Rockburst Intensity Level Prediction Method Based on FA-SSA-PNN Model

Gang Xu, Kegang Li, Mingliang Li, Qingci Qin and Rui Yue
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Gang Xu: School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Kegang Li: School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Mingliang Li: School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Qingci Qin: School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Rui Yue: School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Energies, 2022, vol. 15, issue 14, 1-19

Abstract: To accurately and reliably predict the occurrence of rockburst disasters, a rockburst intensity level prediction model based on FA-SSA-PNN is proposed. Crding to the internal and external factors of rockburst occurrence, six rockburst influencing factors ( σ θ , σ t , σ c , σ c / σ t , σ θ / σ c , W et ) were selected to build a rockburst intensity level prediction index system. Seventy-five sets of typical rockburst case data at home and abroad were collected, the original data were preprocessed based on factor analysis (FA), and the comprehensive rockburst prediction indexes, CPI 1 , CPI 2 , and CPI 3 , obtained after dimensionality reduction, were used as the input features of the SSA-PNN model. Sixty sets of rockburst case data were extracted as the training set, and the remaining 15 sets of rockburst case data were used as the test set. After the model training was completed, the model prediction results were analysed and evaluated. The research results show that the proposed rockburst intensity level prediction method based on the FA-SSA-PNN model has the advantages of high prediction accuracy and fast convergence, which can accurately and reliably predict the rockburst intensity level in a short period of time and can be used as a new method for rockburst intensity level prediction, providing better guidance for rockburst prediction problems in deep rock projects.

Keywords: rock mechanics; factor analysis; sparrow search algorithm; probabilistic neural network; rockburst intensity level prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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