LIU Xiaofei, YOU Shihui. Analysis on Persistent Homology Characteristics of Failure Processes of Shallow Buried Tunnels Under Multi-Time Explosions[J]. Applied Mathematics and Mechanics, 2020, 41(11): 1237-1249. doi: 10.21656/1000-0887.400222
Citation: LIU Xiaofei, YOU Shihui. Analysis on Persistent Homology Characteristics of Failure Processes of Shallow Buried Tunnels Under Multi-Time Explosions[J]. Applied Mathematics and Mechanics, 2020, 41(11): 1237-1249. doi: 10.21656/1000-0887.400222

Analysis on Persistent Homology Characteristics of Failure Processes of Shallow Buried Tunnels Under Multi-Time Explosions

doi: 10.21656/1000-0887.400222
Funds:  The National Natural Science Foundation of China(51375416)
  • Received Date: 2019-07-22
  • Rev Recd Date: 2020-02-26
  • Publish Date: 2020-11-01
  • The failure characteristics of shallow buried tunnels under multi-time explosions make an important research issue in the design and evaluation of protection engineering. It is of great significance to develop a method for early warning of the safety for shallow buried features. The discrete element method was used to establish the mechanical model for the shallow buried tunnel. With the Saint-Venant principle the blast loads were treated as a series of dynamic forces acting uniformly on the surface. Based on the discrete element method, the dynamic response under each blast load and the damage evolution process of the tunnel surrounding rock were obtained. The strength reduction method was used to calculate the safety factor of the surrounding rock. The persistent homology theory was introduced to quantitatively and qualitatively analyze the failure characteristics of the discrete element model under multi-time explosive loads. The results show that, the method of persistent homology can accurately reflect the topological characteristics of the surrounding rock. The maximum 1D barcode connection radius can effectively predict tunnel instability. The work provides a new mathematical method for tunnel safety design and disaster prediction research.
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