References:

<HIDE>

[1]BaiXD, ChengWC, LiG, 2021. A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China. Acta Geotechnica, 16(12):4061-4080.

[2]BouayadD, EmeriaultF, 2017. Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method. Tunnelling and Underground Space Technology, 68:142-152.

[3]BraaksmaJ, KlaassensB, BabušskaR, et al., 2006. Hybrid control design for a robot manipulator in a shield tunneling machine. In: Braz J, Araújo H, Vieira A, et al. (Eds.), Informatics in Control, Automation and Robotics I. Springer, Dordrecht, the Netherlands, p.143-150.

[4]BradleyDA, SewardDW, 1998. The development, control and operation of an autonomous robotic excavator. Journal of Intelligent and Robotic Systems, 21(1):73-97.

[5]BradleyDA, SewardDW, MannJE, et al., 1993. Artificial intelligence in the control and operation of construction plant—the autonomous robot excavator. Automation in Construction, 2(3):217-228.

[6]ChenGQ, 2019. Design and test of intelligent inspection and replacement system of TBM excavation tools. International Conference on Virtual Reality and Intelligent Systems, p.219-222.

[7]ChenK, ChangJD, WangHX, et al., 2016. The fault diagnosis of shield disc cutter based on neural network. Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering, p.752-756.

[8]ChengWC, BaiXD, SheilBB, et al., 2020. Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines. Tunnelling and Underground Space Technology, 106:103592.

[9]CuiYH, AnY, SunW, et al., 2022. Memory-augmented point cloud registration network for bucket pose estimation of the intelligent mining excavator. IEEE Transactions on Instrumentation and Measurement, 71:5003312.

[10]da CostaAZ, FigueroaHEH, FracarolliJA, 2020. Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190:131-144.

[11]DaiZY, LiPN, WangX, et al., 2022. Asymmetric force effect and damage analysis of unlooped segment of large-diameter shield under synchronous propulsion and assembly mode. Applied Sciences, 12(6):2850.

[12]DongCZ, CatbasFN, 2021. A review of computer vision‍–based structural health monitoring at local and global levels. Structural Health Monitoring, 20(2):692-743.

[13]DuL, YuanJJ, BaoS, et al., 2022. Robotic replacement for disc cutters in tunnel boring machines. Automation in Construction, 140:104369.

[14]ElbazK, ShenSL, ZhouAN, et al., 2019. Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm. Applied Sciences, 9(4):780.

[15]ElbazK, ShenSL, ZhouAN, et al., 2021. Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering, 7(2):238-251.

[16]ElbazK, YanT, ZhouAN, et al., 2022. Deep learning analysis for energy consumption of shield tunneling machine drive system. Tunnelling and Underground Space Technology, 123:104405.

[17]ElbazK, ZhouAN, ShenSL, 2023. Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines. Tunnelling and Underground Space Technology, 136:105104.

[18]EralievOMU, LeeKH, ShinDY, et al., 2022. Sensing, perception, decision, planning and action of autonomous excavators. Automation in Construction, 141:104428.

[19]ErharterGH, MarcherT, 2021. On the pointlessness of machine learning based time delayed prediction of TBM operational data. Automation in Construction, 121:103443.

[20]FaramarziL, KheradmandianA, AzhariA, 2020. Evaluation and optimization of the effective parameters on the shield TBM performance: torque and thrust—using discrete element method (DEM). Geotechnical and Geological Engineering, 38(3):2745-2759.

[21]FengZB, ChenHY, ZengTM, et al., 2022. Shield construction multiobjective optimization of surface settlement safety control based on machine learning. Journal of Physics: Conference Series, 2333(1):012018.

[22]FuXC, TaoJF, QinCJ, et al., 2022. A roller state-based fault diagnosis method for tunnel boring machine main bearing using two-stream CNN with multichannel detrending inputs. IEEE Transactions on Instrumentation and Measurement, 71:1-12.

[23]FuXS, GongQM, WuYJ, et al., 2022. Prediction of EPB shield tunneling advance rate in mixed ground condition using optimized BPNN model. Applied Sciences, 12(11):5485.

[24]Gamuda, 2022. Autonomous Tunnel Boring Machine. Gamuda Berhad. https://gamuda-get.‍com/tag/autonomous-tunnel-boring-machine/

[25]Gonzalez-de-SantosP, FernándezR, SepúlvedaD, et al., 2020. Field robots for intelligent farms—inhering features from industry. Agronomy, 10(11):1638.

[26]GuanZC, DengT, JiangYJ, et al., 2014. Probabilistic estimation of ground condition and construction cost for mountain tunnels. Tunnelling and Underground Space Technology, 42:175-183.

