Numerical Analysis and Predictive Modeling Using Artificial Intelligence of the Relaxation Zone Around Hangingwall of Sublevel Open Stopes in Underground Mines

The aim of this study is to thoroughly analyze the relaxation zone developing around sublevel stopes in underground mines and identify the main parameters controlling its extent. A numerical approach based on the finite element method, combined with the Hoek-Brown failure criterion, was implemented to simulate various geometric configurations, geological conditions, and in-situ stress states. A total of 425 simulations were carried out by varying depth, horizontal-to-vertical stress ratio (k), rock mass quality (RMR), foliation orientation and spacing, as well as the height, width, and inclination of the sublevels. The results enabled the development of robust predictive models using regression analysis techniques and artificial neural networks (ANNs) to estimate the extent of the relaxation zone as a function of the different input parameters. It was demonstrated that depth and the k ratio significantly influence the extent of the relaxation zone. Additionally, a decrease in rock mass quality leads to a substantial increase in this zone. Structural characteristics, such as foliation orientation and spacing, also play a decisive role. Finally, the geometric parameters of the excavations, notably the height, width, and inclination of the sublevels, directly impact stress redistribution and the extent of the relaxation zone. The overall ANN model, taking into account all these key parameters, exhibited high accuracy with a correlation coefficient of 0.97. These predictive models offer valuable tools for optimizing the design of underground mining operations, improving operational safety, and increasing productivity.

Numerical Analysis and Predictive Modeling Using Artificial Intelligence of the Relaxation Zone Around Hangingwall of Sublevel Open Stopes in Underground Mines Keywords:

Sublevel , Relaxation , Hangingwall , modeling , ANN