刀盘扭矩和刀盘推力是保障盾构机正常掘进的关键参数,对其准确预测可有效指导设备运行。本项研究的数据来源于成都地铁19号线土压平衡(EPB)盾构机的掘进数据。深入剖析了EPB盾构掘进数据的特点,提出了一种包含数据分割、异常值处理、数据降噪和数据编译4个阶段的标准数据预处理算法。在Butterworth滤波器基础上,利用门控循环单元(GRU)建立了盾构掘进参数预测模型,基于RMSE和MAE指标综合评估预测模型的预测效果。结果表明:预测模型对不同地质条件下的刀盘扭矩和刀盘推力掘进参数均能实现良好预测;经过Butterworth滤波,预测模型的预测精度提高显著;砂岩地层中,预测模型对刀盘扭矩的预测误差最小,RMSE和MAE分别为4.91和3.86。基于GRU算法的掘进参数预测,可提高盾构机掘进状态的判断水平,利于施工参数优化调整。

Cutterhead torque (<i>T</i>) and cutterhead thrust (<i>F</i>) are key parameters to ensure the normal tunneling of shield machine,and their accurate prediction can effectively guide equipment operation.The research datasets are collected from earth pressure balance (EPB) shield machine on line 19 of the Chengdu Metro.By analyzing the characteristics of EPB data,we develop a standard data preprocessing algorithm that includes data segmentation,outlier processing,data filtering and data compilation.Based on Butterworth filter,we establish the prediction model of EPB tunneling parameters by gated recurrent unit (GRU) algorithm,and then comprehensively assess the prediction effect of the model by RMSE and MAE.Results manifest that the proposed model can achieve good prediction for <i>T</i> and <i>F</i> under different geological conditions,and the prediction accuracy of the GRU model in fusion with Butterworth filter is better than that of the unfiltered model.In sandstone formation,the prediction error of the model for <i>T</i> is the smallest,and the RMSE and MAE are 4.91 and 3.86,respectively.The prediction of tunneling parameters based on GRU algorithm can significantly improve the judgment level of shield tunneling state,which is conducive to the optimization and adjustment of construction parameters.