Dynamic Monitoring of Bridge Structures via an Integrated Cloud and Edge Computing System

Traditional bridge monitoring techniques, which predominantly rely on centralized data processing, often exhibit slow and inflexible responses when managing large-scale sensor network data. This study proposes an integrated edge and cloud computing approach to enhance the response time and data processing efficiency of dynamic bridge structure monitoring systems, thereby improving bridge safety and reliability. The proposed monitoring system leverages both edge and cloud computing, incorporating modules such as sensor data management, structural assessment and warning, data processing, monitoring, and data acquisition and transmission. High-performance and cost-effective sensors are utilized to monitor the real-time dynamic responses of the bridge, including displacement, acceleration, tilt, and stress, as well as external loads and environmental effects. The data processing module employs the modal superposition method, frequency response function, and modal analysis for dynamic analysis, while the cloud computing platform facilitates deep learning analysis and long-term data storage. A real case study demonstrates the system's performance across various settings and operational conditions, highlighting the effectiveness of integrating edge and cloud computing. The results indicate that the integration scheme significantly enhances monitoring accuracy, system stability, real-time response capacity, and data processing efficiency.