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Probabilistic prediction of the spatial distribution of potential key blocks during tunnel surrounding rock excavation

Author

Listed:
  • Peng He

    (Shandong University of Science and Technology)

  • Li-ping Li

    (Shandong University)

  • Gang Wang

    (Shandong University of Science and Technology)

  • Fei Xu

    (Shijiazhuang Tiedao University)

  • Shang-qu Sun

    (Shandong University of Science and Technology)

Abstract

Jointed network simulations tend to be more random in nature due to the uncertainty of rock mass structures. In this paper, a series of jointed network models can be established in batches using Monte Carlo simulation (MSC) and loop iteration. Taking the joints, tunnel profile and their intersections as the edges E and vertices V of graph G, the jointed network model can serve as an unweighted undigraph. Then, the breadth-first search is introduced to search the closed paths around the tunnel profile, such as the potential key blocks. With batch simulation of network models, the spatial distribution characteristics and probability distribution rules of blocks can be automatically analysed during the search process. For comparison, the Laohushan tunnel of the Jinan Belt Expressway in China has been analysed using the breadth-first search, discontinuous deformation analysis method and procedure of “Finding the Key Blocks-Unrolled Tunnel Joint Trace Maps”. Each simulation starts from the same probabilistic model of geometrical parameters of joints but develops differently with different outcomes. The spatial distribution rule of potential key blocks simulated by the aforementioned batch jointed network models is essentially identical to the actual rockfall during tunnel excavation.

Suggested Citation

  • Peng He & Li-ping Li & Gang Wang & Fei Xu & Shang-qu Sun, 2022. "Probabilistic prediction of the spatial distribution of potential key blocks during tunnel surrounding rock excavation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1721-1740, March.
  • Handle: RePEc:spr:nathaz:v:111:y:2022:i:2:d:10.1007_s11069-021-05113-w
    DOI: 10.1007/s11069-021-05113-w
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    References listed on IDEAS

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    1. Sun, Shangqu & He, Peng & Wang, Gang & Li, Weiteng & Wang, Hongbo & Chen, Diyang & Xu, Fei, 2021. "Shape characterization methods of irregular cavity using Fourier analysis in tunnel," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 191-214.
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    Cited by:

    1. Han Meng & Nengxiong Xu & Yunfu Zhu & Gang Mei, 2024. "Generating Stochastic Structural Planes Using Statistical Models and Generative Deep Learning Models: A Comparative Investigation," Mathematics, MDPI, vol. 12(16), pages 1-37, August.

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