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黄金科学技术 ›› 2020, Vol. 28 ›› Issue (4): 575-584.doi: 10.11872/j.issn.1005-2518.2020.04.019

• 采选技术与矿山管理 • 上一篇    下一篇

基于核主成分分析与SVM的岩爆烈度组合预测模型

许瑞1(),侯奎奎2,王玺2,刘兴全2,李夕兵1()   

  1. 1.中南大学资源与安全工程学院,湖南 长沙 410083
    2.山东黄金集团深井开采实验室,山东 莱州 261442
  • 收稿日期:2019-12-25 修回日期:2020-04-22 出版日期:2020-08-31 发布日期:2020-08-27
  • 通讯作者: 李夕兵 E-mail:safetyxurui@csu.edu.cn;xbli@mail.csu.edu.cn
  • 作者简介:许瑞(1995-),女,山西晋中人,硕士研究生,从事深部地下工程灾害防治研究工作。safetyxurui@csu.edu.cn
  • 基金资助:
    国家自然科学基金项目“开采扰动下深部工程结构的动态响应机理”(11972378);“考虑非平稳、强噪声信号到时差拾取的硬岩矿山微震定位方法研究”(51904335)

Combined Prediction Model of Rockburst Intensity Based on Kernel Principal Component Analysis and SVM

Rui XU1(),Kuikui HOU2,Xi WANG2,Xingquan LIU2,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 261442,Shandong,China
  • Received:2019-12-25 Revised:2020-04-22 Online:2020-08-31 Published:2020-08-27
  • Contact: Xibing LI E-mail:safetyxurui@csu.edu.cn;xbli@mail.csu.edu.cn

摘要:

为了更好地预测岩土工程中的岩爆烈度,建立了基于多类型核函数的主成分分析方法与遗传算法或粒子群优化算法(GA/PSO)优化的支持向量机(SVM)相结合的组合预测模型。选取围岩最大切向应力σθ、岩石单轴抗拉强度σt、岩石单轴抗压强度σc、应力集中系数SCF、脆性指数B1B2以及弹性应变能指数Wet共7个指标构成岩爆预测指标体系。基于统计的246个国内外岩爆实例数据,分别运用主成分分析和基于线性核函数、RBF核函数以及MLP核函数的主成分分析对数据进行预处理,得到2~4个线性无关的主成分。再将降维后的数据输入GA/PSO优化的SVM模型进行训练和预测。经测试,基于RBF核函数的主成分分析方法与PSO-SVM相结合的模型预测准确率达到了92.3%,为最佳组合模型,为岩土工程中的岩爆烈度预测提供了一种可靠的方法。

关键词: 核主成分分析, 岩爆烈度预测, 遗传算法, 粒子群优化算法, 支持向量机, 组合预测模型

Abstract:

Rockburst is a relatively dangerous engineering geological disaster in underground hard rock engineering constructed in high geostress area.Due to the re-distribution of the stress in surrounding rocks during the excavation of underground engineering,the elastic strain energy is released suddenly and abruptly,causing rock fragments to eject from the rock.And then,the casualties and equipment damage are often happened,which make the rockburst become one of the worldwide difficulties in underground engineering.Therefore,the prediction of possibility of rockburst and its intensity is a problem that must be solved in underground engineering construction.For predicting rock-burst intensity effectively,a combined prediction model based on kernel principal component analysis (KPCA) of multiple types and the support vector machine (SVM) optimized by genetic algorithm or particle swarm optimization algorithm (GA/PSO) was established.According to the characteristics and causes of rockburst,rocks’ maximum tangential stress σθ,rocks’ uniaxial compressive strength σt,rocks’ uniaxial tensile strength σc,stress concentration coefficient SCF,rock brittleness coefficient B1 and B2,and elastic energy index Wet were chosen to form the rockburst prediction indexes system.Based on 246 groups of typical rockburst cases at home and abroad,the data were preprocessed through the principal component analysis and the principal component analysis based on linear kernel function,radial basis function (RBF) kernel function and multi-layer perceptron (MLP) kernel function.On the basis of ensuring the amount of information in the original data,2 to 4 linearly independent principal components are obtained,which reduces the correlation between the indicators and the input parameters of the SVM model,and simplifies the training process.Then input the dimensionality-reduced data into GA/PSO optimized SVM model for training and prediction.To improve classification accuracy and generalization ability of the SVM,GA/PSO were adopted to automatically determine the parameters for support vector machine,and the optimal values of parameters C and g were determined by the method of 10 fold cross validation,which avoided the blindness of manually providing parameters.In this study,220 rockburst samples were randomly selected as the training set,and the remaining 26 samples were selected as the test set.After testing,the optimal parameters,the training set and test set accuracy of the 8 combined models were obtained.The prediction accuracy of the model based on the combination of the principal component analysis method of RBF kernel function and PSO-SVM reached 92.3%,which was the optimal combination model.It demonstrated that the combined prediction model can accurately deal with the complex non-linear relationship between various factors affecting the rockburst intensity,and the model has strong engineering practicability in the prediction of rockburst intensity.

