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Volume 13 | Issue 5 | Year 2026 | Article Id. IJCE-V13I5P113 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I5P113

Predictive Estimation of TBM Torque and Thrust for Tunnel Stability in Challenging Environments

Shilpa Deshpande, Namdeo Hedaoo

Received Revised Accepted Published
09 Feb 2026 06 Apr 2026 18 Apr 2026 29 May 2026

Citation :

Shilpa Deshpande, Namdeo Hedaoo, "Predictive Estimation of TBM Torque and Thrust for Tunnel Stability in Challenging Environments," International Journal of Civil Engineering, vol. 13, no. 5, pp. 187-202, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I5P113

Abstract

Uncertainties associated with tunnelling in geotechnically complex and structurally heterogeneous formations arise due to the unpredictable nature of rock masses, faulted zones, and high groundwater pressures. These ground behaviors result in unstable faces at the tunnel entrance, extensive ground deformation, and TBM jamming, which may hamper construction safety and project duration. Previous literature addressed the framework by torque and thrust independently, which affects tunnel stability indicators and ignores the hydromechanical coupling. To address this research gap, this study outlines a predictive model for estimating key TBM operational parameters, Torque (Tq) and Thrust (Th), in relation to the diameter of the tunnel and specific geomechanical and hydrogeological properties of the site. The Finite Element Analysis (FEA) based methodology was used in estimating these parameters, which represent the interaction between the ground and the TBM, considering the effect of overburden stress, rock mass strength, and groundwater pressure. A series of parametric studies was conducted for different tunnel diameters ranging from 6 to 16 m under various water heads to demonstrate the non-linear relationship between the forces required to operate a TBM. The observed result found that the increase in force required to operate a TBM is significantly greater when there is a higher hydrostatic pressure. The findings were validated using actual geological data collected from a case study of a Himalayan tunnel project, which represents a typical challenging environment for tunnelling. The developed model is a useful decision-making aid for pre-construction planning, selecting TBMs, and managing risks and will be applicable to other mountainous and tectonically active regions that exhibit similar geological complexities.

Keywords

Excavation Face stability, TBM entrapment, Risk Mitigation, TBM Torque, Machine Thrust.

