A Study on the Prediction of Uniaxial Compressive Strength Classification Using Slurry TBM Data and Random Forest

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

doi:10.7474/TUS.2023.33.6.547

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2023 Vol.33, Issue 6 Preview Page Next Page

Research Article

A Study on the Prediction of Uniaxial Compressive Strength Classification Using Slurry TBM Data and Random Forest 이수식 TBM 데이터와 랜덤포레스트를 이용한 일축압축강도 분류 예측에 관한 연구

31 December 2023. pp. 547-560

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Abstract

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

Keywords

Slurry TBM

Machine learning

Uniaxial compressive strength

Classification model

Random forest

최근 국내외에서 기계학습 기법으로 TBM 굴진 데이터와 지반데이터를 분석하는 지반 분류예측 연구가 증가하고 있다. 본 연구에서는 다양한 분야에서 널리 사용되고 있는 머신러닝 기법들 중 의사결정트리 기반 랜덤포레스트 모델을 3곳의 이수식 TBM 현장에서 획득한 기계 데이터와 지반 데이터에 적용하여 일축압축강도에 대한 다중 분류예측 연구를 하였다. 일축압축강도의 다중 분류 예측을 위해서 학습과 테스트 데이터를 7:3으로 분할하였으며, 최적의 파라미터를 선정을 위해서 분할 교차검증을 포함하는 그리드 서치를 활용하였다. 의사 결정 트리를 기반으로 한 랜덤 포레스트를 사용하여 일축압축강도 분류 학습을 수행한 결과, 다중 분류 예측 모델의 정확도는 학습 세트와 테스트 세트에서 각각 0.983 및 0.982로 모두 높게 나타났다. 다만, 클래스 간 데이터 분포의 불균형으로 인하여 클래스 4에서는 재현율이 낮게 평가되었다. 다양한 현장에서 획득한 일축압축강도의 측정 데이터양을 늘리는 연구가 필요한 것으로 판단된다.

키워드

이수식 TBM

기계학습

일축압축강도

분류모델

랜덤포레스트

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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 : 33
No :6
Pages :547-560
Received Date : 2023-11-05
Revised Date : 2023-12-11
Accepted Date : 2023-12-11
DOI :https://doi.org/10.7474/TUS.2023.33.6.547

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

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