TY - JOUR
T1 - Stability assessment of underground mine stopes subjected to stress relaxation
AU - Adoko, Amoussou Coffi
AU - Vallejos, Javier
AU - Trueman, Robert
N1 - Funding Information:
This study was supported by the Faculty Development Competitive Research Grant program of Nazarbayev University, Grant N? 090118FD5338; and the Advanced Mining Technology Center (AMTC), University of Chile, through the Basal Project FB-0809. The authors wish to acknowledge the contribution of the anonymous reviewers.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Mathew’s stability graph
KW - mine stope stability
KW - neural network classifier
KW - Stress relaxation
UR - http://www.scopus.com/inward/record.url?scp=85078897652&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078897652&partnerID=8YFLogxK
U2 - 10.1080/25726668.2020.1721995
DO - 10.1080/25726668.2020.1721995
M3 - Article
AN - SCOPUS:85078897652
VL - 129
SP - 30
EP - 39
JO - Mining Technology: Transactions of the Institute of Mining and Metallurgy
JF - Mining Technology: Transactions of the Institute of Mining and Metallurgy
SN - 2572-6668
IS - 1
ER -