypothetical conditions. It was shown that the developed ANN can be effectively used as a tool for preliminary excavation design and ground settlement prediction for urban excavation problems.

본 본문에서는 도심지 지하굴착 및 터널현장의 예비설계 및 지반침하를 예측이 가능한 인공신경망 개발에 대한 내용을 다루었다. 인공신경망의 개발을 위해 먼저 다양한 도심지 터널 및 지하굴착 현장 계측자료를 수집하여 데이터베이스를 구축하고 이를 인공신경망 학습에 필용한 학습데이터를 구축하는데 활용하였다. 개발된 인공신경망은 학습에 활용되지 않은 검증 데이터 세트를 및 현장계측자료를 활용하여 결정계수(R2), 평균제곱근오차(Root Mean Square Error; RMSE), 절대평균오차(Mean Absolute Error; MAE) 등 통계적 파라메타를 근거로 하여 신뢰도를 검증하였다. 개발된 인공신경망은 도심지 굴착현장의 예비 설계 및 이에 따른 주변침하를 예측하는데 효율적으로 활용될 수 있는 것으로 평가되었다.

Keywords

References

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