BIM-Based Digital Twin and Extended Reality for Electrical Maintenance in Smart Buildings: A Structured Review with Implementation Evidence

The current literature on electrical system maintenance highlights three technology do-mains—building information modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle in-formation management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system mainte-nance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their inte-gration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in elec-trical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facil-ity and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; vali-dation approaches focused on syntactic rather than dynamic model fidelity; and the sep-aration of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limita-tions, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective ap-plication depends on advances in semantic alignment, lifecycle data governance, valida-tion of dynamic models, and scalable integration frameworks, enabling the transition to-ward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance.