A review of building digital twins: Framework and enabling technologies

Digital twins, having gained prominence in industrial sectors, are emerging in construction for enhancing intelligent management through real-time monitoring, performance simulation, and data integration. Despite their potential, systematic analysis of frameworks and enabling technologies remains lacking. This review systematically investigates over 150 studies to: (1) analyze the general framework and its extensions for building digital twins, and (2) evaluate enabling technologies and tools based on the modeling procedure. We propose a general structure for building digital twins and analyze frameworks centered on four data types derived from different sources. Our analysis indicates that different types of frameworks exhibit distinct characteristics and inherent limitations, yet a standardized framework for integrating heterogeneous data is still lacking. Through systematic analysis of enabling technologies across four key aspects of the modeling procedure, we investigate building digital twin cases regarding their modeling procedures, technologies employed, and common issues. We identify four key challenges: (1) limited prediction data integration and data analysis, restricting frameworks to monitoring rather than decision-supporting; (2) multi-source data heterogeneity and poor tool interoperability; (3) complex and non-standardized data integration procedures; and (4) low automation in model development. Future research should focus on: (1) standardizing data formats and interoperability tools, (2) developing unified platforms for multi-source data integration, and (3) integrating predictive analytics to enhance decision-making. This study establishes connections between frameworks and enabling technologies, identifies existing problems, and provides actionable recommendations to accelerate the adoption of building digital twins.