Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
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An Analysis of Artificial Intelligence Algorithms Applied to Rock Engineering

암반공학분야에 적용된 인공지능 알고리즘 분석

  • Received : 2021.02.10
  • Accepted : 2021.02.19
  • Published : 2021.02.28
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Abstract

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

4차 산업혁명 시대의 도래에 따라 암반공학분야에서도 인공지능을 활용한 연구가 점차 증가하고 있다. 본 논문에서는 인공지능에 대한 이해와 그 활용도를 더욱 증진시키기 위하여, 암반공학기술의 주된 적용대상인 터널, 발파, 광산과 관련된 최근의 국내외 연구 중 인공지능이 활용된 논문들에서 그 알고리즘의 종류와 적용방법을 분석하였다. 터널에서는 암반분류, TBM굴진율 및 막장전방 지질 예측, 발파에서는 암반의 파쇄도 및 비산거리, 광산에서는 폐광의 침하가능성 예측을 위해 주로 활용되고 있으며, 기계학습의 다양한 알고리즘 중 인공신경망이 압도적으로 많이 활용되고 있는 것으로 나타났다. 연구결과의 정확도와 신뢰성 제고를 위해 사용하고자 하는 인공지능 알고리즘에 대한 정확하고 상세한 이해가 필수적이며, 현재는 접근이나 분석이 난해한 암반공학 분야의 다양한 문제해결을 위해 기계학습뿐 아니라 CNN 또는 RNN과 같은 딥러닝을 활용한 연구 아이디어들이 점차 증가될 것으로 기대된다.