References

  1. K.S. Valdiya, “Evolution of the Himalaya,” Tectonophysics, vol. 105, no. 1-4, pp. 229-248, 1984.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Albrecht Steck, “Geology of the NW Indian Himalaya,” Eclogae Geologicae Helvetiae, vol. 96, no. 2, pp. 147-196, 2003.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. A. K. Jain et al., “Evolution of the Himalaya,” Proceedings of the Indian National Science Academy, vol. 78, no. 3, pp. 259-275, 2012.
    [    Google Scholar]
  4. O.N. Bhargava, and Birendra P. Singh, “Geological Evolution of the Tethys Himalaya,” Episodes Journal of International Geoscience, vol. 43, no. 1, pp. 404-416, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Birendra P. Singh, and O.N. Bhargava, “Cambrian of the Himalaya and the Peninsular India—Biozonation, Depositional Environments and Biogeographic Provinces,” Episodes Journal of International Geoscience, vol. 43, no. 1, pp. 429-437, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. S. Kumar et al., “Recent Studies in the Proterozoic Sedimentary Belt of Himalaya,” Proceedings of the Indian National Science Academy, vol. 86, pp. 175-181, 2020.
    [    CrossRef] [Google Scholar]
  7. Om N. Bhargava et al., “Evolution of the Lesser Himalaya in Space and Time,” Himalayan Geology, vol. 42, no. 2, pp. 263-289, 2021.
    [    Google Scholar]
  8. Om N. Bhargava, Some Unresolved Problems in the Himalaya: A Synoptic View, Advances in Remote Sensing Technology and the Three Poles, pp. 191-202, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Yogesh Ray, Subhajit Sinha, and Sumit K Ghosh, “Provenance of the Proterozoic Lesser Himalayan Siliciclastics, Northwest Himalaya, India: Implications to Terrain Accretion and Crustal Evolution,” Geosystems and Geoenvironment, vol. 1, no. 2, pp. 1-22, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. R.K. Goel, J.L. Jethwa, and A.G. Paithankar, “Tunnelling through the Young Himalayas — A Case History of the Maneri-Uttarkashi Power Tunnel,” Engineering Geology, vol. 39, no. 1-2, pp. 31-44, 1995.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. R.K. Goel, “Status of Tunnelling and Underground Construction Activities and Technologies in India,” Tunnelling and Underground Space Technology, vol. 16, no. 2, pp. 63-75, 2001.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. R. K. Goel, “Tunnelling through Weak and Fragile Rocks of Himalayas,” International Journal of Mining Science and Technology, vol. 24, no. 6, pp. 783-790, 2014.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. R. K. Goel, “Experiences and Lessons from the Use of TBM in the Himalaya—A Review,” Tunnelling and Underground Space Technology, vol. 57, pp. 277-283, 2016.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. R. K. Goel, and Bhawani Singh, Tunnels in the Himalaya, Rock Mechanics and Engineering, 1st ed., pp. 1-40, CRC Press, 2017.
    [    Google Scholar] [Publisher Link]
  15. Amit K. Verma, and T. N. Singh, “Assessment of Tunnel Instability—A Numerical Approach,” Arabian Journal of Geosciences, vol. 3, pp. 181-192, 2010.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Hafeezur Rehman et al., “Impact of Construction Method and Ground Composition on Headrace Tunnel Stability in the Neelum–Jhelum Hydroelectric Project: A Case Study Review from Pakistan,” Applied Sciences, vol. 11, no. 4, pp. 1-29, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Abdullah H. Alsabhan et al., “The Effect of Opening Shapes on the Stability of Underground Tunnels: A Finite Element Analysis,” GEOMATE Journal, vol. 21, no. 87, pp. 19-27, 2021.
    [    Google Scholar] [Publisher Link]
  18. A. Srivastav et al., “Numerical Analysis of a Collapsed Tunnel: A case study from NW Himalaya, India,” Indian Geotechnical Journal, vol. 52, pp. 132-144, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Muhammad Zaka Emad et al., “Optimum Design of Half Tunnels for Transportation in the Himalayas,” Transportation Infrastructure Geotechnology, vol. 9, pp. 101-116, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Abdullah Ansari, K. Seshagiri Rao, and Arvind K. Jain, “Seismic Response and Fragility Evaluation of Circular Tunnels in the Himalayan Region: Implications for Post-seismic Performance of Transportation Infrastructure Projects in Jammu and Kashmir,” Tunnelling and Underground Space Technology, vol. 137, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Naeem Abbas et al., “Reliability Analysis of Support Strategies in Tunnel Construction: Insights from Geomechanical Analysis of Jointed Rock Masses,” Results in Earth Sciences, vol. 2, pp. 1-13, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Yamin Wu et al., “Failure Mechanism and Structural Optimization of the Primary Support Structure for Expressway Tunnel in Soft Rock: A Case Study,” Periodica Polytechnica Civil Engineering, vol. 68, no. 4, pp. 1268-1280, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Ugur Ates et al., “Estimating Torque, Thrust and other Design Parameters of Different Type TBMs with Some Criticism to TBMs used in Turkish Tunneling Projects,” Tunnelling and Underground Space Technology, vol. 40, pp. 46-63, 2014.