Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation

Abstract

Research on automation and intelligent operation of tunnel boring machine (TBM) is receiving more and more attention, benefiting from the increasing construction data. However, most studies on TBM operations optimization were trained by the labels of human drivers’ decisions, which were subjective and stochastic. As a result, the control parameters suggested by these models could hardly surpass the performance of a human driver, even the possibility of subjective incorrect decisions. Considering that the geomechanical feedback to TBM under drivers’ actions is objective, in this paper, a transformer-based model called the geological response for tunnel boring machine (GRTBM), is proposed to learn the relationship between operation-adjust and TBM monitoring changes. Additionally, with the model-based offline reinforcement learning, this paper provided a novel approach to optimizing the TBM excavation operations. The decision processes, recorded in the Yin-song TBM project for a waterway tunnel in Jilin Province of China, were used for the validation of the model. By adopting an implicit perception of geological conditions in the GRTBM model, the suggested method achieved the desired state within a single action, greatly outperformed the practical adjustments where 500 s were taken, revealing the fact that the proposed model has the potential to surpass the capability of human beings.

Keywords

TBM / Reinforcement learning / Transformer / Data mining

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Yupeng Cao: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Wei Luo: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing. Yadong Xue: Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review & editing. Weiren Lin: Formal analysis, Investigation, Supervision, Writing - original draft, Writing - review & editing. Feng Zhang: Conceptualization, Supervision, Writing - original draft, Writing - review & editing.

Declaration of competing interest

Feng Zhang is a managing editor for Underground Space and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgement

Thanks for the work support by the JST SPRING (Grant No. JPMJSP2110), and the National Natural Science Foundation of China (Grant No. 52078377). The authors are grateful for the data support provided by China Railway Engineering Equipment Group CO., Ltd. and Chinese Institute of Water Resources and Hydropower Research. Also thank Zhu Ruitong and Feng Shuai for the English grammar checking.

References

[1] Armaghani, D. J., Koopialipoor, M., Marto, A., & Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11(4), 779-789.

[2] Bansal, S., Calandra, R., Chua, K., Levine, S., & Tomlin, C. (2017). MBMF: Model-based priors for model-free reinforcement learning [preprint]. Arxiv. https://doi.org/arXiv:1709.03153.

[3] Belletti, F., Beutel, A., Jain, S., & Chi, E. (2018). Factorized recurrent neural architectures for longer range. In Dependence proceedings of the twenty-first international conference on artificial intelligence and statistics, Proceedings of Machine Learning Research. https://proceedings.mlr.press/v84/belletti18a.html.

[4] Berner, C., Brockman, G., Chan, B., Cheung, V., Dezbiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., Pinto, H., Raiman, J., Salimans, T., Schlatter, J., & Zhang, S. (2019). Dota 2 with Large Scale Deep Reinforcement Learning. Arxiv. https://doi.org/10.48550/arXiv.1912.06680.

[5] Bo, Y., Liu, Q., Huang, X., & Pan, Y. (2022). Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization. Tunnelling and Underground Space Technology, 124, 104448.

[6] Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012). A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1-43.

[7] Chandrashekar, L., & Bhatnagar, S. (2014). Approximate dynamic programming with (min, plus) linear function approximation for Markov decision processes. In 53rd IEEE conference on decision and control (pp. 1588-1593), Los Angeles, CA.

[8] Chen, H., Li, X., Feng, Z., Wang, L., Qin, Y., Skibniewski, M. J., Chen, Z.-S., & Liu, Y. (2023). Shield attitude prediction based on Bayesian- LGBM machine learning. Information Sciences, 632, 105-129.

[9] Chen, L., Liu, Z., Mao, W., Su, H., & Lin, F. (2022a). Real-time prediction of TBM driving parameters using in situ geological and operation data. IEEE/ASME Transactions on Mechatronics, 27, 1-12.

[10] Chen, L., Liu, Z., Su, H., Lin, F., & Mao, W. (2022b). Automated rock mass condition assessment during TBM tunnel excavation using deep learning. Scientific Reports, 12(1), 1722.

[11] Coulom, R. (2006). Efficient selectivity and backup operators in Monte- Carlo tree search. In 5th International conference on computers and games, Turin, Italy. Lecture Notes in Computer Science [Computers and games].

