NEURO-SYMBOLIC TWIN NETWORKS FOR AUTONOMOUS INFRASTRUCTURE HEALTH MONITORING AND FAILURE FORECASTING INTEGRATING EXPLAINABLE AI WITH PHYSICS-GUIDED GRAPH NEURAL MODELS FOR REAL-TIME DECISION-MAKING IN SMART CIVIL STRUCTURES

The integration of Neuro-Symbolic Twin Networks with physics-guided Graph Neural Networks (GNNs) offers a transformative approach to real-time infrastructure health monitoring and failure forecasting in smart civil structures. This research investigates how hybrid AI models leveraging symbolic reasoning and physical constraints can enhance decision-making accuracy and transparency. The paper emphasizes explainable artificial intelligence (XAI) integration for interpretability, making these systems viable for safety-critical applications like bridges, buildings, and dams. By embedding domain knowledge into learning architectures, the proposed framework achieves higher forecasting accuracy, interpretable insights, and adaptive responses to structural anomalies.