Research on prediction of support parameters for coal roadways
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摘要: 目前支持向量机（SVM）和随机森林（RF）等算法在煤矿巷道支护领域应用较少。研究了不同的机器学习模型进行支护参数设计的适用性，以建立一个更高性能的模型来实现锚杆支护的合理、科学设计。首先建立煤巷支护智能预测数据库：采用现场调研、问卷调查和文献检索等方式收集煤矿巷道样本；采用缺失值填补、箱形图修改离群点和局部异常因子剔除等方式对数据进行处理，建立煤巷支护数据库。提出一种基于合成少数类过采样（SMOTE）−遗传算法（GA）−SVM的煤巷支护参数预测模型：将数据库中的数据分成训练集与测试集，采用SMOTE技术平衡训练样本，提高模型对少数类样本的拟合能力；训练过程采用GA对SVM的超参数进行全局寻优，进一步提高模型整体性能。测试结果表明，SMOTE−GA−SVM模型的分类精度达到83.8%，比传统的SVM模型提高了21.8%。将SVM、人工神经网络（ANN）、RF、AdaBoost（ADA）和朴素贝叶斯分类器（NBC）等机器学习方法引入到煤巷锚杆支护参数预测中，建立对应的支护参数预测模型，比较结果表明：从最优到最差的预测模型排序分别为SMOTE−GA−SVM、RF、GA−ANN、SVM、NBC和ADA，6种模型的平均分类精度达69.9%，验证了机器学习方法在煤巷锚杆支护参数预测方面的可行性。在山西霍宝干河煤矿有限公司对SMOTE−GA−SVM模型进行了应用，模型预测准确率达87.5%，具有较强的适用性和可靠性。
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关键词:
- 煤矿巷道 /
- 机器学习 /
- 锚杆支护参数 /
- 合成少数类过采样 /
- 遗传算法优化支持向量机
Abstract: Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability. -
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图 5 煤巷支护数据库的整体构建流程
Figure 5. The overall building process of the coal roadway support database
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图 6 SMOTE−GA−SVM支护参数预测模型建立流程
Figure 6. SMOTE-GA-SVM supporting parameter prediction model establishment process
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图 7 输入变量在支护参数预测模型上的重要性
Figure 7. Importance of input variables in support parameter prediction model
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图 8 顶板锚杆间距GA−ANN预测模型的网络拓扑结构
Figure 8. Network topology of GA-ANN prediction model of roof bolt spacing
表 1 基于LOF的异常样本检测结果
Table 1 Test results of abnormal samples based on local outlier factor(LOF)
k=4 k=5 k=6 k=7 k=8 k=9 k=10 105 105 105 105 33 14 14 84 33 33 33 105 33 25 129 84 84 84 14 84 49 33 129 154 124 84 25 33 124 124 129 154 154 49 84 154 154 124 14 124 105 105 36 75 90 129 25 154 154 90 36 17 25 49 124 124 75 44 44 109 109 75 75 70 14 109 75 129 109 59 
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表 2 顶板锚杆支护参数统计
Table 2 Statistics of roof anchor bolt support parameters
参数名称 参数值 频数 参数名称 参数值 频数 直径 18 mm 11 间距 700 mm 5 20 mm 46 800 mm 39 22 mm 60 900 mm 36 长度 2 000 mm 14 1 000 mm 16 2 200 mm 17 1 100 mm 9 2 400 mm 57 1 200 mm 12 2 500 mm 20 排距 700 mm 10 2 600 mm 9 800 mm 33 数量 4 根 13 900 mm 16 5 根 28 1 000 mm 38 6 根 53 1 100 mm 7 7 根 23 1 200 mm 13
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表 3 顶板锚索支护参数统计
Table 3 Statistics of roof anchor cable support parameters
参数名称 参数值 频数 参数名称 参数值 频数 直径 15.24 mm 14 长度 5300 mm 20 17.89 mm 36 6300 mm 39 18.7 mm 18 7300 mm 22 21.6 mm 17 8300 mm 32 22 mm 32 9300 mm 4 数量 1 根 4 布置方式 1 24 2 根 68 2 71 3 根 39 3 22 4 根 6
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表 4 帮部支护参数统计
