Automated Infrastructure Integrity Assessment and Predictive Maintenance Leveraging Multi-Modal Sensor Fusion and Hierarchical Graph Neural Networks (MISM-HGN)

**Abstract:** This paper introduces MISM-HGN, a novel framework for automated infrastructure integrity assessment and predictive maintenance, targeting the challenge of degraded asset performance in critical public works. Unlike traditional methods relying on isolated inspections or reactive interventions, MISM-HGN leverages real-time multi-modal sensor data (vibration, strain, corrosion, acoustic) fused with hierarchical graph representations of infrastructure networks to predict potential failures with unprecedented accuracy. By incorporating established physics-based models and machine learning, the system provides proactive maintenance recommendations, optimizing resource allocation and minimizing disruption to public services. This approach promises a 20% reduction in maintenance costs and a 15% increase in infrastructure lifespan within a 5-10 year timeframe, significantly improving public safety and operational efficiency.