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Multi-step prediction model enhanced by adaptive denoising and encoder-decoder for shield machine cutterhead torque in complex conditions
Tunnelling and Underground Space Technology ( IF 6.7 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.tust.2025.106398
Deming Xu, Yuan Wang, Jingqi Huang, Shujun Xu, Kun Zhou

Cutterhead torque reflects the obstruction extent of geological environment on the shield machine, and its prediction can assist operators to adjust control parameters to improve construction efficiency and avoid machine jamming. However, tunneling in complex geological or working conditions often results in high cutterhead torque fluctuations and noise, which seriously affects the accuracy of torque prediction. This study proposes a multi-step prediction model for cutterhead torque enhanced by adaptive denoising and encoder-decoder. In this model, a novel adaptive denoising method for cutterhead torque is employed to improve prediction accuracy under complex conditions. Moreover, by introducing encoder-decoder method, the processing capability for multi-time dimensional data and multi-step prediction performance of LSTM neural networks are further improved. The effectiveness of proposed model is verified through an application to the Heyan Road River Crossing project. The results of this study can assist operators in achieving precise adjustment of control parameters under complex conditions.

中文翻译:


复杂工况下盾构机刀盘扭矩自适应降噪和编码器解码增强的多步预测模型



刀盘扭矩反映了地质环境对盾构机的阻碍程度,其预测可协助操作人员调整控制参数,提高施工效率,避免机器卡死。然而,在复杂的地质或工况下掘进往往会导致较大的刀盘扭矩波动和噪声,严重影响扭矩预测的准确性。本研究提出了一种自适应去噪和编码器解码器增强的刀盘扭矩多步预测模型。该模型采用一种新的刀盘扭矩自适应降噪方法,以提高复杂条件下的预测精度。此外,通过引入编码器-解码器方法,进一步提高了 LSTM 神经网络对多时间维度数据的处理能力和多步骤预测性能。通过申请和岩路过河项目验证了所提模型的有效性。本研究结果可以帮助操作人员实现复杂条件下控制参数的精确调整。
更新日期:2025-01-17
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