  Pillars are important structural units used to ensure mining safety in
underground hard rock mines. Therefore, precise predictions regarding the
stability of underground pillars are required. One common index that is often
used to assess pillar stability is the Safety Factor (SF). Unfortunately, such
crisp boundaries in pillar stability assessment using SF are unreliable. This
paper presents a novel application of Artificial Neural Network-Backpropagation
(ANN-BP) and Deep Ensemble Learning for pillar stability classification. There
are three types of ANN-BP used for the classification of pillar stability
distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and
ANN-BP GELU. This research also presents a new labeling alternative for pillar
stability by considering its suitability with the SF. Thus, pillar stability is
expanded into four categories: failed with a suitable safety factor, intact
with a suitable safety factor, failed without a suitable safety factor, and
intact without a suitable safety factor. There are five inputs used for each
model: pillar width, mining height, bord width, depth to floor, and ratio. The
results showed that the ANN-BP model with Ensemble Learning could improve
ANN-BP performance with an average accuracy of 86.48% and an F_2-score of
96.35% for the category of failed with a suitable safety factor.
