Deep learning-based prediction method for chamber pressure field in shield machines
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摘要: 土舱压力是盾构机受力状态和掌子面稳定等核心问题中的关键因素。土舱压力具有显著的空间变异性，其形成演化机制源于装备与岩土之间的复杂耦合作用，与地质特征、掘进参数等多源参数相关。然而，现有土舱压力预测方法一般未考虑空间分布特征或地质参数影响。针对该问题，提出了一种基于空间分布物理特征函数导引深度学习的盾构机土舱压力场预测方法。该方法构建物理特征函数用于解耦土舱压力空间分布特征，采用卷积神经网络和门控循环单元分别提取多源参数历史信息的空间特征和特征系数的时序特征，结合多源参数实时信息对特征系数进行预测，从而实现土舱压力场的预测。以长沙地铁四号线某区段为案例，利用该方法准确预测了土舱压力空间分布实测数据，准确率高达0.98，验证了所提方法的有效性。敏感性分析表明，不同地层中土舱压力空间分布特征系数的主要敏感参数基本一致，但其敏感度随地层地质条件的变化规律差异显著，可为复杂地层盾构机土舱压力精细化调控提供参考。Abstract: The chamber pressure is a key factor in the core issues of the stress state of equipment and the stability of tunnel face during shield tunneling. It exhibits significant spatial variability, and its formation and evolution originate from the complex coupling effects of geotechnics and mechanism, which is related to multiple parameters such as geological features and tunnelling parameters. Yet, the spatial distribution features or geological features are generally ignored in the existing methods for predicting the chamber pressure. To probe this problem, a method to predict the chamber pressure field in shield machines is proposed based on the deep learning algorithm guided by the physical feature function of spatial distribution. This method constructs the physical feature function for decoupling the spatial distribution features of the chamber pressure, uses the convolutional neural network and gated recurrent unit to extract the spatial features of the historical information of multi-source parameters and the temporal features of feature coefficient, respectively, and combines the real-time information of multi-source parameters to predict the feature coefficient, so as to realize the prediction of the chamber pressure field. Taking a section of Changsha Metro Line 4 as a case study, this method is used to accurately predict the measured spatial distribution of the chamber pressure with an accuracy of 0.98, which verifies the effectiveness of the proposed method. The sensitivity analysis reveals that the main sensitive parameters of the spatial distribution feature coefficient of the chamber pressure are basically the same in different strata, but their sensitivities vary significantly with the geological conditions of strata. The results may provide guidance for the refined control of the chamber pressure of shield machines in complex strata.
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图 1 物理特征函数三维示意图
Figure 1. Three-dimensional schematic diagram of physical feature function
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图 7 空间分布特征系数自相关分析
Figure 7. Autocorrelation analysis of spatial distribution feature coefficient
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图 8 预测与实测土舱压力对比图
Figure 8. Comparison between predicted and measured chamber pressures
表 1 输入参数统计特征表
