터널과지하공간 (Tunnel and Underground Space)

  • 제31권6호
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  • Pages.494-507
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  • 2021
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  • 1225-1275(pISSN)
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  • 2287-1748(eISSN)
한국암반공학회 (Korean Society for Rock Mechanics and Rock Engineering)
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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

  • 강태호 (한국건설기술연구원 지반연구본부) ;
  • 최순욱 (한국건설기술연구원 지반연구본부) ;
  • 이철호 (한국건설기술연구원 지반연구본부) ;
  • 장수호 (한국건설기술연구원 건설산업진흥본부)
  • Kang, Tae-Ho (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Choi, Soon-Wook (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Chulho (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Chang, Soo-Ho (Construction Industry Promotion Department, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2021.11.22
  • 심사 : 2021.12.08
  • 발행 : 2021.12.31
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초록

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.

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.

키워드

과제정보

본 연구는 국토교통부 국토교통과학기술진흥원의 스마트건설기술개발사업(과제번호: 21SMIP-A157075-02)인 "교량 및 터널의 원격, 자동화 시공을 위한 핵심기술 개발"의 지원으로 수행되었습니다.

참고문헌

  1. Anon, O.H., 1979, "Classification of rocks and soils for engineering geological mapping, Part I: Rock and soil materials", Bulletin of the International Association of Engineering Geology, 19, pp. 364-371. https://doi.org/10.1007/BF02600503
  2. Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N., Yagiz, S. (2017), "Development of hybrid intelligent models for predicting TBM penetration rate in hardrock condition", Tunn. Undergr. Space Technol. Vol. 63, pp. 29-43. https://doi.org/10.1016/j.tust.2016.12.009
  3. Bae, G.-J., Chang, S.-H., Park, Y.-T., Choi, S.-W., Lee, G.-P., Kwon, J.-Y., Han, K.-T., 2014, "Manufacturing of an earth pressure balanced shield TBM cutterhead for a subsea discharge tunnel and its field performance analysis", Journal of Korean Tunnelling and Underground Space Association, Vol. 16, No. 2, pp. 161-172. https://doi.org/10.9711/KTAJ.2014.16.2.161
  4. Barton N, Lien R, Lunde J, 1974. Engineering classification of rock masses for the design of tunnel support, Rock Mech., Vol. 6, pp. 189-236. https://doi.org/10.1007/BF01239496
  5. Bieniawski ZT, 1989. Engineering rock mass classifications. John Wiley & Sons.
  6. Breiman, L, Friedman, J, Stone, C.J, and Olshen, R.A., 1984, Classification and Regression Trees. CRC press.
  7. Breiman, L., 1996. "Bagging predictors." Machine Learning 24, pp. 123-140. https://doi.org/10.1007/BF00058655
  8. Choi, S.-W., Lee, C., Kang, T.-H., Chang, S.-H., 2020, "Current Status of Technical Development for TBM Simulator", TUNNEL & UNDERGROUND SPACE, Vol. 30, No. 5, pp. 433-445. https://doi.org/10.7474/TUS.2020.30.5.433
  9. Deere DU, Miller RP., 1966, Engineering classification and index properties for intact rock, Tech. Rep. No. AFWL-TR-65-115, Air Force Weapons Lab., Kirtland Air Base, New Mexico.
  10. Deere, D.U., Hendron, A.J., Patton, F.D., and Cording, E.J., 1967, Design of surface and near surface construction in rock, 8th U.S. Symposium on Rock Mechanics: Failure and breakage of rock: New York, Society of Mining Engineers, American Institute of Mining, Metallurgical, and Petroleum Engineers.
  11. Gholamnejad, J., Narges, T. (2010), "Application of artificial neural networks to the prediction of tunnel boring machine penetration rate", Min. Sci. Technol. (China) Vol. 20, No. 5, pp. 727-733. https://doi.org/10.1016/S1674-5264(09)60271-4
  12. Grima, M.A., Bruines, P.A., Verhoef, P.N.W. (2000), "Modeling tunnel boring machine performance by neuro-fuzzy methods", Tunn. Undergr. Space Technol. Vol. 15, No.3, pp. 259-269. https://doi.org/10.1016/S0886-7798(00)00055-9
  13. ISRM, 1979, Suggested methods for determining the uniaxial compressive strength and deformability of rock materials, International Journal of Rock Mechanics and Mining. Sciences and Geomechanics Abstracts, 16: pp. 135-140.
  14. ISRM, 1981, Suggested method for laboratory determination of direct shear strength, Rock characterisation testing and monitoring (Brown ET ed.), International Society for Rock Mechanics, Pergamon: Oxford, UK.
