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Frontiers of Structural and Civil Engineering

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Front. Struct. Civ. Eng.    2023, Vol. 17 Issue (1) : 25-36    https://doi.org/10.1007/s11709-022-0908-z
RESEARCH ARTICLE
QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency
Xinyu WANG1,2, Jian WU2,3, Xin YIN2(), Quansheng LIU2, Xing HUANG4, Yucong PAN2, Jihua YANG1, Lei HUANG3, Shuangping MIAO3
1. Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
2. School of Civil Engineering, Wuhan University, Wuhan 430072, China
3. Power China Huadong Engineering Co., Ltd., Hangzhou 311122, China
4. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
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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.
Keywords tunnel boring machine      control parameter optimization      quantum particle swarm optimization      artificial neural network      tunneling energy efficiency     
Corresponding Author(s): Xin YIN   
About author:

Changjian Wang and Zhiying Yang contributed equally to this work.
Just Accepted Date: 24 November 2022   Online First Date: 16 January 2023    Issue Date: 02 March 2023
 Cite this article:   
Xinyu WANG,Jian WU,Xin YIN, et al. QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency[J]. Front. Struct. Civ. Eng., 2023, 17(1): 25-36.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0908-z
https://academic.hep.com.cn/fsce/EN/Y2023/V17/I1/25
Fig.1  Typical three-layer ANN.
Fig.2  QPSO-ANN framework.
technical parameterdesign value
cutterhead diameter (mm)7930
number of hobs56
cutterhead distance (mm)70–89
rated thrust (kN)23260
propulsion cylinder stroke (mm)1800
rated torque (kN·m)8410
maximum cutterhead speed (r·min?1)7.6
Tab.1  Main specifications of the TBM
Fig.3  Proportion of rock mass classes.
Fig.4  Geological profile of study area.
Fig.5  Classification of TBM driving parameters.
Fig.6  Fitting line of driving parameter X in rising stage.
hyperparameterrange of value
Tc2, 5, 10, 50, 100, 150, 200, 250, 300, 400
minLF[1,10]
maxDP[2,50]
Tab.2  Hyperparameter ranges of RF model used in current study
Fig.7  Hyperparameter optimization process.
Fig.8  RF model hyperparameter optimization results.
Fig.9  RF model prediction results.
Fig.10  QPSO-ILF-ANN algorithm framework.
hyperparameterrange of valueoptimization result
POP50, 100, 200, 400, 600, 800, 1000600
β[0.1, 0.5], [0.2, 0.5], [0.3, 0.5], [0.5, 0.7], [0.5, 0.8], [0.5, 0.9], [0.3, 0.7], [0.2, 0.8], [0.1, 0.9][0.3, 0.7]
nLayer1, 22
nNeure[23, 26](32,16)
Tab.3  QPSO-ILF-ANN model hyperparameter optimization results
Fig.11  Model output results: (a) RPM: QPSO-ILF-ANN vs. QPSO-ANN; (b) RPM: QPSO-ILF-ANN vs. operator’s operation; (c) PRev: QPSO-ILF-ANN vs. QPSO-ANN (d) PRev: QPSO-ILF-ANN vs. operator’s operation.
Fig.12  Optimization results of rock-breaking specific energy and penetration rate: (a) Es: QPSO-ILF-ANN vs. QPSO-ANN; (b) Es: QPSO-ILF-ANN vs. operator’s operation; (c) V: QPSO-ILF-ANN vs. QPSO-ANN; (d) V: QPSO-ILF-ANN vs. operator’s operation.
Fig.13  Optimization results of rock-breaking specific energy and penetration rate. (a) QPSO-ANN and QPSO-ILF-ANN; (b) operator’s operation and QPSO-ILF-ANN.
