Traditional IoT-based health monitoring systems for cable-stayed bridges facilitate the transmission and integration of sensor data, but they often lack direct interaction with finite element (FE) mechanical models. Typically, sensor data is indirectly used for modal updating or manually inputted to adjust FE models, limiting the efficiency of real-time structural assessments. In this study, we propose an innovative framework that uses streaming computation to directly integrate real-time sensor data with FE models, enabling continuous, automated updates of the mechanical model. This reduces manual intervention and allows near real-time reflection of structural condition changes in the digital twin. Although minor latency arises from data transmission and FE model updating, the system remains effective for lifecycle health monitoring. Using Unity 3D, we visualize the mechanical model in real-time, creating a unified platform for sensor data, FE analysis and model updating, and digital twin visualization. This approach enhances the precision and responsiveness of bridge monitoring by connecting the sensor data and physical model of the bridge in the digital twin system. It sets a new standard for lifecycle management in civil engineering.