[12] Deng, J., Sierla, S., Sun, J., & Vyatkin, V. (2022). Reinforcement learning for industrial process control: A case study in flatness control in steel industry. Computers in Industry, 143, 103748.

[13] Erharter, G. H., Hansen, T. F., Liu, Z., & Marcher, T. (2021). Reinforcement learning based process optimization and strategy development in conventional tunneling. Automation in Construction, 127, 103701.

[14] Fang, J., Song, X., Yao, N., & Shi, M. (2021). Application of FCM algorithm combined with articial neural network in TBM operation data. Computer Modeling in Engineering & Sciences, 126(1), 397-417.

[15] Feng, S., Chen, Z., Luo, H., Wang, S., Zhao, Y., Liu, L., Ling, D., & Jing, L. (2021). Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning. Tunnelling and Underground Space Technology, 110, 103636.

[16] Fu, X., Wu, M., Ponnarasu, S., & Zhang, L. (2023a). A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns. Expert Systems with Applications, 212, 118721.

[17] Fu, X., Wu, M., Tiong, R. L. K., & Zhang, L. (2023b). Data-driven realtime advanced geological prediction in tunnel construction using a hybrid deep learning approach. Automation in Construction, 146, 104672.

[18] Fu, X., & Zhang, L. (2021). Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach. Automation in Construction, 132, 103937.

[19] Gao, X., Shi, M., Song, X., Zhang, C., & Zhang, H. (2019). Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 98, 225-235.

[20] Gašperov, B., Begušić S., Šimović P., & Kostanjčar, Z. (2021). Reinforcement learning approaches to optimal market making. Mathematics, 9(21), 2689.

[21] Geng, Q., He, F., Ma, M., Liu, X., Wang, X., Zhang, Z., & Ye, M. (2022). Application of full-scale experimental cutterhead system to study penetration performance of tunnel boring machines (TBMs). Rock Mechanics and Rock Engineering, 55(8), 4673-4696.

[22] Gong, Q., Zhou, X., Liu, Y., Han, B., & Yin, L. (2021). Development of a real-time muck analysis system for assistant intelligence TBM tunnelling. Tunnelling and Underground Space Technology, 107, 103655.

[23] Guo, D., Li, J., Jiang, S.-H., Li, X., & Chen, Z. (2022). Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotechnica, 17(4), 1019-1030.

[24] Hassanpour, J., Rostami, J., & Zhao, J. (2011). A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, 26(5), 595-603.

[25] Hemmati, M., & Hadavi, M. A. (2022). Bypassing Web application firewalls using deep reinforcement learning. Isecure-Isc International Journal of Information Security, 14( 2), 131-145.

[26] Herrenknecht. GRIPPER TBM. https://www.herrenknecht.com/en/products/productdetail/gripper-tbm/ [2023-6-20 accesed].

[27] Hou, S., Liu, Y., & Yang, Q. (2022). 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, 14(1), 123-143.

[28] Huang, X., Zhang, Q., Liu, Q., Liu, X., Liu, B., Wang, J., & Yin, X. (2022). A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm. Journal of Rock Mechanics and Geotechnical Engineering, 14(3), 798-812.

[29] Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Pérez, P. (2022). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926.

[30] Kumar, C. V., Vardhan, H., & Murthy, C. S. N. (2022). Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations. Modeling Earth Systems and Environment, 8(1), 141-161.

[31] Leng, S., Lin, J.-R., Hu, Z.-Z., & Shen, X. (2020). A hybrid data mining method for tunnel engineering based on real-time monitoring data from tunnel boring machines. IEEE Access, 8, 90430-90449.

[32] Li, J., Li, P., Guo, D., Li, X., & Chen, Z. (2021). Advanced prediction of tunnel boring machine performance based on big data. Geoscience Frontiers, 12(1), 331-338.

[33] Li, L., Liu, Z., Zhou, H., Zhang, J., Shen, W., & Shao, J. (2022). Prediction of TBM cutterhead speed and penetration rate for highefficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data. Arabian Journal of Geosciences, 15(3), 280.

[34] Li, S., Chen, L., Liu, B., Xu, X., Liu, L., & Chen, Y. (2022). Geologic forward prospecting using improved tunnel-seismic-while-drilling method: A case study of the water supply project at Songhua River, Jilin, China. Geophysics, 87(2), B93-B104.

