A Hybrid-Driven Method Based on Dynamic and Static Responses for Dynamic Girder Strain Reconstruction of Long-Span Suspension Bridges

In structural health monitoring (SHM) of long-span bridges, distributed optical fiber sensing technology can provide strain information with high spatial resolution. However, due to the inherently low sampling frequency of the system, it is difficult to capture the high-frequency dynamic strain responses of bridges under complex operational environments. To address this hardware bottleneck, a hybrid-driven method based on dynamic and static responses for dynamic girder strain reconstruction of long-span suspension bridges is proposed. First, a joint state estimation framework based on variational mode decomposition (VMD) and a Kalman filter is established. This framework utilizes high-frequency acceleration as a dynamic prior while treating low-frequency deflection measurements as absolute constraints, effectively resolving the persistent issue of low-frequency drift caused by uncertain initial conditions in double integration. Second, a multi-task BP neural network is introduced to decode the complex, non-linear spatiotemporal mapping between global girder deflection and localized strain fields, thereby mapping the reconstructed dynamic deflection field to high-frequency dynamic strains at multiple cross-sections of the entire bridge.. The proposed method is validated using numerical simulation and actual monitoring data of a long-span suspension bridge. Results demonstrate that compared to conventional identification methods relying solely on acceleration integration, the proposed approach reduces the maximum root-mean-square error (RMSE) and mean absolute error (MAE) of the reconstructed dynamic strains by up to 50% and 51%, respectively. Furthermore, under Gaussian white noise interference as high as 15%, the method still maintains highly consistent strain reconstruction trends, demonstrating excellent noise robustness.