Determination Method of Rock Strength Based on Digital Drilling Parameters
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摘要:
岩石强度是衡量岩石稳定性和安全性的关键参数，而高效准确地预测岩石强度可以有效指导隧道的开挖和支护工作. 本文收集分析源于不同设备的数字钻进参数和岩石力学性质相关数据，基于钻进过程中的能量传递分析建立数字钻进参数与单轴抗压强度的定量关系；采用机器学习方法建立基于钻进参数的岩石强度预测模型，选择BP神经网络、随机森林、卷积神经网络和长短期记忆网络4种算法比较不同算法的预测效果，最终确定最优模型. 结果显示：相对于理论公式和其他3种机器学习算法，BP神经网络算法在岩石强度预测中表现优秀，其预测结果的均方根误差为5.794，平均绝对误差为4.129，相关系数为
0.9749 .Abstract:Rock strength is a critical parameter for assessing rock stability and safety. Efficient and accurate prediction of rock strength can effectively guide tunnel excavation and support. Digital drilling parameters and mechanical property data of rock were collected from various devices. By analyzing energy transfer during the drilling process, a quantitative relationship between digital drilling parameters and uniaxial compressive strength (UCS) was established. Meanwhile, machine learning methods were employed to develop a rock strength prediction model based on drilling parameters. Four algorithms, including a back-propagation (BP) neural network, random forest, convolutional neural network (CNN), and long short-term memory network were chosen to compare their prediction effects and identify the optimal model. The results indicate that compared to the theoretical formulas and the other three machine learning algorithms, the BP neural network algorithm excels in rock strength prediction, with a root mean square error of 5.794, a mean absolute error of 4.129, and a correlation coefficient of
0.9749 .-
Key words:
- drilling parameters /
- energy method /
- compressive strength /
- neural networks /
- random forests
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表 1 232组数字钻进样本
Table 1. 232 groups of digital drilling samples
组数 来源 强度/MPa 岩石类型 30 组 文献[9] 49.80～61.91 较硬砂岩 1.90～30.81 砂浆试件 45 组 文献[10] 1.98～116.09 砂岩 51 组 文献[11] 2.50～9.00 砂浆试件 36 组 文献[12] 49.80～61.91 较硬砂岩 1.90～35.21 砂浆试件 24 组 文献[13] 48.35～55.68 较硬砂岩 1.56～36.05 砂浆试件 6 组 文献[14] 81.90/69.20/
49.80/49.20完整花岗岩/灰岩/
粉砂岩/砂岩9.40 破碎砂岩 28.10 注浆砂岩 15 组 文献[15] 58.29/约10.89/19.98 完整/破碎/注浆砂岩 75.23/约13.44/21.23 完整/破碎/注浆灰岩 约9.11/约33.45/18.99 完整/破碎/注浆泥岩 1.59～16.49 砂浆试件 25 组 文献[16-17] 1.63～20.80 砂浆试件 
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表 2 部分样本数据
Table 2. Part of sample data
序号 V/
（ mm·min−1）N/
（r·min−1）M/
（N·m）F/
kN岩石强
度/MPa1 105.70 100.00 6.19 0.03 3.29 2 87.94 100.00 7.30 0.02 2.58 3 138.95 100.00 8.38 0.05 3.22 4 124.80 50.00 17.01 0.03 2.37 5 118.08 100.00 21.44 2.07 10.60 6 132.05 100.00 22.34 2.15 10.54 7 83.85 50.00 28.77 2.79 10.23 8 85.18 99.71 48.25 3.86 28.10
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表 3 数据集样本参数统计
Table 3. Dataset sample parameters
取值类型 F/kN M/（N·m） V/
（mm·min−1）N/
（r·min−1）UCS/
MPa最小值 0.01 0.42 0.19 41.92 1.59 最大值 30.51 280.95 3566.00 1035.00 194.58 中位值 1.49 22.34 85.66 100.32 16.24 平均值 6.34 63.40 468.23 222.84 30.69 标准差 9.96 88.01 757.52 192.39 37.30
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表 4 岩石强度计算结果
Table 4. Results of rock strength calculation
序号 真实值/MPa 计算值/MPa 相对误差/% 1 3.29 2.61 10.10 2 2.58 3.14 21.80 3 3.22 2.29 28.93 4 2.37 15.89 24.81 5 10.60 2.21 32.76 6 10.54 4.27 38.98 7 10.23 1.97 30.54 8 28.10 1.07 31.62
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表 5 预测结果对比
Table 5. Comparison of prediction results
序号 真实值/
MPa理论计算
值/MPaBP预测
值/MPa理论计算
误差/%机器学习
误差/%1 3.29 2.23 4.58 32.20 8.93 2 2.58 3.15 5.11 22.27 20.60 3 3.22 2.32 5.06 27.99 5.04 4 2.37 2.63 1.95 10.86 17.52 5 10.60 7.30 11.89 31.18 12.19 6 10.54 7.21 12.33 31.60 16.96 7 10.23 8.40 7.45 17.93 17.40 8 28.10 19.81 34.35 29.51 22.24
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