Bridge Monitoring Data Recovery Based on Adaboost-Transformer Model

In bridge monitoring, data will be missing due to various uncertainties, and the missing data has a non-negligible impact on data analysis. To solve this problem, this paper constructs a data prediction method based on deep learning and A daboost algorithm. Taking Meixi River Bridge as the background, the missing deflection data are predicted. In order to improve the prediction accuracy of the deflection data, we established a combined prediction model based on Adaboost-Transformer. The experimental results show that the Adaboost-Transformer model has higher prediction accuracy and lower RMSE and MAE of 0.1136 and 0.0820, respectively, compared with the Transformer model and the Adaboost-LSTM model. This study demonstrates the effectiveness of the Adaboost-Transformer model in recovering the missing data and improve data availability.