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

TL;DR

This paper presents a comprehensive, scalable framework for deploying digital twins in smart campus buildings, integrating laser scanning, BIM, and IoT data to improve facility management and maintenance workflows.

Contribution

It introduces an integrated workflow combining 3D laser scanning, BIM, and IoT data for scalable digital twin deployment in campus buildings, demonstrated through a case study.

Findings

Enabled centralized asset documentation and visualization.

Improved maintenance workflows and system visibility.

Validated the feasibility of a scalable digital twin prototype.

Abstract

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.