AI-Driven Digital Twins in Industrialized Offsite Construction: A Systematic Review
Mohammadreza Najafzadeh, Armin Yeganeh
Abstract
The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises.