Construction of Highway Bridge Health Monitoring and Vehicle-Mounted Intelligent Operation and Maintenance System Based on Edge Computing Joint Clustering Algorithm (International Journal of Communication Systems and Network Technologies, 2025)
To address the bottlenecks of hidden and sudden structural damage to highway bridges under long-term heavy traffic, the inability of traditional central cloud monitoring to process massive amounts of vehicle-mounted mobile data in real time, and the difficulty in multi-source heterogeneous data fusion, this paper constructs a health monitoring and vehicle-mounted intelligent operation and maintenance system driven by edge computing and joint clustering. First, a three-layer collaborative architecture of "vehicle-roadside-edge cloud" is designed. Then, a distributed multi-view joint clustering algorithm is proposed, simultaneously optimizing bidirectional clustering of sample and sensor features, and exchanging centroids only through federated averaging between edge nodes. Finally, a vehicle-mounted terminal is developed to map damage level and location in real time to graded early warning and maintenance suggestions. Experiments show that the anomaly detection accuracy is 94.7%, alarm latency is 87ms, transmission volume is 0.41MB, a reduction rate of over 99.6%, and strong noise robustness. The integration of edge computing and joint clustering solves the problems of real-time performance and data silos, achieving low-latency and high-precision vehicle-mounted structural health operation and maintenance.
Keywords: Structural Health Monitoring; Edge Computing; Joint Clustering; Damage Identification; Vehicle-Mounted Maintenance