  15. Jung, J.-H., Kim, B.-K., Chung, H., Kim, H.-M., Lee, I.-M., 2019, "A ground condition prediction ahead of tunnel face utilizing time series analysis of shield TBM data in soil tunnel", Journal of Korean Tunnelling and Underground Space Association, Vol. 21, No. 2, pp. 227-242. https://doi.org/10.9711/KTAJ.2019.21.2.227
  16. Kang, S.-H., Kim, D.-H., Kim, H.-T., Song, S.-W., 2017, "Study on the structure of the articulation jack and skin plate of the sharp curve section shield TBM in numerical analysis", Journal of Korean Tunnelling and Underground Space Association, Vol. 19, No. 3, pp. 421-435. https://doi.org/10.9711/KTAJ.2017.19.3.421
  17. Kang, T.-H., Choi, S.-W., Lee, C., Chang, S.-H., 2020, "A Study on Prediction of EPB shield TBM Advance Rate using Machine Learning Technique and TBM Construction Information", TUNNEL & UNDERGROUND SPACE, Vol. 30, No. 6, pp. 540-550. https://doi.org/10.7474/TUS.2020.30.6.540
  18. Kearns, M. and Valiant, L.G., 1994, "Cryptographic limitations on learning Boolean formulae and finite automata", Journal of the Association for Computing Machinery, Vol. 41, pp. 67-95. https://doi.org/10.1145/174644.174647
  19. Kim, T.H., Ko, T.Y., Park, Y.S., Kim, T.K., Lee, D.H., 2020a, "Prediction of Uniaxial Compressive Strength of Rock using Shield TBM Machine Data and Machine Learning Technique", TUNNEL & UNDERGROUND SPACE, Vol. 30, No. 3, pp. 214-225. https://doi.org/10.7474/TUS.2020.30.3.214
  20. Kim, T.H., Kwak, N.S., Kim, T.K., Jung, S., Ko, T.Y., 2021, "A TBM data-based ground prediction using deep neural network", Journal of Korean Tunnelling and Underground Space Association, Vol. 23, No. 1, pp. 13-24. https://doi.org/10.9711/KTAJ.2021.23.1.013
  21. Kim, Y., Hong, J., Kim, B., 2020b, "Performance comparison of machine learning classification methods for decision of disc cutter replacement of shield TBM." Journal of Korean Tunnelling and Underground Space Association, Vol. 22, No. 5, pp. 575-589. https://doi.org/10.9711/KTAJ.2020.22.5.575
  22. Ko, T.Y., Kim, T.K., Lee, D.H., 2019, "Statistical Characteristics and Rational Estimation of Rock TBM Utilization", TUNNEL & UNDERGROUND SPACE, Vol. 29, No. 5, pp. 356-366.
  23. La, Y.S., Kim, M.I., Kim, B., 2019, "Prediction of replacement period of shield TBM disc cutter using SVM", Journal of Korean Tunnelling and Underground Space Association, Vol. 21, No. 5, pp. 641-656. https://doi.org/10.9711/KTAJ.2019.21.5.641
  24. Mahdevari, S., Shahriar, K., Yagiz, S., Shirazi, M.A., 2014, A support vector regression model for predicting tunnel boring machine penetration rates, Int. J. Rock Mech. Min., Vol. 74, pp. 214-229.
  25. Salimi, A., Rostami, J., Moormann, C., Delisio, A., 2016, Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs, Tunn. Undergr. Space Technol., Vol. 58, pp. 236-246. https://doi.org/10.1016/j.tust.2016.05.009
  26. Terzaghi K., 1946, Rock defects and load on tunnel supports, Introduction to rock tunnelling with steel supports, (R.V. Proctor & T.L. White eds.) Commercial Sheering & Stamping Co: Youngstown, USA.
  27. Vapnik, V., Golowich, S., and Smola, A., 1996, "Support Method for Function Approximation Regression Estimation and Signal Processing", In Proceedings of the 9th International Conference on Neural Information Processing Systems (NIPS' 96), Cambridge, pp. 281-287.
  28. Wickham, G.E.; Tiedemann, H.R.; Skinner, E.H., 1972. "Support determination based on geologic predictions". In Lane, K.S.; Garfield, L.A. (eds.). Proc. 1st North American Rapid Excavation &Tunnelling Conference (RETC), Chicago. 1. American Institute of Mining, Metallurgical and Petroleum Engineers (AIME), New York. pp. 43-64.
  29. Yagiz, S., Gokceoglu, C., Sezer, E., Iplikci, S., 2009, Application of two non-linear prediction tools to the estimation of tunnel boring machine performance, Eng. Appl.Artif. Intell. Vol.22 (4-5), pp. 808-814. https://doi.org/10.1016/j.engappai.2009.03.007
  30. Yagiz, S., Karahan, H., 2011, "Prediction of hard rock TBM penetration rate using particle swarm optimization", Rock Mechanics and Mining Science, Vol. 48, No. 3, pp. 427-433. https://doi.org/10.1016/j.ijrmms.2011.02.013