Table 4 Side support parameter statistics
参数名称 参数值 频数 参数名称 参数值 频数 直径 16 mm 7 间距 700 mm 10 18 mm 20 800 mm 41 20 mm 41 900 mm 21 22 mm 49 1 000 mm 27 长度 1 800 mm 16 1 100 mm 4 2 000 mm 25 1 200 mm 14 2 200 mm 12 排距 700 mm 10 2 400 mm 43 800 mm 33 2 500 mm 21 900 mm 15 数量 2 根 7 1 000 mm 39 3 根 29 1 100 mm 7 4 根 52 1 200 mm 13 5 根 29
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表 5 GA全局寻优结果
Table 5 Global optimization results of GA
支护特征 cbest gbest 支护特征 cbest gbest 顶板锚杆直径 85.26 2.32 帮部锚杆间距 33.65 5.30 顶板锚杆长度 6.75 0.63 帮部锚杆排距 54.23 2.65 顶板锚杆间距 70.76 7.44 帮部锚杆数量 30.23 4.64 顶板锚杆排距 81.04 2.07 锚索直径 74.62 7.55 顶板锚杆数量 32.97 3.71 锚索长度 61.50 1.51 帮部锚杆直径 73.86 8.96 锚索数量 87.00 2.65 帮部锚杆长度 10.53 5.07 锚索布置 70.72 5.30
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表 6 机器学习模型在测试集上的分类精度
Table 6 Classification precision of machine learning model on test set
% 模型 顶板锚杆精度 帮部锚杆精度 顶板锚索精度 平均精度 直径 长度 间距 排距 数量 直径 长度 间距 排距 数量 直径 长度 数量 布置 SVM 66.0 83.6 66.0 79.2 74.8 54.0 70.4 70.4 64.8 57.2 54.0 79.2 74.8 69.2 68.8 SMOTE−GA−SVM 86.0 89.8 82.4 84.3 82.1 75.3 79.2 88.4 77.1 77.2 83.3 93.5 90.6 84.0 83.8 RF 79.2 74.8 52.8 83.6 66.0 70.4 61.6 61.6 74.8 52.8 57.2 92.4 88.0 79.2 71.0 ADA 72.6 64.5 53.8 57.8 57.8 48.4 51.1 37.6 59.2 61.8 48.4 78.7 80.7 75.3 60.5 GA−ANN 93.4 63.4 64.4 55.0 66.0 62.7 66.0 57.2 71.5 55.0 74.8 89.8 79.8 82.5 70.1 NBC 66.0 66.0 44.0 44.0 79.2 70.4 57.2 57.2 57.2 74.8 48.4 81.8 88.0 74.8 64.9
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表 7 霍州矿区干河煤矿的特征参数
Table 7 Characteristic parameters of Ganhe Coal Mine in Huozhou Mining area
序号 巷道名称 煤层厚
度/m煤层强
度/MPa基本顶厚
度/m基本顶强
度/MPa直接顶厚
度/m直接顶强
度/MPa直接底厚
度/m直接底强
度/MPa埋深/m 巷道高度/m 巷道宽度/m 1 2−1161巷 4.20 9.38 1.70 65.06 6.40 51.79 1.10 51.79 450 3.6 5.0 2 2−1261巷 3.75 15.00 4.80 86.09 2.45 65.06 2.90 19.16 420 3.7 5.0 3 三采区辅助运输巷 0.78 9.38 3.12 65.06 4.96 86.09 4.60 45.40 420 3.5 4.8 4 2−1021巷 4.20 14.54 3.10 57.32 3.89 25.00 0.80 37.45 500 3.8 5.0
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表 8 SMOTE−GA−SVM模型应用结果
Table 8 Application result of SMOTE-GA-SVM model
序号 巷道名称 顶板锚杆 帮部锚杆 顶板锚索 直径/
mm长度/
mm间距/
mm排距/
mm数量/
根直径/
mm长度/
mm间距/
mm排距/
mm数量/
根直径/
mm长度/
mm布置
方式数量/
根1 2−1161巷 真实值 22 2500 900 900 6 22 2500 900 900 4 21.60 8300 2 3 测试值 22 2400 900 900 6 22 2400 900 900 4 21.60 8300 2 3 2 2−1261巷 真实值 22 2500 800 800 7 22 2500 800 800 5 21.60 8300 3 3 测试值 22 2500 800 800 7 22 2500 800 800 5 21.60 8300 3 3 3 三采区辅助运输巷 真实值 20 2000 800 800 7 20 2000 800 800 2 15.24 7300 2 2 测试值 20 2000 800 1000 7 20 2000 800 1000 2 17.89 7300 2 2 4 2−1021巷 真实值 22 2400 900 1000 6 22 2400 1000 1000 4 21.60 6300 2 2 测试值 22 2400 900 1000 6 20 2400 1000 1000 4 21.60 7300 2 2
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