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.

中文翻译：

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基于人工神经网络和博弈论的地下采矿方法选择新模型

本研究的目的是研究人工神经网络 (ANN) 和博弈论在开发地下采矿方法选择模型中的适用性。为了实现这一点，开发了六种不同的人工神经网络模型，可以评估地下矿山的几何和岩体特性、环境因素和通风条件，以确定满足地下矿山安全条件的采矿方法。在人工神经网络确定的采矿方法中，最优采矿方法由最后通牒博弈确定，其中模拟安全和经济条件之间的折衷。通过结合使用已开发的人工神经网络模型和最后通牒博弈，开发了一种基于人工神经网络和博弈论的地下采矿方法选择新模型。如果学习是持续的，该模型可以通过跟踪技术发展和科学/部门研究中获得的新发现，在缺乏信息的情况下做出预测。此外，该模型可以评估所有选择标准并提供基于文献的解决方案。根据本研究中获得的结果，表明人工神经网络和博弈论可用于选择地下采矿方法。