[논문]쉴드 TBM 데이터와 머신러닝 분류 알고리즘을 이용한 암반 분류 예측에 관한 연구

강태호; 최순욱; 이철호; 장수호

doi:10.7474/tus.2021.31.6.494

쉴드 TBM 데이터와 머신러닝 분류 알고리즘을 이용한 암반 분류 예측에 관한 연구
A Study on the Prediction of Rock Classification Using Shield TBM Data and Machine Learning Classification Algorithms 원문보기

터널과 지하공간: 한국암반공학회지 = Tunnel and underground space, v.31 no.6, 2021년, pp.494 - 507

강태호 (한국건설기술연구원 지반연구본부) , 최순욱 (한국건설기술연구원 지반연구본부) , 이철호 (한국건설기술연구원 지반연구본부) , 장수호 (한국건설기술연구원 건설산업진흥본부)

초록
AI-Helper

TBM의 활용이 증가하면서 최근 국내에서도 머신러닝 기법으로 TBM 데이터를 분석하여 TBM 전방의 지반을 예측하고 디스크커터의 교환주기 예측 및 굴진율을 예측하는 연구가 수행되고 있다. 본 연구에서는 TBM 굴진 시 기계 데이터를 대상으로 전통적 암반에 대한 분류 기법과 최근에 다양한 분야에서 널리 사용되고 있는 머신러닝 기법들을 접목하여 슬러리 쉴드 TBM 현장의 암반 특성에 대한 분류 예측을 하였다. 암반 특성 분류 기준 항목을 RQD, 일축압축강도, 탄성파속도로 설정하고 항목별 암반상태를 클래스 0(양호),1(보통),2(불량)의 3개 클래스로 구분한 다음, 6개의 분류 알고리즘에 대한 기계학습을 수행하였다. 그 결과, 앙상블 계열의 모델이 좋은 성능을 보여주었고 특히 학습성능과 더불어 학습속도에서 우수한 결과를 보인 LigthtGBM 모델이 대상 현장 지반에서 최적인 것으로 나타났다. 본 연구에서 설정한 3가지 암반 특성에 대한 분류 모델을 활용하면 지반정보가 제공되지 않은 구간에 대한 암반 상태를 제공할 수 있어 굴착작업 시 도움을 줄 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

주제어

표/그림 (13)

표 Table 1. Rock mass quality classification according to RQD (Deere et al., 1967)
표 Table 2. Classification of rocks based on UCS (ISRM, 1979)
표 Table 3. P-wave velocity classification (Anon, 1979)
표 Table 4. Criteria for classification of rock mass quality
표 Table 5. Summary of TBM specification
그림 Fig. 1. Distribution of data according to ground conditions for the TBM
표 Table 6. Confusion matrix of classification
표 Table 7. Classification performance measures
표 Table 8. Summary of hyperparameters for classification models
표 Table 9. Evaluating the performance of the classification for Shield TBM Datasets
그림 Fig. 2. Predicted rock classification based on Shield TBM excavation data
그림 Fig. 2. Predicted rock classification based on Shield TBM excavation data (continued)
그림 Fig. 3. Predicted Rock mass quality for the entire data of TBM excavation

참고문헌 (30)

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