A Framework for Scalable Digital Twin Deployment in Smart Campus Building Facility Management

Digital twin (DT) offers significant opportunities for enhancing facility management (FM) in campus environments. However, existing research often focuses narrowly on isolated domains, such as point-cloud geometry or energy analytics, without providing a scalable and interoperable workflow that integrates building geometry, equipment metadata, and operational data into a unified FM platform. This study proposes a comprehensive framework for scalable digital-twin deployment in smart campus buildings by integrating 3D laser scanning, BIM modeling, and IoT-enabled data visualization to support facility operations and maintenance. The methodology includes: (1) reality capture using terrestrial laser scanning and structured point-cloud processing; (2) development of an enriched BIM model incorporating architectural, mechanical, electrical, plumbing, conveying, and sensor systems; and (3) creation of a digital-twin environment that links equipment metadata, maintenance policies, and simulated IoT data within a digital-twin management platform. A case study of the Price Gilbert Building at Georgia Tech demonstrates the implementation of this workflow. A total of 509 equipment items were modeled and embedded with OmniClass classifications into the digital twin. Ten interactive dashboards were developed to visualize system performance. Results show that the proposed framework enables centralized asset documentation, improved system visibility, and enhanced preventive and reactive maintenance workflows. Although most IoT data were simulated due to limited existing sensor infrastructure, the prototype validates the feasibility of a scalable digital twin for facility management and establishes a reference model for real-time monitoring, analytics integration, and future autonomous building operations.

💡 Research Summary

This paper presents a comprehensive framework for deploying scalable digital twins (DT) to enhance facility management (FM) in smart campus buildings. It addresses a key gap in existing research, which often focuses on isolated technological domains like point-cloud geometry or energy analytics, by proposing an integrated and interoperable workflow that unifies building geometry, equipment metadata, and operational data into a single FM platform.

The proposed methodology is structured in three main phases. First, Reality Capture: Terrestrial Laser Scanning (TLS) using equipment like the FARO Focus is employed to capture the as-built conditions of a building. The raw scan data is processed, registered, and cleaned using software like FARO Scene and Autodesk ReCap to generate an accurate, unified point cloud. Second, BIM Model Development: This point cloud is imported into BIM authoring software (Autodesk Revit) to create a detailed and accurate 3D architectural model. The model is then enriched by incorporating Mechanical, Electrical, Plumbing (MEP), conveying systems, and IoT sensor metadata, transforming it from a geometric model into an information-rich BIM model suitable for FM. Third, Digital Twin Development: The enriched BIM model is integrated into a DT management platform (e.g., Autodesk Tandem, Azure Digital Twins). Within this platform, building components are linked via unique identifiers (GUIDs) to real-time or simulated operational data streams from IoT sensors (e.g., temperature, humidity, CO2). Interactive dashboards are created to visualize system performance, enabling dynamic monitoring and analysis.

The framework is validated through a case study of the Price Gilbert Building at the Georgia Institute of Technology. The implementation involved developing a detailed scanning plan, processing the data, and creating a Revit model containing 509 equipment items classified using the OmniClass standard. Within the DT environment, ten interactive dashboards were developed to provide visibility into critical building systems. The results demonstrate that the framework successfully enables centralized asset documentation, improves the visibility of building systems, and enhances both preventive and reactive maintenance workflows.

The paper acknowledges limitations, primarily that most IoT data used in the prototype was simulated due to a lack of extensive real-time sensor infrastructure in the existing building. However, the study successfully validates the feasibility of the end-to-end workflow for creating a scalable digital twin. It establishes a practical reference model that bridges the gap between as-built documentation, precise geometric modeling, and operational data, paving the way for future advancements in real-time monitoring, integration of advanced analytics, and ultimately, autonomous building operations. The research provides a valuable roadmap for the digital transformation of existing campus facilities, offering a tangible solution to the fragmented and reactive nature of traditional FM practices.