Explainable Digital Twin Decision Intelligence Framework for Predictive Risk Assessment and Maintenance Optimization of Critical Infrastructure Systems

Critical infrastructure systems generate massive volumes of operational, structural, and environmental data that are increasingly used to support maintenance and operational decision-making. However, existing predictive maintenance models often function as black-box systems, limiting transparency, stakeholder trust, and regulatory adoption in infrastructure management. This research proposes an explainable digital twin decision intelligence framework that integrates real-time infrastructure representations with machine learning-based risk prediction and interpretable decision support models. The framework combines digital twin synchronization, infrastructure health assessment, uncertainty-aware predictive analytics, explainable machine learning, and multi-objective maintenance optimization to support asset prioritization under operational constraints. Decision explanations are generated using feature attribution techniques and rule-based reasoning, allowing infrastructure operators to understand the rationale behind maintenance recommendations while balancing cost, safety, reliability, and service continuity. The framework provides interpretable infrastructure risk assessment and maintenance prioritization for safety-critical cyber-physical systems operating under uncertain conditions.