Table 1 Statistical characteristics of input parameters
参数类别 特征名称 平均值 最小值 最大值 标准差 单位 施工参数 土舱压力1 0.77 0.41 1.44 0.20 bar 土舱压力2 0.83 0.47 1.47 0.18 bar 土舱压力3 1.02 0.55 1.56 0.18 bar 土舱压力4 0.65 0.33 1.11 0.14 bar 土舱压力5 1.74 1.17 2.47 0.27 bar 土舱压力6 1.50 1.03 1.96 0.19 bar 均值项（a） 1.11 0.72 1.56 0.18 bar 上下梯度项（b） -0.56 -0.90 -0.26 0.09 bar 左右梯度项（c） 0.17 0.07 0.30 0.04 bar 局部异质项（d） 0.24 0.12 0.45 0.07 bar 装备侧滚 35.09 -65.77 103.16 30.23 mm 装备倾角 -0.08 -1.15 2.02 0.88 ° 刀盘转速 1.17 0.43 1.45 0.12 r/min A组油缸推进压力 82.20 33.08 149.59 15.61 MPa B组油缸推进压力 95.03 45.38 158.28 17.95 MPa C组油缸推进压力 115.01 56.61 167.86 17.91 MPa D组油缸推进压力 96.62 48.43 165.72 18.64 MPa 推进速度 31.27 8.99 65.03 8.12 mm/min 贯入度 25.06 7.12 52.92 6.73 mm/r 总推进力 8.33 2.92 12.89 1.84 MN 螺旋机转速 5.20 -0.03 13.88 1.97 r/min 螺旋机上卸料门开度 373.44 112.20 638.49 91.57 mm 螺旋机下卸料门开度 655.86 286.55 738.57 48.93 mm 膨润土流量1 70.11 20.03 161.32 28.52 mm3 膨润土流量2 49.84 11.57 142.68 24.47 mm3 砂浆注入口压力 7.50 2.64 16.33 2.78 MPa 泡沫进水管压力 3.11 0.90 8.93 1.16 MPa 泡沫枪液体流量1 6.13 0.71 15.57 3.46 mm3 泡沫枪液体流量2 7.14 1.81 14.93 2.70 mm3 泡沫枪空气流量1 129.45 18.39 554.74 76.95 mm3 泡沫枪空气流量2 227.44 -0.67 1133.98 114.54 mm3 泡沫枪压力1 1.34 0.30 5.17 0.58 MPa 泡沫枪压力2 1.11 0.54 1.56 0.17 MPa 泡沫枪压力3 1.54 0.66 2.45 0.42 MPa 泡沫枪压力4 1.75 0.82 2.41 0.29 MPa 泡沫枪压力5 0.92 0.48 2.16 0.36 MPa 地质参数 隧道埋深 21.71 11.25 31.65 5.72 m 地下水位 11.44 0.00 25.45 7.83 m 修正标准贯入次数 2.10 0.00 9.92 1.96 — 修正动力触探次数 0.30 0.00 1.99 0.45 — 修正单轴抗压强度 14.22 0.05 39.37 11.59 kPa 静止土压力 275.62 129.53 409.90 73.56 kPa 
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表 2 不同模型预测误差及精度对比
Table 2 Comparison of predicted errors and accuracies between different models
预测模型 特征系数 MSE RMSE MAE R2 CNN a 0.0009 0.0298 0.0221 0.9796 b 0.0011 0.0325 0.0247 0.9419 c 0.0014 0.0372 0.0282 0.9368 d 0.0009 0.0306 0.0231 0.9800 平均值 0.0011 0.0325 0.0245 0.9596 GRU a 0.0009 0.0303 0.0214 0.9791 b 0.0017 0.0407 0.0316 0.9058 c 0.0004 0.0194 0.0124 0.9832 d 0.0004 0.0205 0.0146 0.9910 平均值 0.0008 0.0277 0.0200 0.9648 CNN-GRU a 0.0005 0.0215 0.0148 0.9895 b 0.0004 0.0189 0.0124 0.9812 c 0.0002 0.0158 0.0097 0.9891 d 0.0003 0.0165 0.0113 0.9943 平均值 0.0003 0.0181 0.0121 0.9885
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表 3 特征函数导引方法与数据驱动方法预测结果对比
Table 3 Comparison of predicted results between feature function-guided method and data-driven method
预测方法 评价指标 土舱压力1 土舱压力2 土舱压力3 土舱压力4 土舱压力5 土舱压力6 平均值 特征函数导引预测土舱压力 R2 0.99886 0.95357 0.94713 0.98468 0.99064 0.98476 0.97665 MSE 0.00005 0.00142 0.00162 0.00026 0.00067 0.00055 0.00076 RMSE 0.00674 0.03763 0.04029 0.01603 0.02596 0.02348 0.02502 MAE 0.00582 0.03249 0.03568 0.01288 0.02113 0.01841 0.02107 数据驱动直接预测土舱压力 R2 0.97509 0.98680 0.98347 0.96658 0.97444 0.97267 0.97651 MSE 0.00086 0.00046 0.00062 0.00148 0.00073 0.00082 0.00083 RMSE 0.02941 0.02149 0.02498 0.03853 0.02693 0.02867 0.02833 MAE 0.02251 0.01614 0.01829 0.02783 0.02107 0.02249 0.02139
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