Monitoring And Prediction Of Longwall Instability Using Artificial Intelligence Techniques (71cec1e3-30ef-49d7-b2d0-d17a952611b4)

Artificial intelligence has emerged over the past two decades as an important problem-solving tool including techniques such as expert systems, neural networks, tree classification, fuzzy logic, genetic algorithms, etc. These methods can be employed to solve rock mechanics and ground control problems associated with the highly complex nature of rock masses and ground conditions. A current study at the University of Alabama focuses on development of an artificial intelligence system for real-time interpretation of the data collected from longwall monitoring stations, and development of an early warning system for prediction of undesired conditions and prevention of longwall-mine instability problems. This paper describes a ground control management system using UALSAS (The University of Alabama Longwall Stability Analysis System), which is being developed to facilitate maintenance, on-line analysis, prediction, and includes an early warning system. A detailed framework of computer algorithms and their applications are also discussed. The successful application of these artificial intelligence techniques will contribute to a safer and healthier mining environment.