Prediction of Rock Burst Intensity Based on the ISCSO-KELM Model
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摘要: 针对施工过程中的岩爆事故防控需求，提出了一种基于改进沙猫群-核极限学习机（improved sand cat swam optimization-kernel based extreme learning machine，ISCSO-KELM）算法的新型岩爆预测模型。在指标选取方面，采用围岩最大切向应力、单轴抗压强度、单轴抗拉强度和岩石弹性能量指数作为岩爆的评价指标。选取国内外105组岩爆实例作为机器学习样本，通过对比随机森林、K最近邻、支持向量机、核极限学习机等模型所预测的混淆矩阵，验证了ISCSO-KELM模型在评估精确率（96.774 2%）和召回率方面的优越性。最后，以相关工程实例作为验证集对岩爆等级进行验证。结果表明，ISCSO-KELM模型在处理岩爆问题上可以更好地捕捉岩爆等级与评价指标间的内在关联，具有良好的适用性，为岩爆预测提供了一种新的技术途径。Abstract: In order to reduce the occurrence of rock burst accidents during construction, the rock burst intensity should be assessed. In this paper, we propose a new rock burst prediction model based on the improved sandcat swam optimization-kernel based extreme learning machhine (ISCSO-KELM) algorithm. The maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength and rock elastic energy index were selected as the evaluation indexes of rock burst. 105 domestic and international examples of rock burst were selected as samples for machine learning. Comparison of the relative ratios of the model presented herein with confusion matrix predicted by models including random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and kernel based extreme learning machhine (KELM) models shows that, the ISCSO-KELM model is superior at assessing both evaluation accuracy and recall. The evaluation accuracy of the model reached 96.774 2%, indicating the superiority of ISCSO-KELM. Relevant engineering cases were used to verify the rock burst intensity. The results show that ISCSO-KELM model is more effective in capturing the connection between rock burst intensity and the indexes, thus providing a new highly applicable method for rock burst prediction.
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表 1 岩爆等级及分类依据
Table 1. Rock burst intensity and classification basis
Intensity prediction Classification Classification basis Sound size Rock explosion performance Fragments size Impact on construction Ⅰ No rock burst Ⅱ Slight rock
burstNo sound or
weak soundRock fall freely or
after relaxationThe rock is small in size
and small in quantityIt has little impact
on constructionⅢ Medium rock
burstThere was a
crisp burstLings or blocks pop up
to the face surfaceThe rock size is large and
number is numerousIt has a certain impact
on the constructionⅣ Severe rock
burstThere is a
loud noiseSharp-edged fragments
of rock flew outThe rock size is large and
number is numerousIt has a great impact
on the construction
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表 2 岩爆分级标准
Table 2. Rock burst classification criteria
Intensity prediction σθ/MPa σc/MPa σt/MPa Wet Ⅰ 0−24 0−80 0−5 0−2.0 Ⅱ 24−60 80−120 5−7 2.0−3.5 Ⅲ 60−126 120−180 7−9 3.5−5.0 Ⅳ 126−200 180−320 9−30 5.0−20
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表 3 ISCSO-KELM的Matlab参数
Table 3. Matlab parameters for ISCSO-KELM
Parameters Value Population quantity 10 Iterations 50 Regularized coefficient upper and lower boundaries [100，1] Kernel function parameters upper and lower boundaries [100，1] Dimension 2 Best_pos [97.363 4，1] Best_score 0.187 6
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表 4 部分岩爆案例实测数据
Table 4. Actual measured data of some rock burst cases
Sample No. σθ /MPa σc/MPa σt/MPa Wet Grade 1 43.08 114.08 12.29 6.12 Ⅲ 2 42.15 83.24 8.52 5.60 Ⅱ 3 40.87 139.00 6.00 0.81 Ⅰ 4 50.09 124.00 5.00 6.53 Ⅱ 5 59.09 88.25 3.60 6.14 Ⅱ 6 62.13 124.00 5.00 4.62 Ⅱ … … … … … … 98 68.85 48.96 13.66 1.35 Ⅲ 99 45.94 78.48 14.25 2.45 Ⅱ 100 80.06 67.65 8.28 3.98 Ⅲ 101 119.69 119.77 9.35 10.19 Ⅳ 102 83.63 112.30 10.13 3.21 Ⅲ 103 103.82 206.28 12.10 6.33 Ⅲ 104 112.38 178.81 12.07 7.68 Ⅳ 105 120.37 72.21 9.53 4.15 Ⅲ
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Estimated value True value xT xP xT xTP xFP xF xFN xTN
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表 6 模型评估指标结果
Table 6. Results of the model evaluation indicators
Rock burst intensity Ac/% P/% F1 Rc/% No rock burst 100.00 66.67 0.800 2 Slight rock burst 93.75 100.00 0.967 7 Medium rock burst 100.00 100.00 1.000 0 Severe rock burst 100.00 100.00 1.000 0 All intensities 96.774 2
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表 7 样本数据
Table 7. Sample data
Sample No. σθ/MPa σc/MPa σt/MPa Wet Grade Project case 1 63.80 110.00 4.50 6.31 Ⅲ Maluping mine 750 m K1 2 2.60 20.00 3.00 1.39 Ⅰ Maluping mine 750 m K2 3 46.20 105.00 5.30 2.30 Ⅱ Jinping Ⅱ Hydropower Station 1+640 4 46.40 100.00 4.90 2.00 Ⅱ Jinping Ⅱ Hydropower Station 1+731 5 90.52 107.00 3.92 3.10 Ⅲ Jinping Ⅱ Hydropower Station 3+000 6 88.41 105.00 5.33 2.30 Ⅲ Jinping Ⅱ Hydropower Station 3+390
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表 8 模型结果对比表
Table 8. Comparison of the model results
Sample No. Predicted rock burst grade Actual rock burst grade RF SVM KELM ISCSO-KELM 1 Ⅲ Ⅲ Ⅲ Ⅲ Ⅲ 2 Ⅰ Ⅰ Ⅰ Ⅰ Ⅰ 3 Ⅱ Ⅲ* Ⅲ* Ⅱ Ⅱ 4 Ⅱ Ⅱ Ⅱ Ⅱ Ⅱ 5 Ⅲ Ⅲ Ⅲ Ⅲ Ⅲ 6 Ⅲ Ⅱ* Ⅲ Ⅲ Ⅲ Note: “*” indicate a discrepancy from the actual grade.
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