To address the challenge of response prediction for transmission towers under structural parameter uncertainty and incomplete monitoring information

a graph neural network (GNN)-based deep learning model is proposed

achieving structural mechanics response prediction under incomplete information conditions. By integrating network architecture optimization with a random label masking algorithm

the impact of data missingness has been effectively mitigated. To adapt to engineering scenarios with incomplete information

a two-stage (training-inference) validation framework is designed: during training

data intrinsic correlations are learned through simulated information missingness

while during inference

unknown responses can be predicted through knowledge transfer. Ablation experiments are conducted to compare the effects of different improvement modules on performance of model. Furthermore

the prediction performance of RPTTGNN-II (response prediction of transmission towers based on GNN under incomplete information)under varying proportions of unknown response parameters is investigated

validating its accuracy and effectiveness in practical engineering applications. Research results demonstrate that the model exhibits significant advantages in handling displacement prediction tasks

maintaining over 90% accuracy when the proportion of unknown displacement parameters is as high as 50%. For axial force prediction tasks

although the prediction accuracy performance of model is constrained

the model still provides valuable references under certain information missing conditions.