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 predic-tion 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 fea-tures of the data and distinguish the importance of different features.Then the GRU model is used to ex-tract the temporal characteristics of the data,followed by the attention mechanism to extract the impor-tant time node information.Finally,the prediction results are obtained.To verify the prediction per-formance 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 pro-posed model can also provide reference for predicting researches on shield tunneling tool wear,surface and structural deformation,etc.
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