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Gold Science and Technology ›› 2020, Vol. 28 ›› Issue (1): 134-141.doi: 10.11872/j.issn.1005-2518.2020.01.053

• Mining Technology and Mine Management • Previous Articles     Next Articles

A PSO-RBF Neural Network Model for Rockburst Tendency Prediction

Renhao LI1(),Helong GU1,Xibing LI1,Kuikui HOU2,Deming ZHU2,Xi WANG2   

  1. 1.School of Resource and Safety Engineering,Central South University,Changsha 410083,Hunan,China
    2.Deep Mining Laboratory Branch of Shandong Gold Group Co. ,Ltd. ,Laizhou 261442,Shandong,China
  • Received:2019-05-22 Revised:2019-11-04 Online:2020-02-29 Published:2020-02-26

Abstract:

Rockburst is one of the typical dynamic disasters in the field of underground engineering.The forecast of rockburst tendency in high stress area is of great practical significance.Due to the complexity of rockburst mechanism,the existing prediction models were difficult to reflect the multi-dimensional nonlinear characteristics of rockburst,which result in the low rockburst tendency prediction accuracy.In order to forecast rockburst tendency more accurately, a new rockburst tendency forecast model was proposed by combining particle swarm optimization (PSO) with radial basis function neural network (RBF).After determining the number of the hidden layer nodes by trial-by-error method,the parameters of RBF neural network including the center of basic function,width of the hidden layer node and the output weights formed a multi-dimensional vector,and were optimized as population particle of the PSO algorithm for the purpose of getting the optimal solution within the scope of global solvable space.Further,this paper referenced domestic and foreign related literature and choose four major rockburst tendency indicators,including the uniaxial compressive strength,the rock stress index,the rock brittleness index and the elastic energy index.25 typical practical rockburst engineering cases were took as the learning samples to train the PSO-RBF neural network model parameters.Finally,the established model of PSO-RBF was applied to rockburst tendency prediction of practical engineering.The results show it is approved that the prediction results of the proposed model in this paper are approximately consistent with the actual rockburst status.The relative error rate of PSO-RBF prediction model is 10%,and the accurate is significantly improved than prevenient prediction method.The PSO-RBF neural network rockburst tendency prediction model has a certain practicality and could provide effective guidance for similar projects.

Key words: rock mechanics, rockburst prediction, rockburst tendency, RBF neural network, particle swarm optimization algorithm, intelligent optimization

CLC Number: 

  • TU-45

Fig.1

RBF neural network topology"

Table 1

Classification standard of rockburst proneness parameters"

岩爆分级σcσθ/σcσc?/σtWeq
Ⅰ(无岩爆)<80.0<0.3>40.0<2.0
Ⅱ(弱岩爆)80.0~120.00.3~0.526.7~40.02.0~4.0
Ⅲ(中岩爆)120.0~180.00.5~0.714.5~26.74.0~6.0
Ⅳ(强岩爆)>180.0>0.7<14.5>6.0

Fig.2

PSO-RBF neural network models for prediction of rockburst"

Table 2

Rockburst infomation at home and abroad"

样本序号岩爆指标实际岩爆等级
σcσθ?/σcσc?/σtWeq
11700.5315.049.00
21200.8218.463.80
31400.7717.505.50
4200.086.671.39
51200.3724.005.10
6200.196.671.39
71200.6124.005.10
81800.4221.695.00
91400.7717.505.50
101150.1023.005.70
111760.3124.119.30
121150.5576.675.70
131650.3817.559.00
141320.4313.987.44
151280.5514.666.43
161900.4711.093.97
171700.539.923.97
18830.3712.773.20
192260.4013.147.30
20540.634.463.17
212370.4413.426.38
221570.5813.26.30
231480.4517.55.10
241320.3920.94.60
251070.2041.01.70

Table 3

Prediction results of rockburst tendency"

样本序号岩爆指标输出特征值PSO-RBF预测等级实际岩爆等级RBF预测等级Hoek岩爆判据
σcσθ?/σcσc?/σtWeq
11640.642.808.414.1671Ⅲ*Ⅲ~Ⅳ
21460.586.225.132.6765Ⅱ~Ⅲ
31320.3021.394.220.9672
41490.556.095.603.2167
51390.414.85.382.0910
61410.4310.764.871.9851Ⅲ*
71520.573.717.263.7762Ⅳ*
81350.3816.924.081.1662Ⅱ*
91610.692.977.093.0102Ⅳ*
101300.3129.863.961.1537
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