A Practical Perspective on Digital Twin for Smarter Building Maintenance

Buildings today operate in a landscape where performance expectations are rising while operational budgets remain under pressure. With maintenance accounting for approximately 65% of annual facility management expenses, there is a clear need for approaches that reduce inefficiencies and support timely decisions. At the same time, buildings generate large volumes of technical, operational, and environmental data but much of this information remains underused because it is distributed across sources that rarely connect effectively.

Digital Twin technology offers a structured way to unify these data streams and provide an informed picture of how a building behaves over time. Rather than relying solely on routine schedules or traditional inspections, teams can base decisions on dynamic information drawn directly from the building.

Why Traditional Approaches Fall Behind

One of the biggest limitations in building maintenance is the lack of consistent, consolidated visibility. Sensors record environmental conditions, BIM files store design intent, and maintenance logs capture past actions, yet these inputs often operate independently. This makes it difficult to detect early deviations in performance, anticipate failures, or understand how different components influence one another. As the document highlights, achieving intelligent building management remains challenging when key information is dispersed and system integrity is hard to maintain.

Another practical barrier is the mismatch between as designed and as built conditions. Buildings undergo changes during construction and later modifications, yet digital models frequently remain static. The emphasises is on the importance of accurate as built data to support ongoing maintenance and realistic performance assessments.

What a Digital Twin Adds

A Digital Twin consolidates IoT sensor data, BIM models, historical records, and analytical algorithms into a unified virtual environment. This environment mirrors the physical asset and updates continuously based on real time information through bidirectional data exchange. Its value stems from three core capabilities:

Real time insight
Operational metrics such as temperature, humidity, occupancy, vibration, and energy use form a dynamic snapshot of the asset’s current state. This allows facility teams to see developing issues long before they affect performance.
Intelligent interpretation
Analytical models, including machine learning and hybrid techniques, identify patterns, detect anomalies, and estimate future behaviour. By combining algorithmic outputs with engineering context, the system supports reliable and timely interventions.
Visual understanding
Digital representations ranging from as built BIM to simulation environments and dashboards enable users to interpret complex information quickly and make decisions with greater clarity.

Addressing the Challenges

Several practical steps help establish a functioning Digital Twin environment.

Establishing a unified data foundation
Effective decision making requires a combination of operational data, attribute data, and evaluation data. Attribute information defines the physical characteristics of building components, while evaluation data such as maintenance history and stakeholder feedback helps contextualise performance.
Using interoperability standards
Formats such as IFC, COBie, and JSON allow information from BIM models, sensor systems, and facility management tools to follow consistent structures. This prevents data loss, reduces ambiguity, and simplifies integration.
Applying analytical models strategically
Machine learning can interpret sensor patterns to predict failures, assess comfort, and optimise performance. Hybrid approaches combine engineering logic with data driven insights for cases where behaviour cannot be captured by data alone.
Strengthening visualisation platforms
Digital environments built from as designed BIM, as built models, simulations, and real time dashboards create intuitive interfaces for monitoring and analysis. Immersive tools such as AR or VR further enhance understanding when spatial detail matters.
Leveraging cloud and edge infrastructure
Cloud supported systems manage large data volumes, perform processing at scale, and enable fast distribution of insights. Edge processing helps filter or refine data before transmission, improving efficiency and responsiveness.

The Value Delivered

Bringing these elements together offers clear advantages for building owners and operators:

Earlier detection of performance issues
Reduced reliance on routine interventions
Better comfort and safety through continuous monitoring
More accurate energy insight and optimisation
Longer lifespan of key assets through targeted maintenance

By consolidating information, interpreting it intelligently, and presenting it clearly, Digital Twin systems support decisions that translate into operational savings and improved building performance.

Closing Thoughts

Digital Twin creates a practical foundation for more efficient and informed building maintenance. By connecting data sources, enabling analytical interpretation, and providing clear digital environments for monitoring, they help teams act decisively rather than reactively. When buildings are understood through accurate data and adaptive models, maintenance becomes more precise, predictable, and aligned with long term value.

Reference: https://doi.org/10.1016/j.buildenv.2025.113997

A Practical Perspective on Digital Twin for Smarter Building Maintenance

Why Traditional Approaches Fall Behind

What a Digital Twin Adds

Addressing the Challenges

The Value Delivered

Closing Thoughts

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