Journal of Henan University of Science and Technology(Natural Science), Volume. 46, Issue 4, 73(2025)
Prediction of TBM Penetration Rate Based on NRBO-XGBoost and ABKDE Fusion Interpretable Model
[4] [4] LEVENT O. Development of theoretical equations for predicting tunnel boreability[D]. Golden: Colorado School of Mines, 1977.
[5] [5] JAMAL R. Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure[D]. Golden: Colorado School of Mines, 1997.
[6] [6] SAFFET Y, HALIL K. Prediction of hard rock TBM penetration rate using particle swarm optimization[J]. International journal of rock mechanics and mining sciences, 2011, 48(3): 427-433.
[8] [8] JAHED ARMAGHANI D, FARADONBEH R S, MOMENI E, et al. Performance prediction of tunnel boring machine through developing a gene expression programming equation[J]. Engineering with computers, 2018, 34(1): 129-141.
[9] [9] FARMER I W, GLOSSOP N H. Mechanics of disc cutter penetration[J]. Tunnels and tunnelling international, 1980, 12(6): 22-25.
[10] [10] AMUND B. Hard rock tunnel boring[D]. Trondheim: Norwegian University of Science and Technology, 2000.
[11] [11] HASSANPOUR J, KAZEMI C, ROSTAMI J. Introduction of a modified QTBM model for predicting TBM penetration rate in rock, based on data from mechanized tunneling projects in Iran[J]. Bulletin of engineering geology and the environment, 2024, 83(5): 165.
[16] [16] LIU Z B, WANG Y C, LI L, et al. Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data[J]. Frontiers of structural and civil engineering, 2022, 16(4): 401-413.
[17] [17] SOWMYA R, PREMKUMAR M, JANGIR P. Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems[J]. Engineering applications of artificial intelligence, 2024, 128: 107532.
[26] [26] AGRAWAL A K, MURTHY V M S R, CHATTOPADHYAYA S, et al. Prediction of TBM disc cutter wear and penetration rate in tunneling through hard and abrasive rock using multi-layer shallow neural network and response surface methods [J]. Rock mechanics and rock engineering, 2022, 55(6): 3489-3506.
[27] [27] GAO B, WANG R R, LIN C, et al. TBM penetration rate prediction based on the long short-term memory neural network [J]. Underground space, 2021, 6(6): 718-731.
[28] [28] FU X L, ZHANG L M. Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: a deep learning approach[J]. Automation in construction, 2021, 132: 103937.
[29] [29] ZHOU J, QIU Y G, ARMAGHANI D J, et al. Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques[J]. Geoscience frontiers, 2021, 12(3): 101091.
[30] [30] LI Z M, YAZDANI BEJARBANEH B, ASTERIS P G, et al. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass[J]. Soft computing, 2021, 25(17): 11877-11895.
[31] [31] AFRADI A, EBRAHIMABADI A, HALLAJIAN T. Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)-case study Beheshtabad water conveyance tunnel in Iran[J]. Asian journal of water environment and pollution, 2019, 16(1): 49-57.
[32] [32] KOOPIALIPOOR M, TOOTOONCHI H, JAHED ARMAGHANI D, et al. Application of deep neural networks in predicting the penetration rate of tunnel boring machines[J]. Bulletin of engineering geology and the environment, 2019, 78(8): 6347-6360.
[35] [35] SAMADI H N, KARIMI H, KHAMEHCHIYAN M, et al. Prediction of engineering characteristics of rock masses using actual TBM performance data with supervised and unsupervised learning algorithms (a case study in strong to very strong igneous and pyroclastic rocks)[J]. Rock mechanics and rock engineering, 2024, 57(9): 7223-7252.
Get Citation
Copy Citation Text
YANG Tengjie, GAO Xinqiang, YANG Zhiguo, KONG Chao, DONG Beiyi, LI Tiefeng, ZHU Zhengguo. Prediction of TBM Penetration Rate Based on NRBO-XGBoost and ABKDE Fusion Interpretable Model[J]. Journal of Henan University of Science and Technology(Natural Science), 2025, 46(4): 73
Received: Mar. 10, 2025
Accepted: Aug. 22, 2025
Published Online: Aug. 22, 2025
The Author Email: