This invention provides an intelligent evaluation method for rockburst
hazard at working faces based on
principal component analysis-probabilistic neural networks, belonging to the field of rockburst
hazard evaluation technology. An evaluation
index system is established and data is collected by combining rockburst
hazard influencing factors,
drill cuttings monitoring,
critical stress index monitoring, and actual
field conditions.
Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a
training set and a
test set. A
probabilistic neural network (PNN) is used to
train the evaluation model on the data in the
training set, and then the performance of the evaluation model is tested using data from the
test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's
smoothing factor needs to be modified and retrained until the accuracy of the
evaluation result meets the set value.