Stability assessment of underground mine stopes subjected to stress relaxation

Abstract

Stress relaxation plays an important role in the design of underground stopes. The aim of this paper is to assess the stope stability in connection with the stress relaxation using a classification approach. Three types of stress relaxation were clearly defined, namely partial relaxation, tangential relaxation and full relaxation. A neural network classifier was implemented to assess the stability of the stopes on the basis of case histories of stope performances. The results of the classification were compared to existing empirical methods of quantifying the stress relaxation. Overall, the present study shows higher classification accuracies, especially when the stress relaxation was considered. The results suggested that the relaxation type can be a good predictor of stability. Relaxed stope (full and tangential stress relaxation) cases are the most critical in the sense that lower accuracies were obtained and the probability of correct classification is rather erratic.

A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

“…In that cross validation was not performed on the validation data since the data were already randomly split into training, testing, and validation datasets. This procedure is commonly used when cross validation is not implemented (Adoko et al 2020).…”