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

Peng He, Li-ping Li, Gang Wang, Fei Xu and Shang-qu Sun ()
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 24, 1740 pages

Abstract: 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.

Keywords: Tunnel engineering; Potential key block; Probabilistic prediction; Space distribution; Batch network models; Breath-first search (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05113-w

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