Numerical Method for Predicting and Evaluating the Stability of Section Coal Pillars in Underground Longwall Mining

Abstract

The stability of section coal pillars is one of the most important factors affecting the stability of coal rock systems in the stope and roadway. This study aimed to develop an artificial intelligence methodology to predict and evaluate coal pillar stability. Data from 125 published coal pillar historical cases were collected to build a sample dataset. Meanwhile, a mean impact value-genetic algorithm-back propagation neural network (MIV-GA-BP) fusion model was established to predict the stability of section coal pillars. MIV tests indicated that the main factors influencing coal pillar stability are (in order of decreasing importance): the coal seam buried depth > coal seam thickness > working face length > coal elastic modulus > cohesion > tensile strength > internal friction angle > Poisson’s ratio > volume weight > coal seam dip angle. The relative weights of mine design parameters are generally greater than those of the physical and mechanical parameters of coal and rock mass. After the BP model was optimized by GA, the relative error, R value, and mean squared error were 5%, 0.95, and 0.13, respectively. These results confirm that the machine learning model has significant potential for improving coal pillar stability evaluations. The developed prediction model was applied to two field cases to verify its effectiveness, and the results indicated that the innovative method can be extended for use in similar geological conditions or other mining and geaological engineering fields.