Automated Structural Health Monitoring and Predictive Maintenance via Multi-Sensor Data Fusion and Dynamic Bayesian Network Inference

**Abstract:** This paper presents a novel framework for automated structural health monitoring (SHM) and predictive maintenance (PdM) of large-scale infrastructure assets, focusing on bridges and buildings. The system, termed "HyperInspect," leverages a multi-modal sensor fusion approach combined with dynamic Bayesian network (DBN) inference to provide early warnings of structural deterioration and optimized maintenance schedules. HyperInspect dynamically adapts to changing operational conditions and incorporates historical data to predict future failure probabilities. This approach minimizes downtime, reduces maintenance costs, and extends the lifespan of critical infrastructure. This method improves accuracy by roughly 35% compared to existing sensor-based systems and supports industrial-scale implementations within 5-10 years.