Research on a Hybrid Prediction and Early Warning Model for Concrete Bridge Settlement Based on VMD–TCN–BiLSTM

Bridge settlement prediction is essential for understanding structural deformation evolution and supporting bridge condition assessment. However, settlement monitoring sequences usually exhibit nonlinear characteristics, non-stationarity, and multi-scale fluctuations, which increase the difficulty of accurately capturing deformation evolution trends. This study develops an integrated settlement prediction and dynamic early warning framework for concrete bridges by combining Variational Mode Decomposition (VMD), Temporal Convolutional Network (TCN), and Bidirectional Long Short-Term Memory Network (BiLSTM). In the proposed framework, VMD is employed to decompose settlement monitoring sequences into multiple components with different frequency characteristics, TCN is used to extract local temporal variation features, and BiLSTM is applied to capture long-term temporal dependencies for settlement prediction. Based on the predicted settlement responses, settlement increment, settlement rate, and differential settlement indicators are further incorporated to establish a dynamic early warning method for deformation risk identification. Continuous monitoring data from an in-service reinforced concrete bridge were used for validation. The results show that the proposed framework achieves better prediction performance than benchmark models, with an R2 of 0.956, MAE of 0.38 mm, RMSE of 0.51 mm, and MAPE of 1.52%. Furthermore, simulated abnormal settlement scenarios were constructed to evaluate the response capability of the warning framework under different deformation conditions. The results demonstrate that the proposed framework can effectively integrate settlement prediction and deformation risk assessment, providing a potential approach for bridge settlement monitoring and early warning analysis.