“Multimodal Explainable AI Framework for Real-Time Structural Health Monitoring and Predictive Maintenance in Critical Infrastructure” Abstract: This research introduces an AI-driven framework for early detection and prediction of structural failures

Abstract: This research introduces an AI-driven framework for early detection and prediction of structural failures in critical infrastructure such as buildings and bridges. Leveraging multimodal data fusion from vibration sensors, thermal imaging, and high-resolution visual inputs, the system offers real-time anomaly detection with actionable insights. Explainable AI (XAI) methodologies ensure transparent decision-making through visual heatmaps and interpretable outputs. Edge-based deployment enables seamless monitoring in low-connectivity environments. The study emphasizes predictive maintenance, providing actionable recommendations for repairs, enhancing safety, and minimizing catastrophic risks while ensuring cost-effectiveness. Key Contributions: Multimodal Data Fusion: Integration of visual, thermal, and vibration data for comprehensive structural analysis. Enhanced robustness through attention mechanisms for feature weighting. Real-Time Detection and Explainability: Anomaly detection with XAI-based insights highlighting high-risk regions using visual heatmaps. Integration of interpretable models like LIME or SHAP for real-time explanations. Predictive Maintenance: Forecasting degradation timelines to prioritize repairs, reducing long-term costs and risks. Scheduling maintenance actions based on severity levels. Edge Computing Deployment: Lightweight AI models optimized for resource-constrained environments. Ensures reliability in remote areas with minimal connectivity. Significance: This framework addresses the limitations of existing systems by combining multimodal data analysis with XAI for greater reliability and trust. The edge-based solution ensures scalability and accessibility in rural or underdeveloped regions, offering a transformative approach to infrastructure safety.