[35] Li, S., Liu, B., Xu, X., Nie, L., Liu, Z., Song, J., Sun, H., Chen, L., & Fan, K. (2017). An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology, 63, 69-94.

[36] Li, X., Zhao, S., Shen, Y., Li, G., & Zhu, H. (2023). Integrated parameter optimization approach: Just-in-time (JIT) operational control strategy for TBM tunnelling. Tunnelling and Underground Space Technology, 135, 105040.

[37] Lin, S., Wan, J., Xu, T., Liang, Y., & Zhang, J. (2022). Model-based offline meta-reinforcement learning with regularization. arXiv preprint ar- Xiv:2202.02929.

[38] Liu, B., Wang, R., Zhao, G., Guo, X., Wang, Y., Li, J., & Wang, S. (2020). Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm. Tunnelling and Underground Space Technology, 95, 103103.

[39] Liu, B., Wang, Y., Zhao, G., Yang, B., Wang, R., Huang, D., & Xiang, B. (2021). 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, 116, 104054.

[40] Liu, M.-B., Liao, S.-M., Men, Y.-Q., Xing, H.-T., Liu, H., & Sun, L.-Y. (2022). Field monitoring of TBM vibration during excavating changing stratum: Patterns and ground identification. Rock Mechanics and Rock Engineering, 55(3), 1481-1498.

[41] Liu, M. B., Liao, S., Yang, Y., Men, Y., He, J., & Huang, Y. (2021). Tunnel boring machine vibration-based deep learning for the ground identification of working faces. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1340-1357.

[42] Liu, M. H., Ren, S.Q., Ma, S.Y., Jiao, J. H., Chen, Y.Z., Wang, Z.G., & Song, W. (2021). Gated transformer networks for multivariate time series classification [preprint]. Arxiv. https://doi.org/arXiv:2103.14438.

[43] Liu, Q., Huang, X., Gong, Q., Du, L., Pan, Y., & Liu, J. (2016). Application and development of hard rock TBM and its prospect in China. Tunnelling and Underground Space Technology, 57, 33-46.

[44] Liu, Q., Liu, J., Pan, Y., Kong, X., & Hong, K. (2017). A case study of TBM performance prediction using a Chinese rock mass classification system - Hydropower Classification (HC) method. Tunnelling and Underground Space Technology, 65, 140-154.

[45] Liu, Q., Wang, X., Huang, X., & Yin, X. (2020). Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunnelling and Underground Space Technology, 106, 103595.

[46] Liu, Z., Li, L., Fang, X., Qi, W., Shen, J., Zhou, H., & Zhang, Y. (2021). Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network. Automation in Construction, 125, 103647.

[47] Moerland, T., Broekens, J., & M. Jonker, C. (2017). Learning multimodal transition dynamics for model-based reinforcement learning [preprint]. Arxiv. https://doi.org/arXiv:1705.00470.

[48] Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural Networks, 152, 267-275.

[49] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.

[50] Nelson, P. P., O’rourke, T. D., & Kulhawy, F. H. (1983). Factors Affecting Tbm Penetration Rates In Sedimentary Rocks. In ARMA US rock mechanics/geomechanics symposium (pp. 117) ARMA- 83.

[51] ARMA. Pan, Y., Fu, X., & Zhang, L. (2022). Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach. Automation in Construction, 141, 104386.

[52] Qin, C., Huang, G., Yu, H., Wu, R., Tao, J., & Liu, C. (2023). Geological information prediction for shield machine using an enhanced multihead self-attention convolution neural network with two-stage feature extraction. Geoscience Frontiers, 14(2), 101519.

[53] Qin, C., Shi, G., Tao, J., Yu, H., Jin, Y., Lei, J., & Liu, C. (2021). Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 151, 107386.

[54] Salimi, A., Rostami, J., & Moormann, C. (2019). Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms. Tunnelling and Underground Space Technology, 92, 103046.

[55] Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420.

[56] Shan, F., He, X., Jahed Armaghani, D., Zhang, P., & Sheng, D. (2022). Success and challenges in predicting TBM penetration rate using recurrent neural networks. Tunnelling and Underground Space Technology, 130, 104728.