1 B Liu, L Chen, R R Wang, Y X Wang. 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 R Hasanpour, J Rostami, M Thewes, J Schmitt. 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
https://doi.org/10.1016/j.tust.2017.12.027
3 J N Shirlaw, T O Henderson, I S Haryono, F Dudouit, D Salisbury. The effect of altering the slurry circulation system on TBM tunnelling in weathered Kowloon granite. Tunnelling and Underground Space Technology, 2022, 124: 104474
https://doi.org/10.1016/j.tust.2022.104474
4 M Ramoni, G Anagnostou. Thrust force requirements for TBMs in squeezing ground. Tunnelling and Underground Space Technology, 2010, 25(4): 433–455
https://doi.org/10.1016/j.tust.2010.02.008
5 K Zhao, M Janutolo, G Barla. A completely 3D model for the simulation of mechanized tunnel excavation. Rock Mechanics and Rock Engineering, 2012, 45(4): 475–497
https://doi.org/10.1007/s00603-012-0224-3
6 J Z Huo, H Y Wu, J Yang, W Sun, G Q Li, X L Sun. 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
https://doi.org/10.1007/s12206-015-0701-1
7 S Tiachacht, A Bouazzouni, S Khatir, M Abdel Wahab, A Behtani, R Capozucca. Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm. Engineering Structures, 2018, 177: 421–430
https://doi.org/10.1016/j.engstruct.2018.09.070
8 S M Dsouza, T M Varghese, P R Budarapu, S Natarajan. A non-intrusive stochastic isogeometric analysis of functionally graded plates with material uncertainty. Axioms, 2020, 9(3): 92
https://doi.org/10.3390/axioms9030092
9 V S VarmaR Yogeshwar RaoP R VundavilliM K PanditP R Budarapu. A machine learning-based approach for the design of lower limb exoskeleton. International Journal of Computational Methods, 2022, 19(8): 2142012
10 T Cuong-Le, H L Minh, S Khatir, M A Wahab, M T Tran, S Mirjalili. A novel version of Cuckoo search algorithm for solving optimization problems. Expert Systems with Applications, 2021, 186: 115669
https://doi.org/10.1016/j.eswa.2021.115669
11 X Wang, H H Zhu, M Q Zhu, L Y Zhang, J W Ju. An integrated parameter prediction framework for intelligent TBM excavation in hard rock. Tunnelling and Underground Space Technology, 2021, 118: 104196
https://doi.org/10.1016/j.tust.2021.104196
12 B Y Gao, R R Wang, C J Lin, X Guo, B Liu, W G Zhang. TBM penetration rate prediction based on the long short-term memory neural network. Underground Space, 2021, 6(6): 718–731
https://doi.org/10.1016/j.undsp.2020.01.003
13 X J Gao, M L Shi, X G Song, C Zhang, H W Zhang. Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 2019, 98: 225–235
https://doi.org/10.1016/j.autcon.2018.11.013
14 L Li, Z B Liu, H Y Zhou, J Zhang, W Q Shen, J F Shao. 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
https://doi.org/10.1007/s12517-022-09542-0
15 D Guo, J H Li, S H Jiang, X Li, Z Y Chen. Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotechnica, 2021, 17(4): 1019–1030
16 M Wei, Z L Wang, X Y Wang, J L Peng, Y Song. Prediction of TBM penetration rate based on Monte Carlo-BP neural network. Neural Computing & Applications, 2021, 33(2): 603–611
https://doi.org/10.1007/s00521-020-04993-6
17 O Acaroglu. Prediction of thrust and torque requirements of TBMs with fuzzy logic models. Tunnelling and Underground Space Technology, 2011, 26(2): 267–275
https://doi.org/10.1016/j.tust.2010.10.001
18 W G Zhang, H R Li, C Z Wu, Y Q Li, Z Q Liu, H L Liu. Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling. Underground Space, 2021, 6(4): 353–363
https://doi.org/10.1016/j.undsp.2019.12.003
19 L Chen, Z T Liu, W J Mao, H Y Su, F L Lin. Real-time prediction of tbm driving parameters using in situ geological and operation data. IEEE/ASME Transactions on Mechatronics, 2022, 27(5): 4165–4176
https://doi.org/10.1109/TMECH.2022.3152171
20 W G Zhang, H R Li, Y Q Li, H L Liu, Y M Chen, X M Ding. Application of deep learning algorithms in geotechnical engineering: A short critical review. Artificial Intelligence Review, 2021, 54(8): 5633–5673
https://doi.org/10.1007/s10462-021-09967-1
21 E Farrokh, J Rostami, C Laughton. Study of various models for estimation of penetration rate of hard rock TBMs. Tunnelling and Underground Space Technology, 2012, 30: 110–123
https://doi.org/10.1016/j.tust.2012.02.012
22 D J Armaghani, E T Mohamad, M S Narayanasamy, N Narita, S Yagiz. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunnelling and Underground Space Technology, 2017, 63: 29–43
https://doi.org/10.1016/j.tust.2016.12.009
23 G Armetti, M R Migliazza, F Ferrari, A Berti, P Padovese. 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
https://doi.org/10.1016/j.tust.2018.02.012
