QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Xinyu WANG, Jian WU, Xin YIN, Quansheng LIU, Xing HUANG, Yucong PAN, Jihua YANG, Lei HUANG, Shuangping MIAO

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Front. Struct. Civ. Eng. ›› 2023, Vol. 17 ›› Issue (1) : 25-36. DOI: 10.1007/s11709-022-0908-z
RESEARCH ARTICLE
RESEARCH ARTICLE

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

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Abstract

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

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Keywords

tunnel boring machine / control parameter optimization / quantum particle swarm optimization / artificial neural network / tunneling energy efficiency

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Xinyu WANG, Jian WU, Xin YIN, Quansheng LIU, Xing HUANG, Yucong PAN, Jihua YANG, Lei HUANG, Shuangping MIAO. QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency. Front. Struct. Civ. Eng., 2023, 17(1): 25‒36 https://doi.org/10.1007/s11709-022-0908-z

References

[1]
Liu B, Chen L, Wang R R, Wang Y X. Research progress and prospect of adverse geology forward-prospecting and intelligent decision-making of TBM tunneling. In: Proceedings of Sixth International Conference on Engineering Geophysics. Tulsa: Society of Exploration Geophysicists, 2021, 134–138
[2]
Hasanpour R, Rostami J, Thewes M, Schmitt J. Parametric study of the impacts of various geological and machine parameters on thrust force requirements for operating a single shield TBM in squeezing ground. Tunnelling and Underground Space Technology, 2018, 73: 252–260
CrossRef Google scholar
[3]
Shirlaw J N, Henderson T O, Haryono I S, Dudouit F, Salisbury D. The effect of altering the slurry circulation system on TBM tunnelling in weathered Kowloon granite. Tunnelling and Underground Space Technology, 2022, 124: 104474
CrossRef Google scholar
[4]
Ramoni M, Anagnostou G. Thrust force requirements for TBMs in squeezing ground. Tunnelling and Underground Space Technology, 2010, 25(4): 433–455
CrossRef Google scholar
[5]
Zhao K, Janutolo M, Barla G. A completely 3D model for the simulation of mechanized tunnel excavation. Rock Mechanics and Rock Engineering, 2012, 45(4): 475–497
CrossRef Google scholar
[6]
Huo J Z, Wu H Y, Yang J, Sun W, Li G Q, Sun X L. Multi-directional coupling dynamic characteristics analysis of TBM cutterhead system based on tunnelling field test. Journal of Mechanical Science and Technology, 2015, 29(8): 3043–3058
CrossRef Google scholar
[7]
Tiachacht S, Bouazzouni A, Khatir S, Abdel Wahab M, Behtani A, Capozucca R. Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm. Engineering Structures, 2018, 177: 421–430
CrossRef Google scholar
[8]
Dsouza S M, Varghese T M, Budarapu P R, Natarajan S. A non-intrusive stochastic isogeometric analysis of functionally graded plates with material uncertainty. Axioms, 2020, 9(3): 92
CrossRef Google scholar
[9]
VarmaV SYogeshwar RaoRVundavilliP RPanditM KBudarapuP R. A machine learning-based approach for the design of lower limb exoskeleton. International Journal of Computational Methods, 2022, 19(8): 2142012
[10]
Cuong-Le T, Minh H L, Khatir S, Wahab M A, Tran M T, Mirjalili S. A novel version of Cuckoo search algorithm for solving optimization problems. Expert Systems with Applications, 2021, 186: 115669
CrossRef Google scholar
[11]
Wang X, Zhu H H, Zhu M Q, Zhang L Y, Ju J W. An integrated parameter prediction framework for intelligent TBM excavation in hard rock. Tunnelling and Underground Space Technology, 2021, 118: 104196
CrossRef Google scholar
[12]
Gao B Y, Wang R R, Lin C J, Guo X, Liu B, Zhang W G. TBM penetration rate prediction based on the long short-term memory neural network. Underground Space, 2021, 6(6): 718–731
CrossRef Google scholar
[13]
Gao X J, Shi M L, Song X G, Zhang C, Zhang H W. Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 2019, 98: 225–235
