Dynamic Deflection Monitoring of Wuhan Metro Viaduct

Under dynamic loads such as vehicle traffic and train operation, bridges produce vertical displacement, dynamic deflection and vibration responses. Especially for rail transit viaducts, where trains run at high frequency and loads act in obvious cycles, changes in bridge structural responses need to be recorded continuously and accurately. Therefore, bridge dynamic deflection monitoring focuses not only on individual readings but also on the dynamic variation process of the structure under real operating conditions.

In the Wuhan Metro viaduct dynamic deflection monitoring project, Haytham online visual trackeradopted a non-contact visual measurement method to track key measurement points of the bridge in real time. By continuously collecting images of passive targets, the system identifies position changes of the measurement points and obtains vertical displacement, dynamic deflection and vibration response data during train passages, providing data support for bridge operation state analysis, structural trend judgment and subsequent maintenance management.

To address factors such as on-site vibration, station attitude changes and long-term operating environment variations, the system combines deep learning recognition models with attitude positioning algorithms to automatically identify multiple targets and their angular attitudes, supporting initial calibration and periodic self-calibration. Meanwhile, attitude compensation and baseline correction reduce the influence of station disturbance on measurement results and improve the consistency of long-term monitoring data.

As a non-contact bridge displacement monitoring solution, the online visual tracker requires no complex wiring and is applicable to long-term online monitoring scenarios such as rail transit viaducts, urban bridges and overpass bridges, providing a more flexible way to acquire data for bridge dynamic deflection monitoring and health monitoring.