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

TL;DR

This paper develops a machine learning approach based on persistent homology to analyze and predict tunnel failure characteristics under multiple explosive loads, aiding in tunnel safety and protection.

Contribution

It introduces a novel method combining discrete element modeling with persistent homology-based machine learning for topological analysis of tunnel failure processes.

Findings

Longest Betty 1 bar code length correlates with tunnel stability

Topological features can serve as early warning indicators

Method provides an intelligent description of failure characteristics

Abstract

The study of tunnel failure characteristics under the load of external explosion source is an important problem in tunnel design and protection, in particular, it is of great significance to construct an intelligent topological feature description of the tunnel failure process. The failure characteristics of tunnels under explosive loading are described by using discrete element method and persistent homology-based machine learning. Firstly, the discrete element model of shallow buried tunnel was established in the discrete element software, and the explosive load was equivalent to a series of uniformly distributed loads acting on the surface by Saint-Venant principle, and the dynamic response of the tunnel under multiple explosive loads was obtained through iterative calculation. The topological characteristics of surrounding rock is studied by persistent homology-based machine learning. The geometric, physical and interunit characteristics of the tunnel subjected to explosive loading are extracted, and the nonlinear mapping relationship between the topological quantity of persistent homology, and the failure characteristics of the surrounding rock is established, and the results of the intelligent description of the failure characteristics of the tunnel are obtained. The research shows that the length of the longest Betty 1 bar code is closely related to the stability of the tunnel, which can be used for effective early warning of the tunnel failure, and an intelligent description of the tunnel failure process can be established to provide a new idea for tunnel engineering protection.