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Knowledge-based and data-driven underground pressure forecasting based on graph structure learning

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Abstract

The pressure prediction technology whereby represents the rock pressure law in the excavation is fundamental to safety in production and industrial intelligentization. A growing number of researchers dedicate that machine learning is used to accurate prediction of underground pressure changes. However, the existing research which based on the classical machine learning rarely considers the cause between inducement of underground pressure and the underground pressure change. In this paper, we propose a novel Reinforced and Causal Graph Neural Network, namely RC-GNN, for the prediction task, to overcome the shortage of causal logic. First, we build a causal graph by considering internal relations between inducement and display of pressure and employ prior knowledge to erect the early and properties of the graph. Second, we construct the prediction network for underground pressure by graph convolutional networks and long short-term memory. Finally, we use the performance index of underground pressure prediction to design a reinforcement learning algorithm, which achieves optimization of the causal graph. Compared to six representative methods, experimental results with 18–60% increases in performance on the real prediction task.

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Acknowledgements

The authors of this paper are supported by the National Key R &D Program of China through Grant 2021YFB1714800, S&T Program of Hebei through Grant 20310101D. We also thank the Natural Science Foundation of Beijing Municipality through Grant 4222030.

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Authors and Affiliations

  1. School of Cyber Science and Technology, Beihang University, Huayuan, Beijing, 100083, Beijing, China

    Yue Wang & Haohua Du

  2. Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050041, Hebei, China

    Mingsheng Liu

  3. HBIS Group Co., Ltd, Shijiazhuang, 050023, Hebei, China

    Yongjian Huang

  4. National Energy Group Shendong Coal Group Bulianta Coal Mine, Ordos, 017209, Inner Mongolia, China

    Haifeng Zhou & Xianhui Wang

  5. School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Senzhang Wang

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  1. Yue Wang
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Correspondence to Yue Wang or Mingsheng Liu.

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Wang, Y., Liu, M., Huang, Y. et al. Knowledge-based and data-driven underground pressure forecasting based on graph structure learning. Int. J. Mach. Learn. & Cyber. 15, 3–18 (2024). https://doi.org/10.1007/s13042-022-01650-3

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  • DOI: https://doi.org/10.1007/s13042-022-01650-3

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