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Gold Science and Technology ›› 2020, Vol. 28 ›› Issue (4): 565-574.doi: 10.11872/j.issn.1005-2518.2020.04.040

• Mining Technology and Mine Management • Previous Articles     Next Articles

Research and Application of T-FME Rockburst Propensity Prediction Model Based on Combination Weighting

Tongtong LI1(),Xi WANG2,Huanxin LIU2,Kuikui HOU2,Xibing LI1()   

  1. 1.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
    2.Deep Mining Laboratory of Shandong Gold Group Co. , Ltd. , Laizhou 261400, Shandong, China
  • Received:2020-01-19 Revised:2020-05-26 Online:2020-08-31 Published:2020-08-27
  • Contact: Xibing LI E-mail:Littong@csu.edu.cn;xbli@mail.csu.edu.cn

Abstract:

Due to the limitations of its own operating conditions,many multi-index comprehensive evaluation methods of rockburst are liable to cause low accuracy,and there is currently no unified prediction standard.In order to improve the accuracy of rockburst tendency prediction model,we must ensure that the index weighting method and the selection of the correlation function are more comprehensive and reasonable then a prediction model of T-FME rockburst tendency was established.According to the mechanism and condition of rock ex-plosion,brittle coefficient,tangential stress index and elastic strain energy index were selected as the evaluation indexes from three aspects:Surrounding rock stress,lithological conditions and surrounding rock energy storage.On the one hand,the excessive subjectivity of subjective judgments will affect the objectivity of index weights,on the other hand,in the case of limitated information,entropy weight method excessively depends on the degree of index variation will lead to bias.In order to make up for these deficiencies,the principle of minimum discriminant information was introduced,and the indicators were combined and weighted by combining the subjective and objective weights that the subjective weight of the indicator is determined by the ordinal relationship analysis method and the objective weight of the indicator was determined by the conventional entropy weight method which has been modified by the vague entropy.The T-FME rockburst propensity pre-diction model is based on the fuzzy matter-element analysis method and combines the principles of the TOPSIS method to construct the ideal fuzzy matter-element.The concept of ideal difference-square compound fuzzy matter-element was proposed.Post progress calculation has been optimized,the closeness compound fuzzy matter element was calculated.Finally,the degree of rockburst tendency can be obtained through the closeness analysis.Using the data of 15 domestic and foreign engineering rockburst examples to test the T-FME model and other 4 rockburst propensity prediction models that use different weighting methods and correlation degree functions,and these rockburst propensity prediction models are the ideal fuzzy matter element method based on Vague entropy weight,the ideal fuzzy matter-element method based on expert experience method,the euclid approach degree fuzzy matter-element method based on combined weighting,and the gray favorably membership degree fuzzy matter-element method.By analyzing the results of this test of these models,it is known that the prediction accuracy of the T-FME rockburst tendency prediction model is as high as 93.3%.Compared with other models,the accuracy of the prediction is improved by 6.6%~10.0%,and the prediction of the rockburst propensity level which is biased is higher than actual,so the prediction result is safer.Finally,the model was applied to 5 domestic practical projects,and the prediction results are consistent with the actual rockburst propensity level,which proves that the model has strong feasibility and applicability.

Key words: rockburst prediction, Vague entropy, principle of minimum discriminating information, com-bination weight, ideal fuzzy-matter element, closeness compound fuzzy-matter element

CLC Number: 

  • TD45

Table 1

Reference table for rj?assignmet"

rj说明
1.0指标Cj-1与指标Cj具有同样重要性
1.2指标Cj-1比指标Cj稍微重要
1.4指标Cj-1比指标Cj明显重要
1.6指标Cj-1比指标Cj强烈重要
1.8指标Cj-1比指标Cj极端重要

Table 2

Classification criteria for rockburst tendency"

分级标准σθ/σcσc/σtWet
<0.3>40.0<2.0
0.3~0.540.0~26.72.0~3.5
0.5~0.726.7~14.53.5~5.0
>0.7<14.5>5.0

Table 3

Domestic and foreign rockburst engineering data"

工程编号σθ/σcσc/σtWet岩爆烈度分级实际情况描述数据资料来源
10.3246.6爆发频繁发生,属中等强度[19-20]
20.4129.77.3开挖初期即出现轻微围岩壁面爆裂,属弱岩爆[19-20]
30.10631.27.4无岩爆[19-20]
40.5314.89Ⅲ~Ⅳ片状剥落,岩片弹射崩落,顶板有爆烈声,属中—强岩爆[19-20]
50.3817.69Ⅱ~Ⅳ岩爆破坏规模不等,多数为中弱规模岩爆,少数为强岩爆[19-20]
60.096235.7无岩爆[19-20]
70.3624.65发生中级岩爆[19-20]
80.8218.53.8Ⅱ~Ⅲ发生中弱等级岩爆[19,21]
90.31524.19.3试验洞观察到发生中级岩爆[19,22]
100.2721.75观察到发生中级岩爆[19,23]
110.3724.15洞室岩石剥落与弹射,中级岩爆[22,24]
120.4221.75壁裂、有尖锐的爆裂声响,中级岩爆[22,24]
130.3821.75岩片弹射,伴有响声,中级岩爆[22,24]
140.31721.75弹射与剥落,中级岩爆[22,24]
150.37722.15侧壁发生中级岩爆[22,24]

Table 4

Vague entropy and objective weights of rockburst indicators"

指标σθ/σcσc/σtWet
熵值0.47150.52030.3949
Vague熵权0.3280.2970.375

Table 5

Weights of rockburst indicators"

指标σθ/σcσc/σtWet
主观权重0.4330.3090.258
客观权重0.3280.2970.375
组合权重0.3800.3060.314

Table 6

Comparative analysis of rockburst prediction results"

工程编号模型预测等级实际等级
T(V)-FMET(G)-FME本文方法E(C)-FMEG(C)-FME
岩爆模型预测结果分析准确率较低准确率较高但出现不安全预测结果准确率较高且偏向安全结果准确率最低且出现不安全预测结果准确率较低
1
2(Ⅲ)(Ⅲ)(Ⅲ)(Ⅲ)(Ⅲ)
3(Ⅱ)(Ⅱ)(Ⅱ)
4Ⅲ~Ⅳ
5Ⅱ~Ⅳ
6(Ⅱ)(Ⅱ)(Ⅱ)
7
8Ⅱ~Ⅲ
9
10(Ⅱ)(Ⅱ)
11
12
13
14
15

Fig.1

Comparison of prediction results of 5 rockburst propensity prediction models"

Table 7

Prediction of rockburst tendency in several domestic projects"

工程名称工程编号岩性或位置σc/σtσθ/σcWet实际情况预测等级
平煤集团a三水平大巷岩爆位置15.30.5603.30
小秦岭金矿b888坑38号SM6200段12.20.5424.89
小秦岭金矿c888坑38号SM4740段30.70.4097.30
小秦岭金矿d888坑38号SM4320段29.80.4615.30
冬瓜山e矽卡岩11.10.5543.97
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