CrossRef Google scholar
[14]
Li L, Liu Z B, Zhou H Y, Zhang J, Shen W Q, Shao J F. Prediction of TBM cutterhead speed and penetration rate for high-efficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data. Arabian Journal of Geosciences, 2022, 15(3): 280
CrossRef Google scholar
[15]
Guo D, Li J H, Jiang S H, Li X, Chen Z Y. Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotechnica, 2021, 17(4): 1019–1030
[16]
Wei M, Wang Z L, Wang X Y, Peng J L, Song Y. Prediction of TBM penetration rate based on Monte Carlo-BP neural network. Neural Computing & Applications, 2021, 33(2): 603–611
CrossRef Google scholar
[17]
Acaroglu O. Prediction of thrust and torque requirements of TBMs with fuzzy logic models. Tunnelling and Underground Space Technology, 2011, 26(2): 267–275
CrossRef Google scholar
[18]
Zhang W G, Li H R, Wu C Z, Li Y Q, Liu Z Q, Liu H L. Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling. Underground Space, 2021, 6(4): 353–363
CrossRef Google scholar
[19]
Chen L, Liu Z T, Mao W J, Su H Y, Lin F L. Real-time prediction of tbm driving parameters using in situ geological and operation data. IEEE/ASME Transactions on Mechatronics, 2022, 27(5): 4165–4176
CrossRef Google scholar
[20]
Zhang W G, Li H R, Li Y Q, Liu H L, Chen Y M, Ding X M. Application of deep learning algorithms in geotechnical engineering: A short critical review. Artificial Intelligence Review, 2021, 54(8): 5633–5673
CrossRef Google scholar
[21]
Farrokh E, Rostami J, Laughton C. Study of various models for estimation of penetration rate of hard rock TBMs. Tunnelling and Underground Space Technology, 2012, 30: 110–123
CrossRef Google scholar
[22]
Armaghani D J, Mohamad E T, Narayanasamy M S, Narita N, Yagiz S. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 2017, 63: 29–43
CrossRef Google scholar
[23]
Armetti G, Migliazza M R, Ferrari F, Berti A, Padovese P. Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy). Tunnelling and Underground Space Technology, 2018, 77: 115–126
CrossRef Google scholar
[24]
Yang X, Gong G F, Yang H Y, Jia L H, Ying Q W. A cutterhead energy-saving technique for shield tunneling machines based on load characteristic prediction. Journal of Zhejiang University. Science A, 2015, 16(5): 418–426
CrossRef Google scholar
[25]
Xue Y D, Zhao F, Zhao H X, Li X, Diao Z X. A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study. Tunnelling and Underground Space Technology, 2018, 78: 64–75
CrossRef Google scholar
[26]
Liu B, Wang Y X, Zhao G Z, Yang B, Wang R R, Huang D X, Xiang B. 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, 2021, 116: 104054
CrossRef Google scholar
[27]
Gong Q M, Zhou X X, Liu Y Q, Han B, Yin L J. Development of a real-time muck analysis system for assistant intelligence TBM tunnelling. Tunnelling and Underground Space Technology, 2021, 107: 103655
CrossRef Google scholar
[28]
Xia Y M, Yang M, Mei Y B, Ji Z Y. Influence of geological properties and operational parameters on TBM muck removal performance for Yinsong tunnel. Geotechnical and Geological Engineering, 2022, 40(4): 2291–2306
CrossRef Google scholar
[29]
Khatir S, Boutchicha D, Le Thanh C, Tran-Ngoc H, Nguyen T N, Abdel-Wahab M. Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis. Theoretical and Applied Fracture Mechanics, 2020, 107: 102554
CrossRef Google scholar
[30]
Sharma S, Awasthi R, Sastry Y S, Budarapu P R. Physics-informed neural networks for estimating stress transfer mechanics in single lap joints. Journal of Zhejiang University. Science A, 2021, 22(8): 621–631
CrossRef Google scholar
[31]
Huang X, Yin X, Liu B, Ding Z W, Zhang C F, Jing B Y, Guo X S. A gray wolf optimization-based improved probabilistic neural network algorithm for surrounding rock squeezing classification in tunnel engineering. Frontiers in Earth Science (Lausanne), 2022, 10: 857463
