Rockburst Risk Assessment Based on Soft Computing Algorithms

A key aspect that affect many deep underground mines over the world is rockburst phenomenon, which can have a strong impact in terms of costs and lives. Accordingly, it important their understanding order to support decision makers when such events occur. One way obtain deeper better mechanisms through laboratory experiments. Hence, database tests was compiled, then used develop predictive models for maximum stress risk indexes application soft computing techniques. The next step explore data gathered from situ cases rockburst. This study focusses on analysis information build influence diagrams, enumerate factors interact occurrence rockburst, understand relationships between them. In addition, were also analyzed using different algorithms, namely artificial neural networks (ANNs). aim predict type is, level, based geologic construction characteristics mine or tunnel. main observations taken considerable percentage accidents occur as result excessive loads, generally at depths greater than 1000 m. observed algorithms give an contribution determination construction-related parameters.