Evaluation model for the risk grade of rock burst based on the R-type factor analysis and a probabilistic neural network

被引：0

作者：

Wang J. ^{[1
]}

Zhou Z. ^{[1
]}

Li K. ^{[1
]}

Wang H. ^{[2
]}

Fu Z. ^{[1
]}

Li X. ^{[1
]}

机构：

[1] Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming

[2] School of Resources and Safety Engineering, Central South University, Changsha

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2019年 / 38卷 / 02期

关键词：

Probabilistic neural network(PNN); R-type factor analysis; Risk grade evaluation; Rock burst;

D O I：

10.13465/j.cnki.jvs.2019.02.029

中图分类号：

学科分类号：

摘要：

Rock burst is one of the common dynamic disasters in coal mine production. Hence, the evaluation of the risk grade is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst. Factors of geological and mining technique conditions were considered comprehensively, and 10 factors, including the coal seam thickness, dip angle, depth, geological tectonic situation, change of coal seam dip angle, change of coal seam thickness, gas concentration, roof control, state of pressure relief before excavation and shooting, were selected as evaluation indexes of the risk grade for rock burst. However, there exist somewhat correlations, large or small, between these factors. In order to improve the prediction precision of rock burst, the method of R-type factor analysis was utilized to synthesize 5 main factors from these 10 factors as new evaluation indexes to eliminate the information overlapping of these factors, and reduce the input dimension. Based on this, a PNN model for the evaluation of risk grade of rock burst in Yanshitai coal mine in Chongqing was established. The simulation results show that the PNN model presents a favorable performance for 5 different kinds of training and test samples, and the distinguishing-positive rates are 94.29%, 88.57%, 88.57%, 85.71% and 82.86% respectively. Meanwhile, the evaluation performance of the R-type factor analysis model was validated. It indicates that the combination of R-type factor analysis and PNN model can provide a way for the risk grade evaluation of rock burst in coal mines. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：192 / 203

页数：11

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