基于CGA模型的盾构扭矩预测研究

以盾构近距离下穿既有车站结构为背景，提出了一种结合卷积神经网络(Convolutional Neural Networks, CNN)、门控制循环单元神经网络(Gated Recurrent Unit, GRU)和注意力机制(Attention)的新型盾构荷载预测模型。首先用CNN-Attention模型提取数据的高维空间特征并区分不同特征的重要性，然后通过GRU模型提取数据的时序特性，紧接着通过注意力机制提取出重要时间节点信息，最后得出预测的结果。为验证所提模型的预测效果，选取了4种现有的算法进行比较。结果表明所提出的模型在三种评价指标上均优于其他算法模型，同时该模型还可为盾构刀具磨损、地表及结构变形等方面的预测研究提供思路。

Study on Shield Torque Prediction Based on CGA Model

Shield load is an important parameter of shield machine, and accurate prediction of shield load is very important to ensure the safe construction of shield tunnel. In this paper, a new load prediction model (CGA), combining convolutional neural network (CNN), gate recurrent unit neural network (GRU) and attention mechanism (Attention), is proposed based on the shield machine cross existing station at close range. The CNN-Attention model is first used to extract the high-dimensional spatial features of the data and distinguish the importance of different features. Then the GRU model is used to extract the temporal characteristics of the data, followed by the attention mechanism to extract the important time node information. Finally, the prediction results are obtained. To verify the prediction performance of the proposed model, four existing algorithms are selected for comparison. The results show that the proposed model in this paper outperforms other models in three evaluation metrics, and the proposed model can also provide reference for predicting researches on shield tunneling tool wear, surface and structural deformation, etc.