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Gold Science and Technology ›› 2020, Vol. 28 ›› Issue (6): 920-929.doi: 10.11872/j.issn.1005-2518.2020.06.069

• Mining Technology and Mine Management • Previous Articles     Next Articles

Comparative Study on Three Rockburst Prediction Models of Intensity Classi-fication Based on Machine Learning

Rui TIAN1(),Haidong MENG1(),Shijiang CHEN1,Chuangye WANG1,Dening SUN2,Lei SHI3   

  1. 1.Institute of Mining Engineering，Inner Mongolia University of Science and Technology，Baotou 014010，Inner Mongolia，China
    2.Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines，Northeastern University，Shenyang 110819，Liaoning，China
    3.Inner Mongolia Institute of Geological Environmental Monitoring，Hohhot 010020，Inner Mongolia，China
  • Received:2020-04-08 Revised:2020-07-11 Online:2020-12-31 Published:2021-01-29
  • Contact: Haidong MENG E-mail:tianrui6251@126.com;haidongm@imust.cn

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Abstract:

Rockburst is one of the key scientific problems that must be solved in large-scale underground geotechnical engineering and deep mineral resource mining.The safety of personnel and equipment on site was directly threatened by rockburst.Rockburst could be effectively avoided and controlled in time by scientific and accurate rockburst prediction of intensity classification.Through the analysis of six rockburst engineering examples，on the basis of the factors，characteristics and causes of rockburst，a rockburst prediction index system composed of four evaluation indices，i.e.，tunnel-wall surrounding rock’s maximum tangential stress，rock uniaxial compressive strength，rock uniaxial tensile strength，and rock elastic energy index was established.With reference to other rockburst intensity classification schemes，considering the intensity of rockburst occurrence and the main influencing factors，the rockburst intensity was divided into four levels：None rockburst（Ⅰ），slight rockburst（Ⅱ），intermediate rockburst（Ⅲ） and strong rockburst（Ⅳ）.According to the selected rockburst evaluation index and rockburst intensity grade，a literature survey method was used to establish a database containing 301 groups of rockburst engineering examples，which would be used as the sample data for rockburst prediction.In order to accurately and reliably predicted rockburst disasters，machine learning technology was introduced.First，a random forest-based rockburst evaluation index importance analysis model was established，a new index weight calculation method of random forest-analytic hierarchy processs was proposed，and the rockburst prediction model based on the RF-AHP-cloud model was constructed.Then，the firefly algorithm based on good point set variable step strategy was introduced to optimize the penalty parameters and radial basis function parameters of the support vector machine，and the rockburst prediction model based on ⅠGSO-SVM was constructed.Finally，the Dropout method was used to regularize the model，and the improved Adam algorithm was used to update weight，and the rockburst prediction model based on DA-DNN was constructed.The effectiveness and correctness of the three models were validated by the prediction results of 60 groups of rockburst engineering examples.The research results show that：The DA-DNN，ⅠGSO-SVM，and RF-AHP-cloud model have prediction accuracy rates of 98.3%，90.0% and 85.0%.The core of rockburst intensity classification prediction based on cloud model is weight determination，and the RF-AHP weight calculation method proposed in this paper has a good effect.The data-driven ⅠGSO-SVM and DA-DNN models are based on rockburst engineering instance data.Through data mining，the rockburst intensity level can be effectively predicted，and higher prediction accuracy can be achieved by improvement.The theory of DA-DNN model is easy to understand，the coding is relatively simple and it is easy to implement.As various underground geotechnical engineering develops deeper，rockburst disasters occur frequently，the amount of rockburst data is increasing，and the DA-DNN model has a wider application prospect.

Key words: rockburst prediction, machine learning, random forest, cloud model, support vector machine, deep neural network

CLC Number: 

  • TU45

Table 1

Rockburst project instance database（partial data）"

序号工程名称σθσcσtWet岩爆等级
8瀑布沟水电站洞室[20]43.4123.06.05.0中级岩爆
18意大利Raibl矿井巷道[20]108.4140.08.05.5强烈岩爆
157括苍山隧道[21]13.9124.04.22.0无岩爆
203共和隧道[22]42.450.06.15.3轻微岩爆
???????
292美国Galena金矿[23]52.0175.07.05.2中级岩爆
301巴玉隧道[23]74.2190.08.97.1强烈岩爆

Fig.1

Levels of rockburst distribution"

Fig.2

Rockburst indexes importance score"

Table 2

Parameters of DA-DNN model"

序号参数名称参数取值
1Dropout丢弃比率p=0.5
2初始学习率η=0.001
3动量系数λ=0.95
4一、二阶矩估计得指数衰减率β1=0.9、β2=0.999
5用于数值稳定的常数δ=1e-08
6误差目标取值0.0001
7批大小取值Batch_size=10
8训练次数取值Epoch=60

Table 3

Results of rockburst intensity classification prediction sample"