[27]GuoZG, WangFT, SunW, 2012. Shield attitude rectification decision function based on support vector data description. In: Yang D (Ed.), Informatics in Control, Automation and Robotics. Springer, Berlin, Germany, p.299-307.

[28]HamledariH, MccabeB, DavariS, 2017. Automated computer vision-based detection of components of under-construction indoor partitions. Automation in Construction, 74:78-94.

[29]HasanpourR, RostamiJ, ThewesM, et al., 2018. 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, 73:252-260.

[30]HeYF, XiaYM, XuZ, et al., 2022. A Φ6-m tunnel boring machine steel arch splicing manipulator. Chinese Journal of Mechanical Engineering, 35(1):31.

[31]HongKR, LiFY, ZhouZJ, et al., 2021. A data-driven method for predicting the cutterhead torque of EPB shield machine. Discrete Dynamics in Nature and Society, 2021:5980081.

[32]HuM, WuBJ, ZhouWB, et al., 2022. Self-driving shield: intelligent systems, methodologies, and practice. Automation in Construction, 139:104326.

[33]HuM, LuJ, ZhouWB, et al., 2023. A multistage model for rapid identification of geological features in shield tunnelling. Scientific Reports, 13(1):1799.

[34]HuangHW, ChangJQ, ZhangDM, et al., 2022. Machine learning-based automatic control of tunneling posture of shield machine. Journal of Rock Mechanics and Geotechnical Engineering, 14(4):1153-1164.

[35]JiaDQ, ShiBH, 2014. Research of shield machine fault prediction system based on improved Elman network algorithm. Proceedings of the 33rd Chinese Control Conference, p.7660-7666.

[36]JiangLJ, SunYM, JiaLH, et al., 2022. Design and trajectory simulation of muck removal robot for TBM. Proceedings of the 2nd International Conference on Robotics and Control Engineering, p.102-107.

[37]JinDL, YuanDJ, MaoJH, 2022. Face failure analysis of a shield tunnel with slurry penetration into the ground. Tunnelling and Underground Space Technology, 126:104554.

[38]KongXX, LingXZ, TangL, et al., 2022. Random forest-based predictors for driving forces of earth pressure balance (EPB) shield tunnel boring machine (TBM). Tunnelling and Underground Space Technology, 122:104373.

[39]KuwaharaH, HaradaM, SenoY, et al., 1988. Application of fuzzy reasoning to the control of shield tunnelling. Doboku Gakkai Ronbunshu, 1988(391):169-178.

[40]LiHT, SuXJ, LiX, 2010. Study on the neural network model for shield construction faults diagnosis. International Conference on Artificial Intelligence and Computational Intelligence, p.286-289.

[41]LiL, TaoJF, YuHD, et al., 2017. Online condition monitoring of gripper cylinder in TBM based on EMD method. Chinese Journal of Mechanical Engineering, 30(6):‍1325-1337.

[42]LiSC, LiuB, XuXJ, et al., 2017. An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology, 63:69-94.

[43]LiSJ, ShangguanZC, SunW, et al., 2009. Controlling earth pressure of shield machine with optimization algorithm. International Conference on Environmental Science and Information Application Technology, p.656-659.

[44]LiXF, GongGF, 2019. Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization. Automation in Construction, 107:102928.

[45]LiXL, ZhangHP, XueYF, et al., 2015. Pressure balance control system for slurry shield based on predictive function control. Proceedings of the 8th International Conference on Intelligent Robotics and Applications, p.501-510.

[46]LiY, EmeriaultF, KastnerR, et al., 2009. Stability analysis of large slurry shield-driven tunnel in soft clay. Tunnelling and Underground Space Technology, 24(4):472-481.

[47]LinPH, ZhangLM, TiongRLK, 2023. Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI. Reliability Engineering & System Safety, 234:109172.

[48]LinSS, ShenSL, ZhangN, et al., 2021. Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms. Geoscience Frontiers, 12(5):101177.

[49]LinSS, ZhangN, ZhouAN, et al., 2022. Time-series prediction of shield movement performance during tunneling based on hybrid model. Tunnelling and Underground Space Technology, 119:104245.

[50]LinSS, ZhouAN, ShenSL, 2023. A coupled deep learning approach for shield moving performance prediction of underground tunnel construction. Gondwana Research, 123:125-139.

[51]LingXZ, KongXX, TangL, et al., 2021. Face stability of the slurry shield‍–‍driven tunnel with an impermeable filter cake in saturated medium sand. Arabian Journal of Geosciences, 14(15):1490.

