커터수명지수 예측을 위한 다중선형회귀분석과 트리 기반 머신러닝 기법 적용
Application of Multiple Linear Regression Analysis and Tree-Based Machine Learning Techniques for Cutter Life Index(CLI) Prediction
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초록
TBM 공법은 굴착면 안정성 확보 및 주변환경에 비치는 영향을 최소화하기 때문에 도심지나 하・해저터널 등에서 적용 사례가 증가하는 추세이다. 디스크 커터의 수명을 예측하는 대표적인 모델 중 NTNU모델은 커터수명지수(Cutter Life Index, CLI)를 주요 매개 변수로 활용하지만 복잡한 시험절차와 시험장비의 희귀성으로 측정에 어려움이 있다. 본 연구에서는 다중선형회귀분석과 트리 기반의 머신러닝 기법으로 암석물성을 활용하여 CLI를 예측하였다. 문헌 조사를 통해 암석의 일축압축강도, 압열인장강도, 등가석영함량과 세르샤 마모지수 등을 포함한 데이터베이스를 구축하였고 파생변수를 계산하여 추가하였다. 다중선형회귀분석은 통계적 유의성과 다중공선성을 고려하여 입력 변수를 선정하였고 머신러닝 예측 모델은 변수 중요도를 기반으로 입력 변수를 선정하였다. 학습용과 검증용 데이터를 8:2로 나누어 모델 간 예측 성능을 비교한 결과 XGBoost가 최적의 모델로 선정되었다. 본 연구에서 도출된 다중선형회귀모델과 XGBoost모델을 선행 연구와 예측 성능을 비교하여 타당성을 확인하였다.
Abstract
TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock pro- perties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.
참고문헌 (27)
- Aligholi, S., "Evaluating the relationships between NTNU", SINTEF drillability indices with index properties and petrographic data of hard igneous rocks, 2017, pp.50, 2929~2953
- Altindag, R., "Correlation of specific energy with rock brittleness concepts on rock cutting", J. South. Afr. Inst. Min. Metall, 103(3), pp.163~171 (2003)
- Altindag, R., "Assessment of some brittleness indexes in rock-drilling efficiency", Rock Mech. Rock Eng, 43, pp.361~370 (2010)
- Bruland, A., "Hard rock tunnel boring: vol. 8 drillability test methods", Ph. D., pp.2000
- 장수호, "TBM 굴진성능 예측을 위한 NTNU시험결과의 분석", 한국터널지하공간학회 논문집, 13(3), pp.243~260 (2011), 사단법인 한국터널지하공간학회
- Chen, T., "Xgboost: A scalable tree boosting system", Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min, pp.785~794 (2016)
- Cutler, A., "Ensemble machine learning: Methods and applications", pp.157~175 (2012)
- Dahl, F., "Classifications of properties influencing the drillability of rocks", Tunn. Undergr. Space Technol, 28, pp.150~158 (2012)
- Drucker, H., "Improving regressors using boosting techniques", icml, 97, pp.107~115 (1997)
- Eide, L. N. R., "TBM tunnelling at the Stillwater Mine", MS, pp.2014
- Gehring, K., "Leistungs-und Verschleißprognosen im maschinellen Tunnelbau", Felsbau, 13(6), pp.439~448 (1995)
- Geurts, P., "Extremely randomized trees", Machine learning, 63, pp.3~42 (2006)
- Hucka, V., "Brittleness determination of rocks by different methods", Int. j. rock mech. min. sci. geomech. abstr, 11(10), pp.389~392 (1974)
- Ko, T. Y., "Effect of geomechanical properties on Cerchar Abrasivity Index(CAI)and its application to TBM tunnelling", Tunn. Undergr. Space Technol, 57, pp.99~111 (2016)
- Kuhn, M., "Applied predictive modeling", pp.13~26 (2013), Springer
- Macias, F. J., "New rock abrasivity test method for tool life assessments on hard rock tunnel boring : the rolling indentation abrasion test(RIAT)", Rock Mech. Rock Eng, 49(5), pp.1679~1693 (2016)
- Macias, F. J., "Hard rock tunnel boring: performance predictions and cutter life assessments", Ph. D., pp.2016
- Majeed, Y., "Effects of variation in the particle size of the rock abrasion powder and standard rotational speed on the NTNU", SINTEF abrasion value steel test, 2019, pp.78, 1537~1554
- Majeed, Y., "Abrasivity evaluation for wear prediction of button drill bits using geotechnical rock properties", Bull. Eng. Geol. Environ, 79, pp.767~787 (2020)
- Massalov, T., "Application of soft computing techniques to estimate cutter life index using mechanical properties of rocks", Appl. Sci, 12(3), pp.1446~ (2022)
- Meng, F., "Rock brittleness indices and their applications to different fields of rock engineering : A review", J. Rock Mech. Geotech. Eng, 13(1), pp.221~247 (2021)
- 문경태, "제주도 화산암의 압열인장강도와 일축압축강도로부터 추정된 점착력과 내부마찰각", 한국지반공학회논문집, 36(2), pp.17~28 (2020), 한국지반공학회[DOI]
- Natekin, A., "Gradient boosting machines, a tutorial", Frontiers in neurorobotics, 7, pp.21~ (2013)
- Özfırat, M.K., "A new approach to rock brittleness and its usability at prediction of drillability", J. Afr. Earth Sci, 119, pp.94~101 (2016)
- Piratheepan, J., "Determination of c and φ from IDT and unconfined compression testing and numerical analysis", J. Mater. Civ. Eng, 24(9), pp.1153~1164 (2012)
- Plinninger, R. J., "Klassifizierung und Prognose von Werkzeugverschleiß bei konventionellen Gebirgslösungsverfahren im Festgestein", ~ (2002)
- Rostami, J., "A new model for performance prediction of hard rock TBMs", Proc. Rapid Excav. Tunneling Conf, pp.793~809 (1993)