Prediction of Rockburst Intensity Grade Based on SVM and Adaptive Boosting Algorithm
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摘要: 岩爆烈度等级的准确预测对减轻乃至消除岩爆危害具有重要意义.针对岩爆烈度等级预测模型特征选取模糊和预测准确度不高问题，提出了一种ReliefF-Pearson特征选择下基于SSA-SVM-AdaBoost算法的岩爆等级预测模型.结合ReliefF的权值思想和Pearson系数的相关性原理对特征指标进行选择，利用麻雀搜索算法（SSA）优化支持向量机（SVM）以获得最优模型初始参数，将多个SSA优化后的SVM作为弱分类器组成自适应增强学习算法（AdaBoost）的强分类器.首先通过收集分析国内外岩爆案例数据，选取7种特征指标构成原始特征空间，然后利用ReliefF-Pearson从原始特征空间中筛选出4维优势特征，采用随机过采样对数据进行处理，最后将其输入到SSA-SVM-AdaBoost模型中进行分类预测.研究结果表明：基于ReliefF-Pearson的特征选择方法能够有效提取优势特征；基于多SSA-SVM的AdaBoost模型预测准确率相较于SSA-SVM和单层决策树AdaBoost模型均提高12.5%，相较于SVM提高31.25%，说明SSA-SVM作为弱分类器在分类性能上要优于单层决策树，AdaBoost增强算法集成多个单分类器要优于单个分类模型，且数据过采样处理没有影响模型预测集准确率，表明SSA-SVM-AdaBoost模型可有效应用于岩爆烈度等级预测，为岩爆预测问题提供新思路.Abstract: Accurate prediction of rockburst intensity grade is of great significance for mitigating and eliminating rockburst hazards. Aiming at the problems of uncertain feature selection and low prediction accuracy of rockburst intensity grade prediction model, in this paper it proposes a rockburst grade prediction model based on SSA-SVM-AdaBoost algorithm with ReliefF-Pearson feature selection. The method combines the weight idea of ReliefF and the correlation principle of Pearson coefficient to select feature indexes, and SSA-SVM-AdaBoost algorithm is proposed by using the sparrow search algorithm (SSA) optimized support vector machine(SVM)classifier as the AdaBoost weak classifier to solve the multiclassification problem. First, 7 kinds of feature indicators are selected to form the original feature space by analyzing rockburst case data, then the 4⁃dimension advantage features are selected by ReliefF-Pearson method. The data is processed with random oversampling before input SSA-SVM-AdaBoost prediction model. The research results show that the feature selection method based on ReliefF-Pearson can effectively extract advantage feature indicators. Compared with SSA-SVM and AdaBoost based on single-layer decision tree, the prediction accuracy of SSA-SVM-AdaBoost model is improved by 12.5%, and 31.25% compared with SVM. It shows that SSA-SVM as a weak classifier is better than a single-layer decision tree in classification performance, and the AdaBoost enhancement algorithm integrating multiple single classifiers is better than a single classification model. Data oversampling process does not affect the accuracy of the model prediction set, but improves the prediction accuracy of the training set. It is proved that the proposed model can be effectively applied to rockburst intensity grade prediction, which provides a new perspective for this problem.
