A Study on Prediction of EPB shield TBM Advance Rate using Machine Learning Technique and TBM Construction Information

Tae-Ho Kang; Soon-Wook Choi; Chulho Lee; Soo-Ho Chang

doi:10.7474/TUS.2020.30.6.540

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2020 Vol.30, Issue 6 Preview Page Next Page

Original Article

A Study on Prediction of EPB shield TBM Advance Rate using Machine Learning Technique and TBM Construction Information 머신러닝 기법과 TBM 시공정보를 활용한 토압식 쉴드TBM 굴진율 예측 연구

31 December 2020. pp. 540-550

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Abstract

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

Keywords

EPB Shield TBM

Machine learning

Algorithm

Geotechnical data

Machine data

최근 AI 기술의 발전과 정립으로 자동화 분야에서 머신러닝 기법의 활용이 활발하게 이루어지고 있다. 머신러닝 기법의 활용에 있어 중요한 점은 데이터 특성에 따라 적합한 알고리즘이 존재한다는 점이며, 머신러닝 기법 적용을 위한 데이터세트의 분석이 필요하다. 본 연구에서는 다양한 머신러닝 기법을 기반으로 하천 하부의 토사지반을 통과하는 토압식 쉴드TBM 터널 구간의 지반정보와 굴진정보를 사용하여 토압식 쉴드TBM의 굴진율을 예측하였다. 선형회귀모델에서 모델의 통계적인 유의성과 다중공선성에서는 문제가 없었으나 결정계수가 0.76으로 나타났고 앙상블 모델과 서포트 벡터 머신에서는 0.88이상의 예측성능을 보여, 분석한 데이터세트에서 토압식 쉴드TBM 굴진성능예측에 적합한 모델은 서포트 벡터 머신임을 알 수 있었다. 현재 도출된 결과로 볼 때, 토압식 쉴드TBM의 기계데이터와 지반정보가 포함된 데이터를 활용한 굴진성능 예측 모델의 적합성은 높다고 판단된다. 추가적으로 지반조건의 다양성과 데이터양을 늘리는 연구가 필요한 것으로 판단된다.

키워드

토압식 쉴드TBM

머신러닝

알고리즘

지반정보

기계데이터

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Information

Publisher :Korean Society for Rock Mechanics and Rock Engineering
Publisher(Ko) :한국암반공학회
Journal Title :Tunnel and Underground Space
Journal Title(Ko) :터널과 지하공간
Volume : 30
No :6
Pages :540-550
Received Date : 2020-11-26
Revised Date : 2020-11-30
Accepted Date : 2020-11-30
DOI :https://doi.org/10.7474/TUS.2020.30.6.540

Tunnel and Underground SpaceISSN:1225-1275(Print) 2287-1748(Online)한국암반공학회

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