Key words: kernel principal component analysis, prediction of rockburst intensity, genetic algorithm, particle swarm optimization algorithm, support vector machine, combination prediction model

中图分类号: 

  • TU45

图1

岩爆烈度组合预测模型运行流程图"

表1

部分岩爆样本原始数据"

序号σθ/MPaσc/MPaσt/MPaSCFB1B2Wet岩爆烈度
190.00170.0011.300.5315.040.889.00M
290.00220.007.400.4129.730.937.30L
362.60165.009.400.3817.530.899.00L
455.40176.007.300.3224.110.929.30M
530.0088.703.700.3423.970.926.60M
648.75180.008.300.2721.690.915.00M
?????????
243126.72189.708.950.6721.200.915.43L
24457.97125.377.740.6721.200.462.86L
24557.9796.163.770.4616.200.202.53L
24657.9770.684.190.6025.510.192.87L

图2

岩爆原始数据箱线图"

表2

岩爆评价指标的相关系数"

指标σθ/MPaσc/MPaσt/MPaSCFB1B2Wet
σθ/MPa1.0000.0340.3340.918-0.248-0.2190.468
σc/MPa0.0341.0000.422-0.2650.0760.2340.193
σt/MPa0.3340.4221.0000.164-0.625-0.4710.329
SCF0.918-0.2650.1641.000-0.258-0.2520.329
B1-0.2480.076-0.625-0.2581.0000.526-0.108
B2-0.2190.234-0.471-0.2520.5261.000-0.082
Wet0.4680.1930.3290.329-0.108-0.0821.000

表3

原始数据标准化处理结果"

序号σθ/MPaσc/MPaσt/MPaSCFB1B2Wet
10.59171.37040.9878-0.0869-0.38640.14460.9237
20.59172.54250.0553-0.26550.64510.65700.5147
30.08501.25320.5335-0.3101-0.21160.24700.9237
4-0.04821.51110.0314-0.39940.25050.55450.9959
5-0.5179-0.5353-0.8294-0.36970.24070.55450.3463
6-0.17111.60490.2705-0.47380.08050.4520-0.0387
????????
2431.27081.83220.42590.12150.04610.45200.0648
244-0.00060.32430.13660.12150.0461-4.1597-0.5536
245-0.0006-0.3604-0.8126-0.1911-0.3050-6.8242-0.6330
246-0.0006-0.9577-0.71220.01730.3488-6.9267-0.5512

图3

RBF核主成分分析第一主成分贡献率与核参数关系"

图4

累积贡献率对比"

表4

核参数及主成分的选择"

核函数类型核函数表达式核参数第一主成分贡献率/%主成分个数
主成分分析--40.204
线性核函数Kxi,xj=xi?xj-59.192
高斯核函数Kxi,xj=exp?-xi-xj22σ2σ=89058.523
多层感知器核函数Kxi,xj=tanh?vxi?xj+cv=1×10-5,c=-561.593

图5

降维结果可视化"

表5

模型参数及模型训练集和测试集准确率"

模型SVM参数训练集准确率/%测试集准确率/%
Cg
PCAGA-SVM81.471056.621296.884.6
PSO-SVM97.6345118.443198.284.6
KPCA1GA-SVM50.5654890.241594.673.1
PSO-SVM51.3095586.158889.676.9
KPCA2GA-SVM3.1284256.190591.488.5
PSO-SVM31.2537145.417894.692.3
KPCA3GA-SVM73.1933282.282193.280.8
PSO-SVM82.0614491.932297.788.5

图6

部分模型的适应度曲线"

图7

部分模型测试样本预测结果"

