Operational digital twins in the built environment: A systematic review of semantic infrastructure and deployment practice
Digital twins (DTs) are increasingly positioned as integration infrastructure for building analytics, automation, and emerging AI-enabled workflows. However, evidence remains limited regarding whether documented deployments provide the information foundations required for scalable adoption across diverse buildings and facility portfolios. This study presents a systematic review of operational digital twins in the built environment, examining semantic integration, operational connectivity, and implications for portable analytics and agent-enabled interaction. Following PRISMA guidelines, records were collected from Scopus, Web of Science, IEEE Xplore, and the ASCE Library. A total of 2,839 records were retrieved, of which 46 studies met the eligibility criteria for full analysis. Indoor environmental quality and energy were the most prevalent application areas, accounting for 34 of the 46 studies, with much of the literature addressing HVAC and building automation. Formal semantic representations were documented in roughly half of the reviewed DTs, but fewer than a third used semantic constructs to shape analytic or decision processes more directly. DTs with stronger semantic integration more often support diagnostic outputs requiring contextual interpretation across equipment and spatial relationships, whereas actions extending to direct control or automated system responses are more commonly realized through fixed, site-specific configurations. Operational outputs also remain largely confined to the platform's own interface, with limited evidence of delivery into maintenance systems, control sequences, or other governed external processes. The findings describe a field that has demonstrated meaningful operational capabilities but has not yet assembled them into coherent architectures that transfer across sites. The review indicates that machine-readable building context and lifecycle-governed metadata remain important constraints on portability, workflow integration, and the structured access needed for emerging agent-enabled interaction. Semantic infrastructure is accordingly framed as a lifecycle information requirement, one that should be established early in the building lifecycle and maintained as buildings evolve, to support durable, scalable, and AI-ready building operations.