Ensemble Learning Model on Artificial Neural Network-Backpropagation (ANN-BP) Architecture for Coal Pillar Stability Classification

TL;DR

This paper introduces a novel ensemble learning approach using different ANN-BP architectures to classify underground coal pillar stability more accurately than traditional methods, considering new labeling strategies and multiple input features.

Contribution

It presents a new ensemble learning framework with diverse ANN-BP models and a novel pillar stability labeling scheme, improving classification accuracy in mining safety assessment.

Findings

Ensemble learning improved classification accuracy to 86.48%.

ANN-BP with ensemble achieved an F2-score of 96.35%.

New labeling scheme better captures pillar stability categories.

Abstract

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.