TY - GEN
T1 - A comparison of prediction and classification models of unplanned stope dilution in open stope design
AU - Bazarbay, B.
AU - Adoko, A. C.
N1 - Funding Information:
This study was supported by the Faculty Development Competitive Research Grant program of Nazarbayev University, Grants Nº 090118FD5338 and Nº 021220FD5051.
Publisher Copyright:
© 2021 ARMA, American Rock Mechanics Association.
PY - 2021
Y1 - 2021
N2 - Unplanned stope dilution affects the cost of open stoping mining operations. The stability graph and equivalent linear overbreak slough graph have been in use for designing the stope dimensions that minimize unplanned dilution. However, despite extensive effort made by researchers, the graph methods still suffer from limitations. One of them is the poor generalization ability of these graphs. Hence, in this paper, Artificial Neural Network (ANN) based graphs are proposed as alternative tools capable of relating accurately the dilution to the stope dimensions and the rock mass quality. Two models were developed: a predictive (fitting) model and a classification model. The results indicate that ANN, as a prediction model, is not appropriate for dilution because of a low prediction capability observed (R2 = 0.57). On the other hand, the classifier model showed good classification accuracy (84% on the average). In addition, the output of the classifier was used to determine the probability of unplanned dilution occurrence in the form of maps, which are useful in stope design. Therefore, the use of ANN-based classifier for open stope design is suggested while predictive models (i.e. fitting models) should be discouraged.
AB - Unplanned stope dilution affects the cost of open stoping mining operations. The stability graph and equivalent linear overbreak slough graph have been in use for designing the stope dimensions that minimize unplanned dilution. However, despite extensive effort made by researchers, the graph methods still suffer from limitations. One of them is the poor generalization ability of these graphs. Hence, in this paper, Artificial Neural Network (ANN) based graphs are proposed as alternative tools capable of relating accurately the dilution to the stope dimensions and the rock mass quality. Two models were developed: a predictive (fitting) model and a classification model. The results indicate that ANN, as a prediction model, is not appropriate for dilution because of a low prediction capability observed (R2 = 0.57). On the other hand, the classifier model showed good classification accuracy (84% on the average). In addition, the output of the classifier was used to determine the probability of unplanned dilution occurrence in the form of maps, which are useful in stope design. Therefore, the use of ANN-based classifier for open stope design is suggested while predictive models (i.e. fitting models) should be discouraged.
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M3 - Conference contribution
AN - SCOPUS:85122010933
T3 - 55th U.S. Rock Mechanics / Geomechanics Symposium 2021
BT - 55th U.S. Rock Mechanics / Geomechanics Symposium 2021
PB - American Rock Mechanics Association (ARMA)
T2 - 55th U.S. Rock Mechanics / Geomechanics Symposium 2021
Y2 - 18 June 2021 through 25 June 2021
ER -