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Sinan Acun, Nuh Bilgin, and Ulaş Erboylu, “Contribution on the Understanding of EPB-TBM Drives in Complex Geologic Structures,” Tunnelling and Underground Space Technology, vol. 107, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Abubakar Sharafat, Kamran Latif, and Jongwon Seo, “Risk Analysis of TBM Tunneling Projects based on Generic Bow-tie risk Analysis Approach in Difficult Ground Conditions,” Tunnelling and Underground Space Technology, vol. 111, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Guangyao Cui, and Xu Ke, “Rescue Technology of the Jamming Accident for the Double-shield TBM in Complex Geological Conditions: A Case Study,” Alexandria Engineering Journal, vol. 79, pp. 374-389, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Qian Zhang et al., “Research on TBM Parameter Optimization Based on Failure Probability,” Engineering Failure Analysis, vol. 167, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Diwakar KC et al., “Challenges in Tunneling in the Himalayas: A Survey of Several Prominent Excavation Projects in the Himalayan Mountain Range of South Asia,” Geotechnics, vol. 2, no. 4, pp. 802-824, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. A. K. Mishra, “Tunnelling Challenges in Himalayas,” Indian Geotechnical Journal, vol. 54, pp. 1821-1833, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Benjamín Celada, and Z.T. Bieniawski, Ground Characterization and Structural Analyses for Tunnel Design, 1st ed., CRC Press, pp. 1-472, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  31. Shilpa Kulkarni, and M.S. Ranadive, “Finite Element Analysis for Parametric Study of Mega Tunnels,” Recent Trends in Construction Technology and Management, pp. 1227-1243, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Shilpa Kulkarni, and M.S. Ranadive, “Investigation for Influence of Pressure on Face Stability of Mega Tunnel,” Proceedings of Geotechnical Challenges in Mining, Tunneling and Underground Infrastructures, pp. 357-369, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Abhishek Srivastav et al., “Boundary Element Coupled Structural Analysis of Lesser Himalayan Railway Tunnels: A Case Study of the Shivpuri–Byasi Section, Rishikesh–Karnaprayag BG Rail Link, Uttarakhand, India,” Journal of Earth System Science, vol. 133, no. 4, pp. 1-15, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. Indian Standard, IS 15026 (Part 1) : 2022Tunnelling in Rock Masses—Guidelines, Part 1: Conventional Tunnelling Methods (First Revision, pp. 142, 2022.
    [    Publisher Link]
  35. Yasar Beg et al., “Tunneling Challenges in Himalayan Region—A Review,” Proceedings of 9IYGEC 2023, pp. 207-217, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  36. Alquamar Azad et al., “Tunnel Support Validation Using Numerical Modelling: A Case Study from NW, Himalaya, India,” Geotechnical and Geological Engineering, vol. 41, pp. 4335-4349, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  37. Weiqiang Wu et al., “Investigation into the Electrohydraulic Synchronous Motion Control of a Thrust System for a Tunnel Boring Machine,” Machines, vol. 10, no. 2, pp. 119-136, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  38. Tengtian Yang, “Investigation on Disaster Mechanism of Diversion Tunnel Induced by Gripper TBM in Hydrokarst Erosion Stratum and Engineering Measures,” Buildings, vol. 14, no. 3, pp. 1-18, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  39. Zhiwen Wang et al., “Numerical Simulation Method for Double Shield TBMs Crossing Weak Zones,” Scientific Reports, vol. 15, pp. 1-16, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  40. Indian Standard, IS 15026 : Part 2 : 2023: Tunnelling In Rock Masses - Guidelines - Part 2 Mechanized Tunnelling Methods By Tunnel Boring Machines, pp. 1-37, 2023.
    [    Publisher Link]
  41.  Recommendations for the Selection of Tunnel Boring Machines, Deutscher Ausschuss für unterirdisches Bauen e. V. (German Tunnelling Committee, ITA-AITES), pp. 1-55, 2022.
    [    Google Scholar] [Publisher Link]
  42. Kursat Kilic et al., “Soft Ground Micro TBM Jack Speed and Torque Prediction using Machine Learning Models through Operator Data and Micro TBM-log Data Synchronization,” Scientific Reports, vol. 14, pp. 1-14, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  43. Khoo Chee, Min Mohamad, and Thansirichaisree, “Some Insights into Three-Dimensional Modeling of Tunnel Excavation,” GEOMATE Journal, vol. 27, no. 120, pp. 27-39, 2024.
    [    Google Scholar] [Publisher Link]
  44. Jim Shiau, and Mathew Sams, “Estimation of Tunneling Induced Ground Settlement using Pressure Relaxation Method,” GEOMATE Journal, vol. 13, no. 39, pp. 132-139, 2017.
    [    Google Scholar] [Publisher Link]
  45. Naeem Abbas et al., “Stress-deformation and Stability Challenges in Himalayan Tunnels: Impact of Geological Discontinuities,” Discover Materials, vol. 4, pp. 1-17, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  46. O.C. Zienkiewicz, R.L. Taylor, and David Fox, The Finite Element Method for Solid and Structural Mechanics,” Elsevier Science,pp. 1-631, 2005.
    [    Google Scholar] [Publisher Link]
