CLASSIFICATION OF COAL MINE PILLAR STABILITY USING EXTREME LEARNING MACHINE AND PARTICLE SWARM OPTIMIZATION ADAPTIVE WEIGHT DELAY VELOCITY

In underground mining, pillars are prime structural parts for supporting the overburden. Precise prediction of pillar stability is necessary because pillar failure might cause catastrophic events that could endanger mining personnel and equipment. This research aims to classify the stability of underground coal mining pillars using the Extreme Learning Machine model with Particle Swarm Optimization Adaptive Weight Delay Velocity that used to optimize the model's input weights and bias. Extreme Learning Machine is a model for training artificial neural networks using a single-layer feedforward Network architecture. Performance comparison is presented between the proposed model and the Particle Swarm Optimization-Extreme Learning Machine. The dataset originated from South African coal mining with two pillar stabilities: failed and intact. The pillar stability of the dataset expanded into five categories: failed upper, failed lower, intact upper, intact lower slender, intact lower not-slender. Out of the five pillar stability categories, the failed lower category is the most dangerous pillar category, with the rest are non-dangerous pillar category. The expanded categories are according to the Probability of Failure of the pillar and the type of pillar (slender, intermediate, and squat). The results showed that the AUC 91,4%; 74,3%; 72,6%, and G-mean 82,2% were all at least 10% higher in the proposed model. The proposed model successfully classified 91.24% of non-dangerous pillar stability category, but only 36% of the most dangerous pillar stability category. The results of this study are expected could give assistance to provide information as a consideration in predicting pillar.