[52]LiuB, ChenL, LiSC, et al., 2017. Three-dimensional seismic ahead-prospecting method and application in TBM tunneling. Journal of Geotechnical and Geoenvironmental Engineering, 143(12):04017090.

[53]LiuB, ChenL, LiSC, et al., 2018. A new 3D observation system designed for a seismic ahead prospecting method in tunneling. Bulletin of Engineering Geology and the Environment, 77(4):1547-1565.

[54]LiuWL, LiA, LiuCJ, 2022. Multi-objective optimization control for tunnel boring machine performance improvement under uncertainty. Automation in Construction, 139:104310.

[55]LiuXY, ZhangKJ, 2019. Earth pressure balance control of shield tunneling machine based on nonlinear least squares support vector machine model predictive control. Measurement and Control, 52(1-2):3-10.

[56]LiuXY, ShaoC, MaHF, et al., 2011. Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO. Automation in Construction, 20(4):321-327.

[57]LiuXY, XuS, ZhangKJ, 2020. Earth pressure balance control for shield tunneling machine based on adaptive dynamic programming. Transactions of the Institute of Measurement and Control, 42(13):2440-2449.

[58]LuP, YuanDJ, ChenJ, et al., 2021. Face stability analysis of slurry shield tunnels in rock-soil interface mixed ground. KSCE Journal of Civil Engineering, 25(6):2250-2260.

[59]LuSY, ZhangY, SuJJ, 2017. Mobile robot for power substation inspection: a survey. IEEE/CAA Journal of Automatica Sinica, 4(4):830-847.

[60]MahmoodzadehA, ZareS, 2016. Probabilistic prediction of expected ground condition and construction time and costs in road tunnels. Journal of Rock Mechanics and Geotechnical Engineering, 8(5):734-745.

[61]MahmoodzadehA, MohammadiM, Nariman AbdulhamidS, et al., 2021a. Dynamic reduction of time and cost uncertainties in tunneling projects. Tunnelling and Underground Space Technology, 109:103774.

[62]MahmoodzadehA, MohammadiM, Hashim IbrahimH, et al., 2021b. Machine learning forecasting models of disc cutters life of tunnel boring machine. Automation in Construction, 128:103779.

[63]MengZC, YangDJ, HuoJZ, et al., 2021. Development and performance evaluation of an integrated disc cutter system for TBMs. Applied Sciences, 11(2):644.

[64]MokhtariS, NavidiW, MooneyM, 2020. White-box regression (elastic net) modeling of earth pressure balance shield machine advance rate. Automation in Construction, 115:103208.

[65]MourtzisD, 2020. Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7):‍1927-1949.

[66]MourtzisD, AngelopoulosJ, PanopoulosN, 2022. A literature review of the challenges and opportunities of the transition from Industry 4.0 to Society 5.0. Energies, 15(17):6276.

[67]NieLC, ZhouW, XuXJ, et al., 2021. TBM-mounted seismic ahead-prospecting for fast detecting anomalous geology ahead of tunnel face. IEEE Access, 9:359-369.

[68]QinCJ, ShiG, TaoJF, et al., 2021. Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 151:107386.

[69]QinCJ, HuangGQ, YuHG, et al., 2023a. Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction. Geoscience Frontiers, 14(2):101519.

[70]QinCJ, WuRH, HuangGQ, et al., 2023b. A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging. Science China Technological Sciences, 66(2):512-527.

[71]RenDJ, ShenSL, ArulrajahA, et al., 2018. Prediction model of TBM disc cutter wear during tunnelling in heterogeneous ground. Rock Mechanics and Rock Engineering, 51(11):3599-3611.

[72]SchaefferK, MooneyMA, 2016. Examining the influence of TBM-ground interaction on electrical resistivity imaging ahead of the TBM. Tunnelling and Underground Space Technology, 58:82-98.

[73]ShahrourI, ZhangWG, 2021. Use of soft computing techniques for tunneling optimization of tunnel boring machines. Underground Space, 6(3):233-239.

[74]ShangWT, SongZX, ChenZF, et al., 2023. Experimental investigation of face stability of a slurry shield tunnel based on a newly developed model test system. Geotechnical and Geological Engineering, 41(7):4137-4152.

[75]ShaoC, LanDS, 2014. Optimal control of an earth pressure balance shield with tunnel face stability. Automation in Construction, 46:22-29.

[76]SheilB, 2021. Discussion of “on the pointlessness of machine learning based time delayed prediction of TBM operational data” by Georg H. Erharter and Thomas Marcher. Automation in Construction, 124:103559.