  47. Feng Shan et al., “Real-time Forecasting of TBM Cutterhead Torque and Thrust Force using Aware-context Recurrent Neural Networks,” Tunnelling and Underground Space Technology, vol. 152, pp. 1-13, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  48. Chee-Min Khoo et al., “Volume Loss Caused By Tunnelling in Kenny Hill Formation,” GEOMATE Journal, vol. 16, no. 54, pp. 164-169, 2019.
    [    Google Scholar] [Publisher Link]
  49. Pattanasak Chaipanna, and Pornkasem Jongpradist, “3D-FEM Analysis of Shield Tunnel Construction with Ground-spring Model,” GEOMATE Journal, vol. 12, no. 31, pp. 58-62, 2017.
    [    Google Scholar] [Publisher Link]
  50. Peng Zhang, and Pan Li, “Failure Mechanism and Control Technology of the Primary Support for Asymmetric Deformation Disaster of Deep Tunnel,” Periodica Polytechnica Civil Engineering, vol. 69, no. 1, pp. 146-158, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  51. Vivek Kumar, and Praveen Anand, “Study of Tunnelling-Induced Response of Building Frame with Shake Table Test,” Arabian Journal of Scientific Engineering, vol. 49, pp. 5159-5169, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  52. Praveen Anand, and Vivek Kumar, “Framed Building Response to Tunnelling on Different Types of Foundations,” Iran Journal of Science and Technology, Transactions of Civil Engineering, vol. 48, pp. 2235-2247, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  53. Youliang Chen et al., “NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions,” AI, vol. 6, no. 6, pp. 1-36, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  54. Dong Guo et al., “Intelligent Assistant Driving Method for Tunnel Boring Machine based on BIG Data,” Acta Geotechnica, vol. 17, pp. 1019-1030, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  55. Shujun Xu, “Prediction of Tunneling Parameters for Ultra-large Diameter Slurry Shield TBM in Cross-river Tunnels based on Integrated Algorithms,” Geomechanics and Engineering, vol. 38, no. 1, pp. 69-77, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  56. Yongbo Pan, and Xunlin Zhu, “Application of HMM and Ensemble Learning in Intelligent Tunneling,” Mathematics, vol. 10, no. 10, pp. 1-17, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  57. Jelena Ninić, and Günther Meschke, “Model Update and Real-time Steering of Tunnel Boring Machines using Simulation-based Meta Models,” Tunnelling and Underground Space Technology, vol. 45, pp. 138-152, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  58. Jinhui Li et al., “Advanced Prediction of Tunnel Boring Machine Performance based on Big Data,” Geoscience Frontiers, vol. 12, no. 1, pp. 331-338, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  59. Yunfu Jia et al., “Research on Prediction of Excavation Parameters for Deep Buried Tunnel Boring Machine Based on Convolutional Neural Network-Long Short-Term Memory Model,” Buildings, vol. 14, no. 8, pp. 1-18, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  60. Ruohan Wang, Guan Chen, and Yong Liu, “A Dynamic Model of Machine Learning and Deep Learning in Shield Tunneling Parameters Prediction,” Proceedings of the 17th East Asian-Pacific Conference on Structural Engineering and Construction, pp. 1241-1254, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  61. Yu Liang et al., “Prediction of Tunnelling Parameters for Underwater Shield Tunnels, Based on the GA-BPNN Method,” Sustainability, vol. 14, no. 20, pp. 1-15, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  62. Hanan Samadi, Jafar Hassanpour, and Jamal Rostami, “Prediction of Earth Pressure Balance for EPB-TBM using Machine Learning Algorithms,” International Journal of Geo-Engineering, vol. 14, pp. 1-31, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  63. Xiaobang Wang et al., “Sensitivity of TBM’s Performance to Structural, Control and Geological Parameters Under Different Prediction Models,” IEEE Access, vol. 7, pp. 19738-19751, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  64. Liang Chen et al., “Real-Time Prediction of TBM Driving Parameters Using Geological and Operation Data,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 5, pp. 4165-4176, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  65. Lin Wang et al., “Prediction of TBM Tunneling Parameters Based on RF,” 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Hangzhou, China, pp. 406-409, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  66. Xianlei Fu, and Limao Zhang, “Spatio-temporal Feature Fusion for Real-time Prediction of TBM Operating Parameters: A Deep Learning Approach,” Automation in Construction, vol. 132, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  67. Qian Zhang et al., “Theoretical Model for Loads Prediction on Shield Tunneling Machine with Consideration of Soil-rock Interbedded Ground,” Science China Technological Sciences, vol. 56, pp. 2259-2267, 2013.
    [    CrossRef] [Google Scholar] [Publisher Link]
  68. Qian Zhang et al., “Modeling and Prediction for the Thrust on EPB TBMs under Different Geological Conditions by Considering Mechanical Decoupling,” Science China Technological Sciences, vol. 59, pp. 1428-1434, 2016.
    [    CrossRef] [Google Scholar] [Publisher Link]
