Study of surrounding rock classification of underground cavern based on mathematical statistics and drifting degree theory
Nine indicators were selected as evaluation factors for classifying surrounding rock by contrast analysis. Sample set was generated by monte carlo simulation with the assumption of evaluation factor’s independent distribution. Sample evaluation matrix was constructed by the linear type of uncertainty measurement theory. Sample weight was established dynamically by drifting degree theory, avoiding the subjectivity of weights determination, to make it more objective and accurate. Sample classification was evaluated by credible degree recognition criterion. Solving process was completely driven by sample. Based on mathematical statistics, the classification corresponding with the maximum probability was selected as evaluation object’s, then a surrounding rock classification model of underground cavern was established. Evaluation factor’s influence degree was defined to provide data support for taking measures to improve surrounding rock.