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图 4 岩爆等级与围岩洞壁最大切向应力$ {\sigma }_{\theta } $的关系
Fig. 4. Relationship between rockburst grade and maximum tangential stress of surrounding rock cave wall
图 5 岩爆等级与岩石应力系数$ {\sigma }_{\theta }/{\sigma }_{c} $的关系
Fig. 5. Relationship between rockburst grade and stress coefficient
图 6 岩爆等级与岩石弹性能指数$ {W}_{et} $的关系
Fig. 6. Relationship between rockburst grade and elastic energy index
图 8 基于SSA-SVM-AdaBoost的岩爆等级预测模型
Fig. 8. Prediction model of rockburst grade based on SSA-SVM-AdaBoost
图 9 模型各等级预测准确率对比图
Fig. 9. The comparison chart of the prediction accuracy of each level of the model
表 1 岩爆烈度预测的特征指标和分级标准汇总表
Table 1. Summary of feature indicators and grading standards for rockburst intensity prediction
参考文献 特征指标 无岩爆 轻微岩爆 中等岩爆 强烈岩爆 张乐文等, 2010 $ {\sigma }_{c} $ ＜80 80~120 120~180 ＞180 $ {\sigma }_{c}/{\sigma }_{1} $ ＞14.5 5.5~14.5 2.5~5.5 ＜2.5 $ {\sigma }_{c}/{\sigma }_{t} $ ＞40.0 26.7~40.0 14.5~26.7 ＜14.5 $ {\sigma }_{\theta }/{\sigma }_{c} $ ＜0.3 0.3~0.5 0.5~0.7 ＞0.7 Wet ＜2.0 2.0~3.5 3.5~5.0 ＞5.0 $ H $ ＜50 50~200 200~700 ＞700 KV ＜0.55 0.55~0.65 0.65~0.75 ＞0.75 Zhou et al., 2012 H、$ {\sigma }_{c}/{\sigma }_{t} $、$ {\sigma }_{\theta } $、$ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c} $、$ {\sigma }_{t} $、Wet Dong et al., 2013 $ {\sigma }_{c}/{\sigma }_{t} $、$ {\sigma }_{\theta } $、$ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c} $、$ {\sigma }_{t} $、Wet 周科平等, 2013 $ {\sigma }_{c} $、$ {\sigma }_{c}/{\sigma }_{t} $、$ {\sigma }_{\theta }/{\sigma }_{c} $、KV Wet ＜2 2~4 4~6 ＞6 王羽等, 2013 KV、$ {\sigma }_{c}/{\sigma }_{t} $、$ {\sigma }_{\theta }/{\sigma }_{c} $、Wet KV ＜0.50 0.50~0.65 0.65~0.80 0.80~1 Zhou et al., 2016 $ {\sigma }_{c} $、$ {\sigma }_{t} $、$ {\sigma }_{\theta } $、$ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c}/{\sigma }_{t} $、Wet 吴顺川等, 2019 $ {\sigma }_{c} $、$ {\sigma }_{t} $、$ {\sigma }_{\theta } $、$ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c}/{\sigma }_{t} $、Wet 李明亮等, 2020 $ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c}/{\sigma }_{t} $、$ {\sigma }_{c} $、Wet 高磊等, 2021 $ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c}/{\sigma }_{t} $、Wet 周航等, 2022 $ {\sigma }_{c}/{\sigma }_{t} $、KV、Wet $ {\sigma }_{c}/{\sigma }_{\mathrm{m}\mathrm{a}\mathrm{x}} $ $ \ge $7 4~7 2~4 ＜2 $ {\sigma }_{\theta }/{\sigma }_{c} $ ＜0.2 0.2~0.3 0.3~0.55 $ \ge $0.55 汤志立和徐千军, 2020 $ {\sigma }_{c}/{\sigma }_{t} $、B2、H、$ {\sigma }_{\theta } $、$ {\sigma }_{\theta }/{\sigma }_{c} $、$ {\sigma }_{c} $、$ {\sigma }_{t} $、Wet 杨小彬等, 2021 $ {\sigma }_{\theta } $、$ {\sigma }_{c} $、$ {\sigma }_{t} $ 注：$ {\sigma }_{c} $为岩石单轴抗压强度，MPa；$ {\sigma }_{1} $为围岩洞壁的轴向应力，MPa；$ {\sigma }_{t} $为岩石单轴抗拉强度，MPa；$ {\sigma }_{\theta } $为围岩洞壁最大切向应力，MPa；Wet为岩石弹性能指数；H为隧洞埋深，m；$ {\sigma }_{\mathrm{m}\mathrm{a}\mathrm{x}} $为围岩洞壁最大主应力, MPa；KV为岩体完整程度；B2为岩石单轴抗压强度与抗拉强度之差与两者之和的比值；未标明的指标分级标准表示与张乐文等（2010）论文中指标分级标准相同. 