24 X Yang, G F Gong, H Y Yang, L H Jia, Q W Ying. 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
https://doi.org/10.1631/jzus.A1400323
25 Y D Xue, F Zhao, H X Zhao, X Li, Z X Diao. 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
https://doi.org/10.1016/j.tust.2018.03.030
26 B Liu, Y X Wang, G Z Zhao, B Yang, R R Wang, D X Huang, B Xiang. 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
https://doi.org/10.1016/j.tust.2021.104054
27 Q M Gong, X X Zhou, Y Q Liu, B Han, L J Yin. Development of a real-time muck analysis system for assistant intelligence TBM tunnelling. Tunnelling and Underground Space Technology, 2021, 107: 103655
https://doi.org/10.1016/j.tust.2020.103655
28 Y M Xia, M Yang, Y B Mei, Z Y Ji. Influence of geological properties and operational parameters on TBM muck removal performance for Yinsong tunnel. Geotechnical and Geological Engineering, 2022, 40(4): 2291–2306
https://doi.org/10.1007/s10706-021-02028-y
29 S Khatir, D Boutchicha, C Le Thanh, H Tran-Ngoc, T N Nguyen, M Abdel-Wahab. 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
https://doi.org/10.1016/j.tafmec.2020.102554
30 S Sharma, R Awasthi, Y S Sastry, P R Budarapu. Physics-informed neural networks for estimating stress transfer mechanics in single lap joints. Journal of Zhejiang University. Science A, 2021, 22(8): 621–631
https://doi.org/10.1631/jzus.A2000403
31 X Huang, X Yin, B Liu, Z W Ding, C F Zhang, B Y Jing, X S Guo. 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
https://doi.org/10.3389/feart.2022.857463
32 S Khatir, K Dekemele, M Loccufier, T Khatir, M Abdel Wahab. 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
https://doi.org/10.1016/j.crme.2017.11.008
33 S Khatir, M Abdel Wahab, D Boutchicha, T Khatir. 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
https://doi.org/10.1016/j.jsv.2019.02.017
34 J Sun, B Feng, W B Xu. Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 Congress on Evolutionary Computation. Portland: IEEE, 2004, 1: 325–331
35 H Tran-Ngoc, S Khatir, G De Roeck, T Bui-Tien, M Abdel Wahab. 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
https://doi.org/10.1016/j.engstruct.2019.109637
36 Z Wang, B Wang, C Liu, W S Wang. Improved BP neural network algorithm to wind power forecast. Journal of Engineering (Stevenage, England), 2017, 2017(13): 940–943
https://doi.org/10.1049/joe.2017.0469
37 Q S Liu, X Y Wang, X Huang, X Yin. 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
https://doi.org/10.1016/j.tust.2020.103595
38 X Yin, Q S Liu, X Huang, Y C Pan. 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
https://doi.org/10.1016/j.tust.2021.104285
39 S K Hou, Y R Liu. 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
https://doi.org/10.1016/j.engappai.2022.104842
40 R Teale. The concept of specific energy in rock drilling. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1965, 2(1): 57–73
41 M Mirahmadi, M Tabaei, M S Dehkordi. 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
https://doi.org/10.1007/s10706-017-0222-z
42 B Liu, R Wang, G Zhao, X Guo, Y Wang, J Li, S Wang. 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
https://doi.org/10.1016/j.tust.2019.103103
43 S Khatir, M Abdel Wahab. Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm. Engineering Fracture Mechanics, 2019, 205: 285–300
https://doi.org/10.1016/j.engfracmech.2018.09.032
44 J Reinoso, P Durand, P R Budarapu, M Paggi. Crack patterns in heterogenous rocks using a combined phase field-cohesive interface modeling approach: A numerical study. Energies, 2019, 12(6): 965
https://doi.org/10.3390/en12060965
45 Q L Zhang, Z Y Liu, J R Tan. Prediction of geological conditions for a tunnel boring machine using big operational data. Automation in Construction, 2019, 100: 73–83
https://doi.org/10.1016/j.autcon.2018.12.022
46 S K Hou, Y R Liu, Q Yang. 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
https://doi.org/10.1016/j.jrmge.2021.05.004
47 Q Zhang, C Y Qu, Y L Kang, G Y Huang, Z X Cai, Y Zhao, H F Zhao, P C Su. 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
https://doi.org/10.1016/j.tust.2011.12.003
48 L Breiman. Random forests. Machine Learning, 2001, 45(1): 5–32
https://doi.org/10.1023/A:1010933404324
49 W G Zhang, R H Zhang, C Z Wu, A T C Goh, L Wang. 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
https://doi.org/10.1016/j.undsp.2020.03.001
50 A Cutler, D R Cutler, J R Stevens. Random Forests. Ensemble Machine Learning. Boston: Springer, 2012, 157–175
51 P Refaeilzadeh, L Tang, H Liu. Cross-validation. Encyclopedia of Database Systems, 2009, 5: 532–538
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