A study on Development of Artificial Neural Network (ANN) for Preliminary Design of Urban Deep Excavation and Tunnelling

Chungsik Yoo; Jaewon Yang

doi:10.12814/jkgss.2020.19.1.011

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2020 Vol.19, Issue 1 Preview Page Next Page

Research Article

A study on Development of Artificial Neural Network (ANN) for Preliminary Design of Urban Deep Excavation and Tunnelling 도심지 지하굴착 및 터널시공 예비설계를 위한 인공신경망 개발에 관한 연구

31 March 2020. pp. 11-23

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Abstract

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.

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.

Keywords

Artificial Neural Network

Bigdata

Deep excavation

Tunnelling

Field monitoring

References

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Information

Publisher :Korean Geosythetics Society
Publisher(Ko) :한국지반신소재학회
Journal Title :Journal of the Korean Geosynthetics Society
Journal Title(Ko) :한국지반신소재학회 논문집
Volume : 19
No :1
Pages :11-23
Received Date : 2019-03-30
Revised Date : 2020-01-17
Accepted Date : 2020-01-20
DOI :https://doi.org/10.12814/jkgss.2020.19.1.011

Journal of the Korean Geosynthetics Society ISSN:2508-2876(Print) 2287-9528(Online) 한국지반신소재학회 논문집

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