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Predicting Slurry Pressure Balance with a Long Short-Term Memory Recurrent Neural Network in Difficult Ground Condition
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-02-22 , DOI: 10.1155/2021/6678355
Qiang Wang 1 , Xiongyao Xie 1 , Hongjie Yu 2 , Michael A Mooney 2
Affiliation  

The safety of tunneling with shield tunnel boring machines largely depends on the tunnel face pressure, which is currently decided by human operators empirically. Face pressure control is vulnerable to human misjudgment and human errors can cause severe consequences, especially in difficult ground conditions. From a practical perspective, it is therefore beneficial to have a model capable of predicting the tunnel face pressure given operation and the changing geology. In this paper, we propose such a model based on deep learning. More specifically, a long short-term memory (LSTM) recurrent neural network is employed for tunnel face pressure prediction. To correlate with PLC data, linear interpolation is employed to transform the borehole geological data into sequential geological data according to the shield machine position. The slurry pressure in the excavation chamber (SPE) is taken as the output in the case study of Nanning Metro, which is confronted with the clogging problem due to the mixed ground of mudstone and round gravel. The LSTM-based SPE prediction model achieved an overall MAPE and RMSE of 3.83% and 10.3 kPa, respectively, in mudstone rich ground conditions. Factors that influence the model, including different kinds and length of input data and comparison with the traditional machine learning-based model, are also discussed.

中文翻译:

在困难地面条件下使用长期短期记忆递归神经网络预测泥浆压力平衡

使用盾构隧道掘进机进行隧道掘进的安全性在很大程度上取决于隧道的工作面压力,这目前由操作人员凭经验确定。面部压力控制容易受到人为错误判断的影响,人为错误会导致严重的后果,尤其是在困难的地面条件下。因此,从实际的角度来看,拥有一个能够在给定的操作和不断变化的地质条件下预测隧道面压力的模型是有益的。在本文中,我们提出了一种基于深度学习的模型。更具体地说,长短期记忆(LSTM)递归神经网络用于隧道面压力预测。为了与PLC数据关联,采用线性插值法将钻孔地质数据根据盾构机位置转换为顺序地质数据。以南宁地铁为例,以开挖室(SPE)中的泥浆压力为输出,由于泥岩和圆砾石混合地基,因此面临堵塞问题。在富含泥岩的地面条件下,基于LSTM的SPE预测模型分别实现了3.83%和10.3 kPa的总MAPE和RMSE。还讨论了影响模型的因素,包括输入数据的种类和长度以及与基于传统机器学习的模型的比较。
更新日期:2021-02-22
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