AI-Augmented Digital Twin Architecture for Predictive Maintenance in Smart Urban Infrastructure: A Cross-Domain Engineering Framework

Abstract

As our cities continue to transform into sophisticated cyber-physical systems, there is an escalating requirement for intelligent, preemptive infrastructure management. To facilitate predictive maintenance of the smart urban infrastructure systems, we present an AI-empowered DT architecture in this review. Through integrating theories and techniques from electronic engineering, civil engineering and intelligent computing, the framework links physical infrastructure with digital intelligence. The use of artificial intelligence in DT systems enables urban actors to predict breakdowns, to optimize asset performance, and to automatically decide ‘on-the-fly’ for maintenance purposes. Through use cases in smart transportation, water distribution networks, and public building management, the work reveals how a sensor network, embedded electronics, structural modelling and AI algorithms integrate in a single system. Further, the model is designed to overcome cross agency issues like data heterogeneity, semantic interoperability and computational scalability. It not only improves system resilience but also contributes to urban sustainability goals. The review is completed by distinction of emerging research areas, including edge-AI implementation, real-time anomaly detection and ethical data management in urban scenario. The results serve as a guide for researches and city planners for deploying intelligent maintenance solutions at a larger scale with low cost, low downime and low environmental impact.