Adoko, Amoussou CoffiSaadaari, FestusMireku-Gyimah, DanielImashev, Askar2022-05-182022-05-182022Adoko, A. C., Saadaari, F., Mireku-Gyimah, D., & Imashev, A. (2021). A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design. Geotechnical and Geological Engineering, 40(2), 677–696. https://doi.org/10.1007/s10706-021-01915-8http://nur.nu.edu.kz/handle/123456789/6160Assessing 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)enAttribution-NonCommercial-ShareAlike 3.0 United StatesType of access: Open AccessOpen stope stabilityHydraulic radiusANN classifiersStability graphHangingwallFootwallArticle