Bridge Dynamic Strain Prediction Based on Stacked GRU Neural Network

As important infrastructures, bridges may face considerable safety hazards due to the long-term influence of the natural environment and daily loads. Therefore, the health status of bridge structures must be monitored and predicted in real time. In existing studies, issues such as easy errors in prediction errors, poor stability, and lack of real-time performance monitoring have been identified that inhibit the the prediction of health status of complex bridge structures. To resolve the aforementioned issues, this study proposes a Stacked Gated Recurrent Unit (GRU) with Attention and Auto-Cycle (SGRUA) model based on a stacked GRU encoder-decoder. It improves the accuracy and stability of prediction by better capturing long-term dependencies and important features in time-series data and uses a smaller number of parameters to increase prediction speed, making predictions in real time. First, missing values are filled, and outliers are detected and processed for actual bridge monitoring data to ensure that the data meet the integrity and availability requirements for time-series prediction. Subsequently, the SGRUA model is used to predict the bridge dynamic strain index in the time-series, and the effectiveness of the model is verified through comparative tests and ablation experiments. The experimental results show that, compared with the TSMixer time-series prediction model, the SGRUA reduces the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE) indicators by 11.07%, 11.02%, 11.00%, and 10.96% on the Bridge B dataset. The SGRUA provides a new and effective method for bridge structure health monitoring and prediction. Additionally, it provides useful solutions for health monitoring problems of other similar structures.