Keywords

References

  1. Armaghani, D.J., M. Koopialipoor, A. Marto and S. Yagiz, 2019, Application of several optimization techniques for estimating TBM advance rate in granitic rocks, Journal of Rock Mechanics and Geotechnical Engineering 11(4), 779-789. https://doi.org/10.1016/j.jrmge.2019.01.002.
  2. Butler A.G. and J.A. Franklin, 1990, An expert system for rock mass classification, Static and Dynamic considerations in Rock Engineering, Balkema, Rotterdam, 73-80.
  3. Cachim, P. and A. Bezuijen, 2019, Modelling the Torque with Artificial Neural Networks on a Tunnel Boring Machine. KSCE J Civ Eng 23, 4529-4537. https://doi.org/10.1007/s12205-019-0302-0.
  4. CERIK (Construction & Economy Research Institute of Korea), 2019, Survey on Smart Technology Applications of Korean Construction Companies and Strategies for Activation, Report.
  5. Chen, H., C. Xiao, Z. Yao, H. Jiang, T. Zhang and Y. Guan, 2019, Prediction of TBM Tunneling Parameters through an LSTM Neural Network," 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 702-707. doi: 10.1109/ROBIO49542.2019.8961809.
  6. Chen, J., T. Yang, D. Zhang, H. Huang and Y. Tian, 2021, Deep learning based classification of rock structure of tunnel face, Geoscience Frontiers 12, 395-404. https://doi.org/10.1016/j.gsf.2020.04.003
  7. Committee on the fourth industrial revolution, 2019, Recommendations to the government for the fourth industrial revolution.
  8. Das, A., S. Sinha and S. Ganguly, 2019, Development of a blast-induced vibration prediction model using an artificial neural network. Journal of the Southern African Institute of Mining and Metallurgy, 119(2), 187-200. https://dx.doi.org/10.17159/2411-9717/2019/v119n2a11.
  9. Dong L.J., X.B. Li and K. Peng, 2013, Prediction of rockburst classification using Random Forest, Transactions of Nonferrous Metals Society of China 23(2), 472-477, https://doi.org/10.1016/S1003-6326(13)62487-5.
  10. Fan, G., F. Chen, D. Chen, Y. Li and Y. Dong, 2020, A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones, Mobile Information Systems, Article ID 7462524, 14. 2020. https://doi.org/10.1155/2020/7462524.
  11. Feng, G., G. Xia, B. Chen, Y. Xiao and R. Zhou, 2019, A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model. Sustainability 11, 3212. https://doi.org/10.3390/su11113212.
  12. Ghasemi, E., H. Amini, M. Ataei and R. Khalokakaei, 2014, Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arab J Geosci 7, 193-202. https://doi.org/10.1007/s12517-012-0703-6.
  13. Hasegawa, N., S. Hasegawa, T. Kitaoka and H. Ohtsu, 2019, Applicability of Neural Network in Rock Classification of Mountain Tunnel, Materials Transactions, 60(5), 758-764. https://doi.org/10.2320/matertrans.z-m2019809
  14. Hejmanowski, R., A.A. Malinowska and W.T. Witkowski, 2016, Use of the Artificial Neural Networks for Modelling of Surface Subsidence Caused by Dehydration of Rock Mass. 16th international congress for mine surveying, Brisbane, Australia, 12-16.
  15. Hochreiter, S. and J. Schmidhuber, 1997, Long Short-term Memory, Neural Computation 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  16. Houdt, G.V., C. Mosquera and G. Napoles, 2020, A review on the long short-term memory model. Artificial Intelligence Review 53, 5929-5955. https://doi.org/10.1007/s10462-020-09838-1.
  17. Hudson, J.A., 1992, Rock Engineering Systems: Theory and Practice; Ellise Horwood: Chichester, UK.
  18. Jang, M.H., T. Ha and K.H. Choi, 2019, A Study on Mechanical RMR Classification Using Artificial Neural Networks and Robust Design, J. Korean Soc. Miner. Energy Resour. Eng. 56(6), 654-664. https://doi.org/10.32390/ksmer.2019.56.6.654.
  19. Jung, J.H., H. Chung, Y.S. Kwon and I.M. Lee, 2019, An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data. KSCE J Civ Eng 23, 3200-3206. https://doi.org/10.1007/s12205-019-1460-9.
  20. Kim, H., L. Cho and K.S. Kim, 2019, Rock Classification Prediction in Tunnel Excavation Using CNN. Journal of the Korean geotechnical society 35(9), 37-45. https://doi.org/10.7843/KGS.2019.35.9.37.
  21. Kim, Y. and S.S. Lee, 2020, Application of Artificial Neural Networks in Assessing Mining Subsidence Risk. Applied Sciences 10(4), 1302. https://doi.org/10.3390/app10041302.
  22. Koukoutas, S.P. and A.I. Sofianos, 2015, Settlements Due to Single and Twin Tube Urban EPB Shield Tunnelling. Geotech Geol Eng 33, 487-510. https://doi.org/10.1007/s10706-014-9835-7.
  23. Lee, C.W. and H.K. Moon, 1994, Development of an Artificial Neural Network - Expert System for Preliminary Design of Tunnel in Rock Masses, Geotechnical Engineering 10(3), 79-96.
  24. Lee, J.S., S.K. Lee, D.W. Kim, S.J. Hong and S.I. Yang, 2018, Trends on Object Detection Techniques Based on Deep Learning, ETRI, Electronics and Telecommunications Trends 33(4), 23-32.
  25. Li, J., P. Li, D. Guo, X. Li and Z. Chen, 2021, Advanced prediction of tunnel boring machine performance based on big data, Geoscience Frontiers 12, 331-338. https://doi.org/10.1016/j.gsf.2020.02.011
  26. Li, N., R. Jimenez and X. Feng, 2017, The Influence of Bayesian Networks Structure on Rock Burst Hazard Prediction with Incomplete Data, Procedia Engineering 191, 206-214. https://doi.org/10.1016/j.proeng.2017.05.173.