1 李夕兵.岩石动力学基础与应用[M].北京:科学出版社,2014.
Li Xibing.Rock Dynamics Fundamentals and Applications[M].Beijing:Science Press,2014.
2 王元汉,李卧东,李启光,等.岩爆预测的模糊数学综合评判方法[J].岩石力学与工程学报,1998,17(5):493-501.
Wang Yuanhan,Li Wodong,Li Qiguang,et al.Method of fuzzy comprehensive evaluations for rockburst prediction[J].Chinese Journal of Rock Mechanics and Engineering,1998,17(5):493-501.
3 李夕兵,宫凤强,王少锋,等.深部硬岩矿山岩爆的动静组合加载力学机制与动力判据[J].岩石力学与工程学报,2019,38(4):708-723.
Li Xibing,Gong Fengqiang,Wang Shaofeng,et al.Coupled static-dynamic loading mechanical mechanism and dynamic criterion of rockburst in deep hard rock mines[J].Chinese Journal of Rock Mechanics and Engineering,2019,38(4):708-723.
4 宫凤强,李夕兵.岩爆发生和烈度分级预测的距离判别方法及应用[J].岩石力学与工程学报,2007,26(5):1012-1018.
Gong Fengqiang,Li Xibing.A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(5):1012-1018.
5 赵洪波.岩爆分类的支持向量机方法[J].岩土力学,2005,26(4):642-644.
Zhao Hongbo.Classification of rockburst using support vector machine[J].Rock and Soil Mechanics,2005,26(4):642-644.
6 白明洲,王连俊,许兆义.岩爆危险性预测的神经网络模型及应用研究[J].中国安全科学学报,2002,12(4):65-69.
Bai Mingzhou,Wang Lianjun,Xu Zhaoyi.Study on a neutral network model and its application in predicting the risk of rock blast[J].China Safety Science Journal,2002,12(4):65-69.
7 高玮.基于蚁群聚类算法的岩爆预测研究[J].岩土工程学报,2010,32(6):874-880.
Gao Wei.Prediction of rock burst based on ant colony clustering algorithm[J].Chinese Journal of Geotechnical Engineering,2010,32(6):874-880.
8 Zhou J,Li X B,Shi X Z.Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J].Safety Science,2012,50(4):629-644.
9 邱道宏,李术才,张乐文,等.基于模型可靠性检查的QGA-SVM岩爆倾向性分类研究[J].应用基础与工程科学学报,2015,23(5):981-991.
Qiu Daohong,Li Shucai,Zhang Lewen,et al.Research on QGA-SVM rock burst orientation classification based on model reliability examination[J].Journal of Basic Science and Engineering,2015,23(5):981-991.
10 贾义鹏,吕庆,尚岳全.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩石力学与工程学报,2013,32(2):344-348.
Jia Yipeng,Qing Lü,Shang Yuequan.Rockburst prediction using particle swarm optimization algorithm and general regression neural network[J].China Journal of Rock Mechanics and Engineering,2013,32( 2):343-348.
11 Liu S,Dou L M,Si G Y,et al.A principal component analysis/fuzzy comprehensive evaluation model for coal burst liability assessment[J].International Journal of Rock Mechanics and Mining Sciences,2016,81(5):62-69.
12 吴顺川,张晨曦,成子桥.基于PCA-PNN原理的岩爆烈度分级预测方法[J].煤炭学报,2019,44(9):2767-2776.
Wu Shunchuan,Zhang Chenxi,Cheng Ziqiao. Prediction of intensity classification of rockburst based on PCA-PNN principle[J].Journal of China Coal Society,2019,44(9):2767-2776.
13 Zhou J,Li X B,Hani S M.Classification of rockburst in underground projects comparison of ten supervised learning methods[J].Journal of Computing in Civil Engineering,2016,30(5):1-19.
14 赵国彦,刘雷磊,王剑波,等.岩爆等级预测的PCA-OPF模型[J].矿冶工程,2019,39(4):1-5.
Zhao Guoyan,Liu Leilei,Wang Jianbo,et al.PCA-OPF model for rock burst prediction[J].Mining and Metallurgical Engineering,2019,39(4):1-5.
15 辛焕平.MATLAB R2017a模式识别与智能计算[M].北京:电子工业出版社,2018.
Xin Huanping.MATLAB R2017a Pattern Recognition and Intelligent Computing[M].Beijing:Publishing House of Electronics Industry,2018.
16 Sun H T,Lü G D,Mo J Q,et al.Application of KPCA combined with SVM in Raman spectral discrimination[J].Optik,2019,184(5):214-219.
17 冯夏庭,赵洪波.岩爆预测的支持向量机[J].东北大学学报(自然科学版),2002,23(1):57-59.
Feng Xiating,Zhao Hongbo.Prediction of rockburst using support vector machine[J].Journal of Northeastern University(Natural Science),2002,23( 1):57-59.
18 陈晋音,熊晖,郑海斌.基于粒子群算法的支持向量机的参数优化[J].计算机科学,2018,45(6):197-203.
Chen Jinyin,Xiong Hui,Zheng Haibin.Parameters optimization for SVM based on particle swarm algorithm[J].Computer Science,2018,45(6):197-203.
19 Pu Y Y,Apel D B,Xu H W.Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier[J].Tunnelling and Underground Space Technology,2019,90:12-18.
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