A FEASIBILITY STUDY ON THE IMPLEMENTATION OF NEURAL NETWORK CLASSIFIERS FOR OPEN STOPE DESIGN
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Date
2021-07-06
Authors
Adoko, Amoussou Coffi
Saadaari, Festus
Mireku-Gyimah, Daniel
Imashev, Askar
Journal Title
Journal ISSN
Volume Title
Publisher
Geotechnical and Geological Engineering
Abstract
Assessing the stability of stopes is essen tial in open stope mine design as unstable hangingwalls
and footwalls lead to sloughing, unplanned stope
dilution, and safety concerns compromising the prof itability of the mine. Over the past few decades,
numerous empirical tools have been developed to
dimension open stope in connection with its stability,
using the stability graph method. However, one of the
principal limitations of the stability graph method is to
objectively determine the boundary of the stability
zones, and gain a clear probabilistic interpretation of
the graph. To overcome this issue, this paper aims to
explore the feasibility of artificial neural network
(ANN) based classifiers for the design of open stopes.
A stope stability database was compiled and included
the stope dimensions, rock mass properties, and the
stope stability conditions. The main parameters
included the modified stability number (N’), and the
stope stability conditions (stable, unstable, and failed),
and hydraulic radius (HR). A feed-forward neural
network (FFNN) classifier containing two hidden
layers (110 neurons each) was employed to identify
the stope stability conditions. Overall, the outcome of
the analysis showed good agreement with the field
data; most stope surfaces were correctly predicted
with an average accuracy of 91%. This shows an
improvement over using the existing stability graph
method. In addition, for a better interpretation of the
results, the associated probability of occurrence of
stable, unstable, or caved stope was determined and
shown in iso-probability contour charts which were
compared with the stability graph. The proposed
FFNN-based classifier outperformed the conventional
stability graph method in terms of accuracy and better
prabablistic interpretation. It is suggested that the
classifier could be a reliable tool that can complement
the conventional stability graph for the design of open
stopes.
Description
Keywords
Type of access: Open Access, Open stope stability, Hydraulic radius, ANN classifiers, Stability graph, Hangingwall, Footwall
Citation
Adoko, A. C., Saadaari, F., Mireku-Gyimah, D., & Imashev, A. (2021b). A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design. Geotechnical and Geological Engineering. Published. https://doi.org/10.1007/s10706-021-01915-8