  27. Liu, B., R. Wang, G. Zhao, X. Guo,Y. Wang, J. Li and S. Wang, 2020, Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm, Tunnelling and Underground Space Technology 95, 103103. https://doi.org/10.1016/j.tust.2019.103103.
  28. Lu, X., M. Hasanipanah, K. Brindhadevi, H.B. Amnieh and S. Khalafi, 2020, ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting. Nat Resour Res 29, 641-654. https://doi.org/10.1007/s11053-019-09532-2.
  29. McCarth, J., 2007, What is Artificial intelligence, Stanford University. Computer Sience Department (Download from http://jmc.stanford.edu/articles/whatisai/whatisai.pdf).
  30. Mitchell, T.M., 1997, Machine Learning, McGraw Hill.
  31. Monjezi, M., H. Amiri, A. Farrokhi and K. Goshtasbi, 2010, Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks. Geotech Geol Eng 28, 423-430. https://doi.org/10.1007/s10706-010-9302-z.
  32. Moor, J., 2006, The Dartmouth college artificial intelligence conference: The next fifty years, American Association for Artificial lntelligence, Al Magazine 27(4), 87-91.
  33. Morgenroth, J., U.T. Khan and M.A. Perras, 2019, An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design, Geosciences 9(12), 504. https://doi.org/10.3390/geosciences9120504
  34. Oge, I.F., 2018, Regression Analysis and Neural Network Fitting of Rock Mass Classification Systems, Journal of Science and Engineering 20(59), 354-368.
  35. Oh, H.J., M. Syifa, C.W. Lee and S. Lee, 2019, Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models. Applied Sciences 9(6), 1248. https://doi.org/10.3390/app9061248.
  36. Parida, A. and M.K. Mishra, 2015, Blast Vibration Analysis by Different Predictor Approaches-A Comparison, Procedia Earth and Planetary Science 11, 337-345. https://doi.org/10.1016/j.proeps.2015.06.070.
  37. Pham, C. and H.S. Shin, 2020, A Feasibility Study on Application of a Deep Convolutional Neural Network for Automatic Rock Type Classification. Tunnel and Underground space 30(5), 462-472. https://doi.org/10.7474/TUS.2020.30.5.462
  38. Pham, H.V., F. Yuji and K. Kamei, 2011, Hybrid Artificial Neural Networks for TBM performance prediction in complex underground conditions, IEEE/SICE International Symposium on System Integration (SII), Kyoto, 1149-1154. doi: 10.1109/SII.2011.6147611.
  39. Ran, X., L. Xue, Y. Zhang, Z. Liu, X. Sang and J. He, 2019, Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network. Mathematics 7, 755. https://doi.org/10.3390/math7080755.
  40. Ren, Q., G. Wang and M. Li, 2019, Prediction of Rock Compressive Strength Using Machine Learning Algorithms Based on Spectrum Analysis of Geological Hammer. Geotech Geol Eng 37, 475-489. https://doi.org/10.1007/s10706-018-0624-6
  41. Rosales-Huamani, J.A., R.S. Perez-Alvarado, U., Rojas-Villanueva and J. L. Castillo-Sequera, 2020, Design of a Predictive Model of Rock Breakage by Blasting Using Artificial Neural Networks, Symmetry 12(9), 1405. https://doi.org/10.3390/sym12091405
  42. Salimi, A., C. Moormann, T.N. Singh and P. Jain, 2015, TBM Performance Prediction in Rock Tunneling Using Various Artificial Intelligence Algorithms, 11th Iranian and 2nd Regional Tunneling Conference, Tehran, Iran.
  43. Secretariat of committee on the fourth industrial revolution, 2020, Trend of policy on global AI.
  44. Sklavounos, P. and M. Sakellariou, 1995, Intelligent classification of rock masses, Transactions on Information and Communications Technologies 8.
  45. Specht, D.F., 1990, Probabilistic Neural Networks, Neural Networks 3, 109-118. https://doi.org/10.1016/0893-6080(90)90049-Q
  46. Spencer B.F., V. Hoskerea and Y. Narazaki, 2019, Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring, Engineering 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
  47. Sudakov, O., E. Burnaev and D. Koroteev, 2019, Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks, Computers & Geosciences 127, 91-98. https://doi.org/10.1016/j.cageo.2019.02.002.
  48. Xu, H., J. Zhou, P.G. Asteris, D.J. Armaghani and M.M. Tahir, 2019, Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate. Appl. Sci. 3715. https://doi.org/10.3390/app9183715.
  49. Xue, Y., Z. Li, D. Qiu, L. Zhang, Y. Zhao, X. Zhang and B. Zhou, 2019, Classification model for surrounding rock based on the PCA-ideal point method: an engineering application, Bulletin of Engineering Geology and the Environment 78, 3627-3635. https://doi.org/10.1007/s10064-018-1368-5
  50. Yang, H.S. and J.C. Kim, 1999, Rock Mass Rating for Korean Tunnels Using Artificial Neural Network, J. of Korean Society for Rock Mechanics, Tunnel & Underground 9(3), 214-220.
  51. You, K.H. and S.W. Jeon, 2013, A study on the fast prediction of the fragmentation zone using artificial neural, network when a blasting occurs around a tunnel. J. of Korean Tunn undergr Sp Assoc., 15(2), 81-95. https://doi.org/10.9711/KTAJ.2013.15.2.081.
  52. Zhao, H. and B. Chen, 2020, Data-Driven Model for Rockburst Prediction", Mathematical Problems in Engineering, Article ID 5735496, 14. https://doi.org/10.1155/2020/5735496.
  53. Zhao, J., M. Shi, G. Hu, X. Song, C. Zhang, D. Tao and W. Wu, 2019, A Data-Driven Framework for Tunnel Geological-Type Prediction Based on TBM Operating Data, IEEE Access 7, 66703-66713. doi: 10.1109/ACCESS.2019.2917756.
  54. Zhou, J., B.Y. Bejarbaneh, D.J. Armaghani and M.M. Tahir, 2020, Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bull Eng Geol Environ 79, 2069-2084. https://doi.org/10.1007/s10064-019-01626-8.