  69. Hanan Samadi, and Jafar Hassanpour, “EPB-TBM Cutterhead Torque and Thrust Modelling in Rock Tunnels through an Analytical Method and TSFS Model,” Heliyon, vol. 10, no. 11, pp. 1-21, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  70. Li Wu, Tianmin Guan, and Lei Lei, “Discrete Element Model for Performance Analysis of Cutterhead Excavation System of EPB Machine,” Tunnelling and Underground Space Technology, vol. 37, pp. 37-44, 2013.
    [    CrossRef] [Google Scholar] [Publisher Link]
  71. M.D. Han et al., “Dynamic Numerical Simulation of Cutterhead Loads in TBM Tunnelling,” Tunnelling and Underground Space Technology, vol. 70, pp. 286-298, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  72. Diyuan Li et al., “Numerical Analysis of Hard Rock Tunnel Excavated by Double Shield TBM based on CWFS Model,” Journal of Vibroengineering, vol. 21, no. 4, pp. 819-832, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  73. Masoud Zare Naghadehi, and Reza Mikaeil, “Optimization of Tunnel Boring Machine (TBM) Disc Cutter Spacing in Jointed Hard Rock Using a Distinct Element Numerical Simulation,” Periodica Polytechnica Civil Engineering, vol. 61, no. 1, pp. 56-65, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  74. Jelena Ninić, Steffen Freitag, and Günther Meschke, “A Hybrid Finite Element and Surrogate Modelling Approach for Simulation and Monitoring Supported TBM Steering,” Tunnelling and Underground Space Technology, vol. 63, pp. 12-28, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  75. Tao Fu, Tianci Zhang, and Xueguan Song, “A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine,” Energies, vol. 15, no. 8, pp. 1-17, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  76. Shuangli Zhang, Qingfeng Du, and Sicheng Zhao, “Predicting Cutterhead Torque for TBM based on Different Characteristics and AGA-Optimized LSTM-MLP,” 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, pp. 1165-1171, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  77. Yiheng Wang et al., “Prediction Method of TBM Key Tunneling Parameters Based on Real-time Operation Data,” 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), Ningbo, China, pp. 1827-1832, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  78. Long Li et al., “Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine,” IEEE Access, vol. 10, pp. 112695-112712, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  79. Xianjie Gao et al., “Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods,” arXiv preprint, pp. 1-19, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  80. Bin Liu et al., “Intelligent Decision-making Method of TBM Operating Parameters based on Multiple Constraints and Objective Optimization,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 15, no. 11, pp. 2842-2856, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  81. Renchong Wang et al., “Mechanistic Analysis of TBM Cutterhead-ground Interaction under Mud build-up Effect,” Frontiers in Earth Science, vol. 13, pp. 1-11, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  82. Shufang Zhai, Yingjie Song, and and Hao Tian, “Development of Thrust, Torque, and Power Estimation Model, and Prediction Performance of Earth Pressure Balance Tunnel Boring Machine in Mixed-Face Strata,” Applied Sciences, vol. 14, no. 13, pp. 1-19, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  83. Jianwei Lu et al., “A New Prediction Model of Cutterhead Torque in Soil Strata Based on Ultra-Large Section EPB Pipe Jacking Machine,” Infrastructures, vol. 9, no. 12, pp. 1-21, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  84. Qinglong Zhang et al., “Intelligent Tunnelling Robot System for Deep-buried Long Tunnels,” Frontiers in Earth Science, vol. 11, pp. 1-17, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  85. Gabriel Rodriguez Garcia et al., “Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines,” Automation in Construction, vol. 131, pp. 1-12, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  86. Haitao Long et al., “A Dynamic Learning Method Based on the Gaussian Process for Tunnel Boring Machine Intelligent Driving,” Frontiers in Earth Science, vol. 11, pp. 1-13, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  87. Bin Liu et al., “Intelligent Decision Method for Main Control Parameters of Tunnel Boring Machine based on Multi-objective Optimization of Excavation Efficiency and Cost,” Tunnelling and Underground Space Technology, vol. 116, pp. 1-29, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  88. Yapeng Zhang et al., “An Interpretable Probabilistic Prediction Algorithm for Shield Movement Performance,” Frontiers in Earth Science, vol. 12, pp. 1-14, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  89. Feng Shan et al., “Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review,” Eng, vol. 4, no. 2, pp. 1516-1535, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  90.  Jagendra Singh et al., “AI-Driven Innovations in Tunnel Construction and Transport: Enhancing Efficiency with Advanced Machine Learning and Robotics,” The Open Transportation Journal, vol. 19, no. 1, pp. 1-14, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  91. Chairul SalamM, and Orhan Kural, “Predictive Modeling and Optimization of TBM Operations: Advanced Techniques Applied to the Jakarta MRT Project,” Research Square pp. 1-18, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]