Digital Twins for Infrastructure
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I2P112Keywords:
Digital Twin, Infrastructure Management, Artificial Intelligence, Predictive Maintenance, Cyber-Physical SystemsAbstract
The adoption of digital twin (DT) technologies in infrastructure systems is rapidly transforming how built assets are designed, monitored, and maintained. A digital twin is a dynamic virtual representation of a physical asset or system that integrates real-time data, simulation, and predictive analytics to support decision-making (Wang et al., 2023). In the context of infrastructure including transportation networks, utilities, and civil assets these technologies offer significant potential to enhance resilience, optimise lifecycle performance, and enable proactive maintenance. However, the integration of artificial intelligence (AI) and Internet of Things (IoT) with infrastructure digital twins remains an evolving research frontier, with persistent challenges around data interoperability, cybersecurity, and scalable deployment (Attaran, 2023; Qiu et al., 2023). This paper presents a conceptual framework for AI‐driven digital twins in infrastructure management, grounded in current literature and supported by case-study analysis. It examines how advanced analytics, sensor networks, and simulation models converge to form a closed-loop infrastructure digital twin workflow, spanning design, operation, and decommissioning phases. The findings suggest that infrastructure owners and practitioners can achieve improved performance metrics such as reduced downtime, lower maintenance costs, and enhanced situational awareness through DT-enabled systems. Nonetheless, significant barriers remain, including standardisation of data models, secure connectivity for large-scale asset networks, and the cultural shift required for operational adoption. The paper concludes by outlining research and implementation pathways that address these gaps and advance infrastructure digital twins toward smarter, more adaptive systems
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