样本序号工程名称DA-DNNIGSO-SVMRF-AHP-云模型FCM-RS-云模型实际岩爆等级
1天生桥二级水电站引水隧洞ⅢⅢⅢⅢⅢ
2二滩水电站2号支洞ⅡⅡⅢⅢⅡ
3龙羊峡水电站地下洞室ⅠⅠⅠⅠⅠ
4鲁布革水电站地下洞室ⅠⅠⅠⅠⅠ
5渔子溪水电站引水隧洞ⅢⅢⅢⅢⅢ
6太平驿水电站地下洞室ⅡⅡⅡⅡⅡ
7李家峡水电站地下洞室ⅠⅠⅠⅡⅠ
8瀑布沟水电站地下洞室ⅢⅡⅡⅢⅢ
9锦屏二级水电站引水隧洞ⅢⅢⅢⅢⅢ
10拉西瓦水电站地下厂房ⅢⅢⅢⅢⅢ
11挪威Sima水电站地下厂房ⅢⅢⅢⅢⅢ
12挪威Heggura公路隧道ⅢⅢⅢⅢⅢ
13挪威Sewage隧道ⅢⅢⅢⅢⅢ
14瑞典Forsmark核电站冷却水隧洞ⅢⅢⅢⅡⅢ
15瑞典Vietas水电站引水隧洞ⅡⅡⅡⅡⅡ
16前苏联Rasvumchorr矿井巷ⅢⅢⅢⅢⅢ
17日本关越隧道ⅢⅢⅢⅡⅢ
18意大利Raibl铅硫化锌矿井巷ⅣⅣⅣⅣⅣ
19秦岭隧道DyK77+176ⅢⅢⅢⅢⅢ
20秦岭隧道DyK72+440ⅣⅣⅣⅣⅣ
21秦岭隧道某段一ⅢⅢⅢⅢⅢ
22秦岭隧道某段二ⅢⅣⅣⅣⅢ
23括苍山隧道ⅡⅡⅡⅡⅡ
24通渝隧道K21+720断面ⅡⅡⅡⅡⅡ
25通渝隧道K21+212断面ⅡⅡⅡⅡⅡ
26通渝隧道K21+740断面ⅡⅡⅡⅡⅡ
27通渝隧道K21+680断面ⅡⅡⅡⅡⅡ
28江边水电站引5+486ⅠⅠⅠⅠⅠ
29江边水电站引7+366ⅠⅠⅠⅠⅠ
30江边水电站引7+790ⅢⅢⅣⅢⅣ
31江边水电站引7+806ⅡⅡⅠⅡⅡ
32锦屏二级电站1+731ⅡⅡⅡⅡⅡ
33锦屏二级电站3+390ⅢⅢⅢⅢⅢ
34锦屏二级电站1+640ⅡⅠⅡⅡⅡ
35锦屏二级电站3+000ⅢⅢⅢⅢⅢ
36程潮铁矿K8ⅣⅣⅣⅣⅣ
37程潮铁矿K9ⅠⅠⅠⅠⅠ
38程潮铁矿K10ⅣⅣⅣⅣⅣ
39程潮铁矿K11ⅠⅠⅠⅠⅠ
40程潮铁矿K12ⅢⅢⅢⅢⅢ
41程潮铁矿K13ⅣⅣⅣⅣⅣ
42苍岭隧道K97+702~K98+152ⅡⅡⅠⅠⅡ
43苍岭隧道K98+152~K98+637ⅡⅡⅡⅡⅡ
44苍岭隧道K98+637~K99+638ⅢⅢⅡⅢⅢ
45苍岭隧道K99+638~K100+892ⅡⅡⅡⅡⅡ
46苍岭隧道K100+892~K101+386ⅠⅠⅠⅠⅠ
47冬瓜山矿K1ⅢⅢⅢⅢⅢ
48北洺河铁矿K1ⅠⅠⅡⅡⅠ
49北洺河铁矿K2ⅣⅣⅣⅢⅣ
50北洺河铁矿K3ⅢⅢⅢⅢⅢ
51北洺河铁矿K4ⅠⅠⅠⅠⅠ
52美国CAD-A矿ⅣⅢⅢⅢⅣ
53美国CAD-B矿ⅢⅢⅢⅢⅢ
54美国CAD-C矿ⅡⅡⅡⅡⅡ
55苏联X矿山ⅢⅡⅢⅢⅢ
56瑞士布鲁格水电站地下硐室ⅠⅠⅠⅠⅠ
57乌兹别克斯坦卡姆奇克隧道ⅣⅣⅢⅣⅣ
58美国加利纳矿ⅡⅡⅡⅢⅡ
59重丘山岭某隧道ⅠⅠⅠⅠⅠ
60中国巴玉隧道ⅣⅣⅣⅣⅣ
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[5] CUI Ting-Jun, DAI Ke-Sai, PENG Yong, FU Xing. Discussion on the Geological Characteristics and Metallogenic Regularity of the  Southern Edge of Qaidam Basin Gold Metallogenic Belt in Qinghai Province[J]. J4, 2010, 18(3): 63 -67 .
[6] YANG Meng-Rong, MAO Chang-Xian. Uncertainty Evaluation of Arsenic and Antimony in Chemical Prospecting Sa-mple by Atomic Fluorescence Spectrometry[J]. J4, 2010, 18(3): 68 -71 .
[7] SU Jian-Hua, LIU Shu-Lin. Study of Gold Extraction from Tail Solution with High Acid Content and Low Concentration[J]. J4, 2010, 18(3): 72 -75 .
[8] WANG Da-Beng, SONG Bing-Jian, HUI Ku-Meng. The Application of High Power IP Method for Searching Concealed Metallic Mine in Beishuiquan，Liaoning Province[J]. J4, 2010, 18(3): 76 -78 .
[9] LIU Qing-Guang, GAO Hai-Feng, HUANG Suo-Yang. The Application of PDA in Geology Department of Jiaojia Gold Mine[J]. J4, 2010, 18(3): 79 -82 .
[10] LIU Xin-Hui, LIU Jia-Jun, CHEN Cai-Hua. [J]. J4, 2010, 18(4): 6 -11 .

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