Mapping digital twin applications in infrastructure and the built environment across research types, methods, sectors, phases, and scales

Digital Twin technologies are increasingly used in infrastructure and the built environment to create dynamic, data-driven models of physical assets and processes. This review analyses recent advancements across sectors such as tunnels, bridges, roads, buildings, construction management, and urban planning, covering all life-cycle phases from design to operation. Integrating Digital Twins with Building Information Modelling, Internet of Things sensors, and Artificial Intelligence enhances real-time monitoring, decision-making, and asset performance. Key methods include monitoring, modelling, and simulation, which improve resource use and proactive maintenance. However, adoption faces challenges such as poor data interoperability, high costs, and technical complexity in merging multiple technologies. Ethical and governance issues around data privacy and security also persist. The review identifies future research needs in improving interoperability, expanding predictive analytics, and assessing large-scale impacts. It highlights Digital Twins' potential to improve resilience, efficiency, and sustainability, stressing the need for policy support and stakeholder collaboration. • Digital Twins integrate BIM, IoT, and AI for real-time asset monitoring and control. • Most DT research focuses on operation and maintenance with predictive capabilities. • Buildings, bridges, and tunnels lead DT adoption; city-scale twins remain emerging. • Major challenges include data interoperability, high costs, and complexity. • Future work needs scalable frameworks, standardization, and cross-phase data.