[57] Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

[58] Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through selfplay. Science, 362(6419), 1140-1144.

[59] Singh, B., Kumar, R., & Singh, V. P. (2022). Reinforcement learning in robotic applications: A comprehensive survey. Artificial Intelligence Review, 55(2), 945-990.

[60] Snowdon, R. A., Ryley, M. D., & Temporal, J. (1982). A study of disc cutting in selected British rocks. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 19(3), 107-121.

[61] Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Reinforcement learning: An introduction. MIT Press.

[62] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

[63] Wan, L., Lan, X., Zhang, H., & Zheng, N. (2019). A review of deep reinforcement learning theory and application. Pattern Recognition and Artificial Intelligence, 32(1), 67-81 (in Chinese).

[64] Wang, K., Wu, X., Zhang, L., & Song, X. (2023a). Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach. Advanced Engineering Informatics, 55, 101854.

[65] Wang, K., Zhang, L., & Fu, X. (2023b). Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI). Automation in Construction, 147, 104730.

[66] Wang, X., Wu, J., Yin, X., Liu, Q., Huang, X., Pan, Y., Yang, J., Huang, L., & Miao, S. (2023). QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency. Frontiers of Structural and Civil Engineering, 17(1), 25-36.

[67] Wang, X., Zhu, H., Zhu, M., Zhang, L., & Ju, J. W. (2021). An integrated parameter prediction framework for intelligent TBM excavation in hard rock. Tunnelling and Underground Space Technology, 118, 104196.

[68] Wei, M., Wang, Z., Wang, X., Peng, J., & Song, Y. (2021). Prediction of TBM penetration rate based on Monte Carlo-BP neural network. Neural Computing and Applications, 33(2), 603-611.

[69] Wen, S., & Zhang, C. (2022). Experimental and simulation study on rockbreaking efficiency of disc cutters on composite rocks. International Journal of Rock Mechanics and Mining Sciences, 153, 105089.

[70] Xiao, H., Chen, Z., Cao, R., Cao, Y., Zhao, L., & Zhao, Y. (2022). Prediction of shield machine posture using the GRU algorithm with adaptive boosting: A case study of Chengdu Subway project. Transportation Geotechnics, 37, 100837.

[71] Xu, J., Zhang, Z., Zhang, L., & Liu, D. (2023). Predicting shield position deviation based on double-path hybrid deep neural networks. Automation in Construction, 148, 104775.

[72] Xu, Z. H., Wang, W. Y., Lin, P., Nie, L. C., Wu, J., & Li, Z. M. (2021). Hard-rock TBM jamming subject to adverse geological conditions: Influencing factor, hazard mode and a case study of Gaoligongshan Tunnel. Tunnelling and Underground Space Technology, 108, 103683.

[73] Xue, Y. D., Zhao, F., Zhao, H. X., Li, X., & Diao, Z. X. (2018). A new method for selecting hard rock TBM tunnelling parameters using optimum energy: A case study. Tunnelling and Underground Space Technology, 78, 64-75.

[74] Xue, Y., Wang, J., Zhou, M., Liu, J., Guo, Y., & Wang, J. (2022). Prediction of optimum TBM penetration strategy with minimum energy consumption in hard rocks. Computers and Geotechnics, 148, 104844.

[75] Xue, Y., Zhou, J., Liu, C., Shadabfar, M., & Zhang, J. (2021). Rock fragmentation induced by a TBM disc-cutter considering the effects of joints: A numerical simulation by DEM. Computers and Geotechnics, 136, 104230.

[76] Xue, Z., Chen, L., Liu, Z., Lin, F., & Mao, W. (2021). Rock segmentation visual system for assisting driving in TBM construction. Machine Vision and Applications, 32(4), 77.

[77] Yang, H., Song, K., & Zhou, J. (2022). Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines. Rock Mechanics and Rock Engineering, 55(3), 1499-1516.

[78] Yang, Z., He, B., Liu, Y., Wang, D., & Zhu, G. (2021). Classification of rock fragments produced by tunnel boring machine using convolutional neural networks. Automation in Construction, 125, 103612.

[79] Yıldız, A. Y., Koç E., & Koç A. (2022). Multivariate Time Series Imputation With Transformers. IEEE Signal Processing Letters, 29, 2517-2521.

