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The study of tunnel failure characteristics under the load of externalexplosion source is an important problem in tunnel design and protection, inparticular, it is of great significance to construct an intelligent topologicalfeature description of the tunnel failure process. The failure characteristicsof tunnels under explosive loading are described by using discrete elementmethod and persistent homology-based machine learning. Firstly, the discreteelement model of shallow buried tunnel was established in the discrete elementsoftware, and the explosive load was equivalent to a series of uniformlydistributed loads acting on the surface by Saint-Venant principle, and thedynamic response of the tunnel under multiple explosive loads was obtainedthrough iterative calculation. The topological characteristics of surroundingrock is studied by persistent homology-based machine learning. The geometric,physical and interunit characteristics of the tunnel subjected to explosiveloading are extracted, and the nonlinear mapping relationship between thetopological quantity of persistent homology, and the failure characteristics ofthe surrounding rock is established, and the results of the intelligentdescription of the failure characteristics of the tunnel are obtained. Theresearch shows that the length of the longest Betty 1 bar code is closelyrelated to the stability of the tunnel, which can be used for effective earlywarning of the tunnel failure, and an intelligent description of the tunnelfailure process can be established to provide a new idea for tunnel engineeringprotection.

Analysis of tunnel failure characteristics under multiple explosion loads based on persistent homology-based machine learning