Prediction of Rockburst Propensity Based on Intuitionistic Fuzzy Set—Multisource Combined Weights—Improved Attribute Measurement Model

A rockburst is a geological disaster that occurs in resource development or engineering construction. In order to reduce the harm caused by rockburst, this paper proposes prediction study of propensity based on intuitionistic fuzzy set-multisource combined weights-improved attribute measurement model. From perspective rock mechanics, uniaxial compressive strength σc, tensile stress σt, shear σθ, compression/tension ratio σc/σt, shear/compression σθ/σc, and elastic deformation coefficient Wet were selected as indicators for predicting corresponding classification set was established. Constructing model framework an set–improved includes transforming vagueness with controlling uncertainty results measurements, well improving accuracy using Euclidean distance method improve identification method. To further transform indicators, multisource system weights constructed minimum entropy weighting method, game theory multiplicative synthetic normalization integrated sets, single-valued data changed into intervalized basis subjective analytic hierarchy process objective weights, variation Choosing 30 groups typical cases, indicator calculated analyzed through paper’s Firstly, comparing other three single-combination models measurement, 86.7%, which higher than least addition number uncertain indicating has been effectively dealt with; secondly, rationality multiple sources verified, controlled.