A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

Abstract

The aim of this study is to investigate the applicability of artificial neural networks (ANN) and game theory in the development of an underground mining method selection model. To realize this, six different ANN models that can evaluate geometric and rock mass properties of an underground mine, environmental factors and ventilation conditions to determine mining methods that satisfy the safety conditions for an underground mine were developed. Among the mining methods determined by ANNs, the optimal mining method was determined by the ultimatum games, in which a compromise between safety and economic conditions was simulated. By using a combination of developed ANN models and ultimatum games, a new model based on artificial neural networks and game theory for the selection of underground mining method was developed. This model can make predictions in the presence of lack of information by following technological developments and new findings obtained in scientific/sectoral studies if learning is continuous. Moreover, the model can evaluate all selection criteria and provide literature-based solutions. In the light of findings obtained within this study, it is revealed that artificial neural networks and game theory can be used in the selection of underground mining methods.