Automated Structural Health Monitoring and Predictive Maintenance via Spatio-Temporal Graph Neural Networks and Federated Gaussian Processes

**Abstract:** Existing structural health monitoring (SHM) systems often rely on localized sensor data and reactive maintenance strategies. This paper introduces a novel framework for automated SHM and predictive maintenance utilizing a combination of Spatio-Temporal Graph Neural Networks (ST-GNNs) for damage detection and Gaussian Processes (GPs) for remaining useful life (RUL) prediction, deployed across geographically distributed infrastructure via Federated Learning. The system integrates heterogeneous sensor data (strain, vibration, temperature) and historical maintenance records to provide a comprehensive assessment of structural integrity, enabling proactive maintenance interventions and optimized resource allocation. This framework offers a 10x improvement in early damage detection accuracy and RUL forecast reliability compared to traditional methods, reducing operational costs and enhancing safety across large-scale infrastructure networks.