[80] Yin, X., Liu, Q., Huang, X., & Pan, Y. (2022). Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning. Tunnelling and Underground Space Technology, 120, 104285.

[81] Yu, H., Li, Y., & Li, L. (2020). Evaluating some dynamic aspects of TBMs performance in uncertain complex geological structures. Tunnelling and Underground Space Technology, 96, 103216.

[82] Yu, H., Qin, C., Tao, J., Liu, C., & Liu, Q. (2023a). A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction. Tunnelling and Underground Space Technology, 133, 104949.

[83] Yu, H., Sun, H., Jianfeng, T., Qin, C., Xiao, D., Jin, Y., & Liu, C. (2023b). A multi-stage data augmentation and AD-ResNet-based method for EPB utilization factor prediction. Automation in Construction, 147, 104734.

[84] Yu, H., Tao, J., Qin, C., Xiao, D., Sun, H., & Liu, C. (2021). Rock mass type prediction for tunnel boring machine using a novel semisupervised method. Measurement, 179, 109545.

[85] Yun, B. (2019). Chapter 4 - Underground construction. In B. Yun (Ed.), Underground engineering (pp. 117-204). Academic Press.

[86] Zeng, J., Roy, B., Kumar, D., Mohammed, A. S., Armaghani, D. J., Zhou, J., & Mohamad, E. T. (2022). Proposing several hybrid PSOextreme learning machine techniques to predict TBM performance. Engineering with Computers, 38(5), 3811-3827.

[87] Zhang, C., Liang, M., Song, X., Liu, L., Wang, H., Li, W., & Shi, M. (2022). Generative adversarial network for geological prediction based on TBM operational data. Mechanical Systems and Signal Processing, 162, 108035.

[88] Zhang, D., Fu, L., Huang, H., Wu, H., & Li, G. (2022). Deep learningbased automatic detection of muck types for earth pressure balance shield tunneling in soft ground. Computer-Aided Civil and Infrastructure Engineering, 38.

[89] Zhang, Q., Liu, Z., & Tan, J. (2019). Prediction of geological conditions for a tunnel boring machine using big operational data. Automation in Construction, 100, 73-83.

[90] Zhang, Q., Zhu, Y., Ma, R., Du, C., Du, S., Shao, K., & Li, Q. (2022). Prediction method of TBM tunneling parameters based on PSO-Bi- LSTM model. Frontiers in Earth Science, 10, 854807.

[91] Zhang, Y. K., Gong, G. F., Yang, H. Y., Chen, Y. X., & Chen, G. L. (2022). Towards autonomous and optimal excavation of shield machine: A deep reinforcement learning-based approach. Journal of Zhejiang University-Science A, 23, 458-478.

[92] Zhang, Y., Chen, Z., Jin, F., Jing, L., Xing, H., & Li, P. (2023). Crossproject prediction for rock mass using shuffled TBM big dataset and knowledge-based machine learning methods. Science China Technological Sciences, 66(3), 751-770.

[93] Zhao, J., Shi, M., Hu, G., Song, X., Zhang, C., Tao, D., & Wu, W. (2019). A data-driven framework for tunnel geological-type prediction based on TBM operating data. IEEE Access, 7, 66703-66713.

[94] Zheng, Z., Wang, F., Gong, G., Yang, H., & Han, D. (2023). Intelligent technologies for construction machinery using data-driven methods. Automation in Construction, 147, 104711.

[95] Zhou, C., Kong, T., Zhou, Y., Zhang, H., & Ding, L. (2019). Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory. Automation in Construction, 107, 102924.

[96] Zhou, J., Qiu, Y., Armaghani, D. J., Zhang, W., Li, C., Zhu, S., & Tarinejad, R. (2021a). Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques. Geoscience Frontiers, 12(3), 101091.

[97] Zhou, J., Qiu, Y., Zhu, S., Armaghani, D. J., Li, C., Nguyen, H., & Yagiz, S. (2021b). Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence, 97, 104015.

[98] Zhu, M., Gutierrez, M., Zhu, H., Ju, J. W., & Sarna, S. (2021). Performance Evaluation Indicator (PEI): A new paradigm to evaluate the competence of machine learning classifiers in predicting rockmass conditions. Advanced Engineering Informatics, 47, 101232.