[77]ShenSL, ElbazK, ShabanWM, et al., 2022. Real-time prediction of shield moving trajectory during tunnelling. Acta Geotechnica, 17(4):1533-1549.

[78]ShiG, QinCJ, TaoJF, et al., 2021. A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowledge-Based Systems, 228:107213.

[79]ShojaeiK, 2021. Intelligent coordinated control of an autonomous tractor-trailer and a combine harvester. European Journal of Control, 59:82-98.

[80]SongYP, 2011. Research on design of excavating face balance control for large slurry shield. IEEE International Conference on Computer Science and Automation Engineering, p.664-668.

[81]SunW, WangXB, ShiML, et al., 2018. Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization. Advances in Mechanical Engineering, 10(1):1687814018754726.

[82]SunZH, ZhengHY, ShiBH, 2019. Faults prediction and diagnoses of shield machine based on LSTM. Chinese Control and Decision Conference, p.5254-5259.

[83]SunusiII, ZhouJ, WangZZ, et al., 2020. Intelligent tractors: review of online traction control process. Computers and Electronics in Agriculture, 170:105176.

[84]SutterB, LelevéA, PhamMT, et al., 2018. A semi-autonomous mobile robot for bridge inspection. Automation in Construction, 91:111-119.

[85]TakeiT, IchikawaK, OkawaK, et al., 2013. Path planning of wheel loader type robot for scooping and loading operation by genetic algorithm. Proceedings of the 13th International Conference on Control, Automation and Systems, p.1494-1499.

[86]TakeiT, HoshiT, SarataS, et al., 2015. Simultaneous determination of an optimal unloading point and paths between scooping points and the unloading point for a wheel loader. IEEE/RSJ International Conference on Intelligent Robots and Systems, p.5923-5929.

[87]VargasJP, KoppeJC, PérezS, 2014. Monte Carlo simulation as a tool for tunneling planning. Tunnelling and Underground Space Technology, 40:203-209.

[88]WangLT, YangX, GongGF, et al., 2018a. Pose and trajectory control of shield tunneling machine in complicated stratum. Automation in Construction, 93:192-199.

[89]WangLT, SunW, LongYY, et al., 2018b. Reliability-based performance optimization of tunnel boring machine considering geological uncertainties. IEEE Access, 6:19086-19098.

[90]WangQ, XieXY, ShahrourI, 2020. Deep learning model for shield tunneling advance rate prediction in mixed ground condition considering past operations. IEEE Access, 8:215310-215326.

[91]WangQ, XieXY, YuHJ, et al., 2021. Predicting slurry pressure balance with a long short-term memory recurrent neural network in difficult ground condition. Computational Intelligence and Neuroscience, 2021:6678355.

[92]WangXY, WuJ, YinX, et al., 2023. QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency. Frontiers of Structural and Civil Engineering, 17(1):25-36.

[93]WeiLJ, MageeDR, CohnAG, 2018. An anomalous event detection and tracking method for a tunnel look-ahead ground prediction system. Automation in Construction, 91:216-225.

[94]XiaoH, XingB, WangY, et al., 2021. Prediction of shield machine attitude based on various artificial intelligence technologies. Applied Sciences, 11(21):10264.

[95]XieHB, DuanXM, YangHY, et al., 2012. Automatic trajectory tracking control of shield tunneling machine under complex stratum working condition. Tunnelling and Underground Space Technology, 32:87-97.

[96]XieHB, LiuZB, YangHY, 2016. Pressure regulation for earth pressure balance control on shield tunneling machine by using adaptive robust control. Chinese Journal of Mechanical Engineering, 29(3):598-606.

[97]XuC, ZhuYJ, SongDQ, et al., 2022. Spacing optimization of the TBM disc cutter rock fragmentation, based on the energy entropy method. Sustainability, 14(20):13226.

[98]XuDM, WangYS, HuangJQ, et al., 2023. Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms. Tunnelling and Underground Space Technology, 131:104852.

[99]YangHY, ShiH, GongGF, et al., 2009. Earth pressure balance control for EPB shield. Science in China Series E: Technological Sciences, 52(10):2840-2848.

[100]YehIC, 1997. Application of neural networks to automatic soil pressure balance control for shield tunneling. Automation in Construction, 5(5):421-426.

[101]YuHG, TaoJF, QinCJ, et al., 2022. A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 165:108353.

[102]YuSN, JangJH, HanCS, 2007. Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Automation in Construction, 16(3):255-261.

[103]YuY, HanC, 2010. Fault diagnosis of metro shield machine based on rough set and neural network. Proceedings of the 3rd International Conference on Intelligent Networks and Intelligent Systems, p.588-591.

