Digital Twins and Artificial Intelligence for Smart Infrastructure: Applications, Challenges, and Future Directions
TL;DR
This study critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields, and highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation.
Abstract
The swift evolution of technologies like artificial intelligence (AI), the Internet of Things (IoT), Building Information Modeling (BIM), cloud computing, and sensor technology has dramatically reshaped how society manages modern civil infrastructure. Among these innovations, the Digital Twin (DT) has emerged as a standout technology: a dynamic virtual representation of physical infrastructure that continuously exchanges real-time data with its physical counterpart. By merging AI with DT, infrastructure systems can engage in smart monitoring, predictive maintenance, fault diagnosis, performance optimization, and even autonomous decision-making throughout their lifecycle. This powerful combination boosts operational efficiency, cuts down maintenance costs, enhances safety, and promotes sustainable infrastructure development. This study reviews the integration of DT technology with AI for smart infrastructure applications. It explores the core concepts, enabling technologies, architecture, and lifecycle of AI-driven DT, while showcasing their applications across buildings, bridges, transportation networks, highways, railways, water distribution systems, and smart cities. Additionally, it discusses the latest advancements in machine learning, deep learning, computer vision, generative AI, large language models (LLMs), and physics-informed AI that elevate the capabilities of DT for infrastructure monitoring and management. It also critically analyzes existing literature to pinpoint current achievements, implementation strategies, and technological progress across various civil engineering fields. The review highlights several key research challenges, such as data interoperability, cybersecurity, computational complexity, scalability, standardization, data quality, and model validation. It also explores exciting future research opportunities in areas like explainable AI, autonomous DT, edge computing, 6G communication, quantum computing, and sustainable infrastructure management.