Figures (7)  Tables (2)

    • Figure 1. 

      The framework of the deformation prediction method.

    • Figure 2. 

      Structure of the GAT-GRU network.

    • Figure 3. 

      Location and layout of the deformation monitoring system.

    • Figure 4. 

      Monitoring data of different factors. (a) Stress, (b) temperature, (c) strain, and (d) deformation.

    • Figure 5. 

      Comparison of the accuracy of different models at different monitoring locations. (a) MAE, and (b) RMSE.

    • Figure 6. 

      Comparison of prediction results for each model. (a) MAE at each node, (b) typical node (node 1).

    • Figure 7. 

      The predictive capability of the model under different conditions. (a) Time step, and (b) prediction scale.

    • Parameter Range Selected value
      hidden_feature (GAT) 6, 12, 24 6
      num_head (GAT) 2, 4, 6, 8 2
      hidden_size (GRU) 64, 128, 256 64
      num_layer (GRU) 1, 2, 3 2
      learning_rate 0.001, 0.01, 0.1 0.01
      batch_size 16, 32, 64 64
      num_epoch 50, 100, 150, 200 150

      Table 1. 

      Selection of parameters.

    • Model MAE (mm) RMSE (mm)
      GRU 0.115 0.14
      TCN 0.122 0.149
      ChebNet 0.115 0.14
      GCN 0.12 0.146
      GAT 0.093 0.126
      GAT-TCN 0.074 0.099
      GAT-GRU 0.042 0.059

      Table 2. 

      Comparison of model accuracy.