CrossRef Google scholar
[32]
Khatir S, Dekemele K, Loccufier M, Khatir T, Abdel Wahab M. Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization. Comptes Rendus. Mécanique, 2018, 346(2): 110–120
CrossRef Google scholar
[33]
Khatir S, Abdel Wahab M, Boutchicha D, Khatir T. Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis. Journal of Sound and Vibration, 2019, 448: 230–246
CrossRef Google scholar
[34]
Sun J, Feng B, Xu W B. Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation. Portland: IEEE, 2004, 1: 325–331
[35]
Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Abdel Wahab M. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Engineering Structures, 2019, 199: 109637
CrossRef Google scholar
[36]
Wang Z, Wang B, Liu C, Wang W S. Improved BP neural network algorithm to wind power forecast. Journal of Engineering (Stevenage, England), 2017, 2017(13): 940–943
CrossRef Google scholar
[37]
Liu Q S, Wang X Y, Huang X, Yin X. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunnelling and Underground Space Technology, 2020, 106: 103595
CrossRef Google scholar
[38]
Yin X, Liu Q S, Huang X, Pan Y C. Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning. Tunnelling and Underground Space Technology, 2022, 120: 104285
CrossRef Google scholar
[39]
Hou S K, Liu Y R. Early warning of tunnel collapse based on Adam-optimised long short-term memory network and TBM operation parameters. Engineering Applications of Artificial Intelligence, 2022, 112: 104842
CrossRef Google scholar
[40]
Teale R. The concept of specific energy in rock drilling. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1965, 2(1): 57–73
[41]
Mirahmadi M, Tabaei M, Dehkordi M S. Estimation of the specific energy of TBM using the strain energy of rock mass, case study: Amir-Kabir water transferring tunnel of Iran. Geotechnical and Geological Engineering, 2017, 35(5): 1991–2002
CrossRef Google scholar
[42]
Liu B, Wang R, Zhao G, Guo X, Wang Y, Li J, Wang S. Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm. Tunnelling and Underground Space Technology, 2020, 95: 103103
CrossRef Google scholar
[43]
Khatir S, Abdel Wahab M. Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm. Engineering Fracture Mechanics, 2019, 205: 285–300
CrossRef Google scholar
[44]
Reinoso J, Durand P, Budarapu P R, Paggi M. Crack patterns in heterogenous rocks using a combined phase field-cohesive interface modeling approach: A numerical study. Energies, 2019, 12(6): 965
CrossRef Google scholar
[45]
Zhang Q L, Liu Z Y, Tan J R. Prediction of geological conditions for a tunnel boring machine using big operational data. Automation in Construction, 2019, 100: 73–83
CrossRef Google scholar
[46]
Hou S K, Liu Y R, Yang Q. Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(1): 123–143
CrossRef Google scholar
[47]
Zhang Q, Qu C Y, Kang Y L, Huang G Y, Cai Z X, Zhao Y, Zhao H F, Su P C. Identification and optimization of energy consumption by shield tunnel machines using a combined mechanical and regression analysis. Tunnelling and Underground Space Technology, 2012, 28: 350–354
CrossRef Google scholar
[48]
Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32
CrossRef Google scholar
[49]
Zhang W G, Zhang R H, Wu C Z, Goh A T C, Wang L. Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression. Underground Space, 2022, 7(2): 233–241
CrossRef Google scholar
[50]
Cutler A, Cutler D R, Stevens J R. Random Forests. Ensemble Machine Learning. Boston: Springer, 2012, 157–175
[51]
Refaeilzadeh P, Tang L, Liu H. Cross-validation. Encyclopedia of Database Systems, 2009, 5: 532–538

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 41941018, 52074258, 42177140, and 41807250), and the Key Research and Development Project of Hubei Province (No. 2021BCA133). We gratefully acknowledge the support provided.

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