[104]YuanJJ, GuanRM, DuJL, 2019. Design and implementation of disc cutter changing robot for tunnel boring machine (TBM). IEEE International Conference on Robotics and Biomimetics, p.2402-2407.

[105]YuanJJ, GuanRM, GuoDZ, et al., 2020a. Discussion on the robotic approach of disc cutter replacement for shield machine. IEEE International Conference on Real-Time Computing and Robotics, p.204-209.

[106]YuanJJ, GuanRM, DuL, et al., 2020b. A robotic gripper design and integrated solution towards tunnel boring construction equipment. IEEE/RSJ International Conference on Intelligent Robots and Systems, p.2650-2655.

[107]YueM, SunW, WeiJ, 2011. Sliding mode robust controller for automatic rectification of shield machine. Journal of Central South University, 18(2):536-541.

[108]YueM, SunW, HuP, 2012. Dynamic coordinated control of attitude correction for the shield tunneling based on load observer. Automation in Construction, 24:24-29.

[109]ZhangF, GaoYF, WuYX, et al., 2018. Face stability analysis of large-diameter slurry shield-driven tunnels with linearly increasing undrained strength. Tunnelling and Underground Space Technology, 78:178-187.

[110]ZhangHD, MengZC, GuoZG, et al., 2021. Motion control of disc cutter changing robot body for tunnel boring machine. IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, p.1300-1305.

[111]ZhangP, ChenRP, WuHN, 2019. Real-time analysis and regulation of EPB shield steering using random forest. Automation in Construction, 106:102860.

[112]ZhangQ, QuCY, KangYL, et al., 2012. Identification and optimization of energy consumption by shield tunnel machines using a combined mechanical and regression analysis. Tunnelling and Underground Space Technology, 28:350-354.

[113]ZhangQL, LiuZY, TanJR, 2019. Prediction of geological conditions for a tunnel boring machine using big operational data. Automation in Construction, 100:73-83.

[114]ZhangQL, ZhuYW, MaR, et al., 2023. Intelligent tunnelling robot system for deep-buried long tunnels. Frontiers in Earth Science, 11:1135948.

[115]ZhangTR, GengL, ChenXL, et al., 2012. Research on fault diagnosis of TBM main bearing based on improved BP neural network. Proceedings of the UKACC International Conference on Control, p.579-583.

[116]ZhangTR, WangZY, YuTB, et al., 2013. Research on fault diagnosis for TBM based on wavelet packet transforms and BP neural network. Proceedings of the 3rd IEEE International Advance Computing Conference, p.677-681.

[117]ZhangXP, TangSH, LiuQS, et al., 2021. An experimental study on cutting tool hardness optimization for shield TBMs during dense fine silty sand ground tunneling. Bulletin of Engineering Geology and the Environment, 80(9):6813-6826.

[118]ZhangYK, GongGF, YangHY, et al., 2020. Precision versus intelligence: autonomous supporting pressure balance control for slurry shield tunnel boring machines. Automation in Construction, 114:103173.

[119]ZhangYK, GongGF, YangHY, et al., 2022. Towards autonomous and optimal excavation of shield machine: a deep reinforcement learning-based approach. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 23(6):458-478.

[120]ZhouC, DingLY, HeR, 2013. PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze river. Automation in Construction, 36:208-217.

[121]ZhouC, DingLY, SkibniewskiMJ, et al., 2018. Data based complex network modeling and analysis of shield tunneling performance in metro construction. Advanced Engineering Informatics, 38:168-186.

[122]ZhouC, XuHC, DingLY, et al., 2019. Dynamic prediction for attitude and position in shield tunneling: a deep learning method. Automation in Construction, 105:102840.

[123]ZhouSY, LiuSL, KangYL, et al., 2022. Physics-based machine learning method and the application to energy consumption prediction in tunneling construction. Advanced Engineering Informatics, 53:101642.

[124]ZhouXH, GongGF, ZhangYK, et al., 2023. Performance evaluation of TBM using an improved load prediction model. Machines, 11(2):141.

[125]ZhuT, XieHB, YangHY, 2022. Design and tracking control of an electro-hydrostatic actuator for a disc cutter replacement manipulator. Automation in Construction, 142:104480.

[126]ZhuYT, ZhaiYX, MinR, et al., 2021. Model test on the synchronous technology combining with shield tunneling and segment assembling based on the linear distribution principle of the thrust force. IOP Conference Series: Earth and Environmental Science, 861:052079.

[127]ZouL, LiangL, 2018. Fault diagnosis of shield machine based on SOM-BP neural network fusion. International Conference on Sensing, Diagnostics, Prognostics, and Control, p.232-237.