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表 2 部分岩爆案例实测数据
Table 2. Measured data of some rockburst cases
工程名称 H(m) $ {\sigma }_{\theta } $(MPa) $ {\sigma }_{c} $(MPa) $ {\sigma }_{t} $(MPa) $ {\sigma }_{\theta }/{\sigma }_{c} $ $ {\sigma }_{c}/{\sigma }_{t} $ Wet 等级 鱼子溪水电站引水隧道 200 90 170 11.3 0.53 15.04 9 Ⅲ 二滩水电站2#支洞 194 90 220 7.4 0.41 29.73 7.3 Ⅱ 拉西瓦水电站地下厂房 300 55.4 176 7.3 0.32 24.11 9.3 Ⅲ 天生桥Ⅱ级水电站引水隧道 400 30 88.7 3.7 0.34 23.97 6.6 Ⅲ 瑞典Vietas水电站引水隧道 250 80 180 6.7 0.44 26.87 5.5 Ⅱ 大相岭隧道YK55+119 362 25.7 59.7 1.3 0.43 45.9 1.7 Ⅰ 大相岭隧道ZK61+201 980 58.2 83.6 2.6 0.69 32.1 5.9 Ⅳ 日本关越隧道 890 89 236 8.3 0.38 28.43 5 Ⅲ 马路坪矿井巷 700 3.8 20 3 0.19 6.67 1.39 Ⅰ 括苍山隧道 204 35 133.4 9.3 0.26 14.34 2.9 Ⅱ 通榆隧道K21+740 1030 43.62 78.1 3.2 0.56 24.41 6 Ⅱ 河滩水电站引水隧道 203 157.3 91.23 6.92 0.58 13.18 6.27 Ⅳ
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表 3 特征指标相关性矩阵
Table 3. Correlation matrix of feature indicators
编号 $ H $ $ {\sigma }_{\theta } $ $ {\sigma }_{\theta }/{\sigma }_{c} $ $ {W}_{et} $ $ H $ 1 0.14 0.27 ‒0.23 $ {\sigma }_{\theta } $ 0.14 1 0.29 0.37 $ {\sigma }_{\theta }/{\sigma }_{c} $ 0.27 0.29 1 ‒0.001 $ {W}_{et} $ ‒0.23 0.37 ‒0.001 1
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表 4 岩爆烈度预测特征指标及分级标准
Table 4. Feature indicators and grading standards for rockburst intensity prediction
岩爆等级 预测指标 埋深H 围岩洞壁最大切向应力$ {\sigma }_{\theta } $ 岩石应力系数$ {\sigma }_{\theta }/{\sigma }_{c} $ 弹性变形能系数Wet 无（Ⅰ） ＜50 ＜24 ＜0.3 ＜2.0 轻微（Ⅱ） 50~200 24~60 0.3~0.5 2.0~3.5 中等（Ⅲ） 200~700 60~126 0.5~0.7 3.5~5.0 强烈（Ⅳ） ＞700 ＞126 ＞0.7 ＞5.0
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表 5 模型各等级预测情况及总体准确率
Table 5. Prediction of each level of the model and overall accuracy
岩爆烈度等级 实际数量 预测模型 SVM SSA-SVM AdaBoost SSA-SVM-AdaBoost 1 3 2 3 3 3 2 5 2 4 4 3 3 6 5 4 5 6 4 2 0 1 0 2 总计 16 9 12 12 14 准确率 56.25% 75% 75% 87.5%
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表 6 随机过采样对模型准确度的影响
Table 6. The effect of random oversampling on model accuracy
模型 数据处理方法 原始数据 随机过采样处理 SVM 56.25% 68.75% SSA-SVM 75% 81.25% AdaBoost 75% 75%% SSA-SVM-AdaBoost 87.5% 87.5%
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表 7 桑珠岭隧道岩爆数据和预测结果
Table 7. Rockburst data and prediction results of Sangzhuling Tunnel
样本编号 隧道里程 特征指标 实际等级 本文模型 H(m) $ {\sigma }_{\theta } $(MPa) $ {\sigma }_{\theta }/{\sigma }_{c} $ Wet 1 DK188+280~DK188+896 1100 58.4 0.41 4.60 Ⅲ Ⅲ 2 DK188+896~DK188+946 860 54.4 0.38 4.60 Ⅲ Ⅲ 3 DK188+946~DK189+167 780 54.0 0.38 4.60 Ⅲ Ⅲ 4 DK189+167~DK189+217 750 54.8 0.38 4.60 Ⅲ Ⅲ 5 DK189+217~DK189+390 650 41.9 0.28 4.00 Ⅱ Ⅱ 6 DK189+430~DK189+450 590 30.9 0.21 4.00 Ⅲ Ⅱ（×） 7 DK189+450~DK189+610 460 27.2 0.18 4.00 Ⅱ Ⅱ 8 DK189+660~DK189+065 100 32.3 0.22 4.00 Ⅱ Ⅱ
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