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Product, Service & Design Innovation
Digital Twins:
A Data-Driven Approach to Sustainable Building Operations

Digital twins are now beginning to be recognized for their ability to monitor a building’s performance in real time. Companies' building projects should include a model that is prepared for this new, data-driven approach to sustainability.

A common vision for the post-pandemic future is the connection of the real world and digital world. Within the built environment, this concept is embodied by the “digital twin” — a 3D, virtual replica of an operational building that stores and displays both static and dynamic data. In this way, digital twins fulfill a myriad array of new value propositions for a building’s owner.

The digital twin originates from building information modeling (BIM), which is the common technology in today’s design and construction industry. BIM not only unlocked digital 3D design as the new paradigm, it also enabled rich data and analytics tools to be applied against the design. Chief among these are building sustainability and performance, building operations and maintenance, and occupant health and wellness. Simply put, digital twins are the activation of these metrics and analytics through BIM in the building operations phase.

Static and dynamic data types are at the heart of a digital twin. Static data reflects building assets that are fixed in place. Commonly, this data includes room names, room areas, fixed equipment, furnishings, finishes, etc. Metadata can also be incorporated into any item to record asset provenance, warranty information, operation manuals, maintenance schedules, etc.

Dynamic data, on the other hand, supplements static data and records information from building components that are ever-changing and increasingly driven by embedded building sensor technologies. Also known as the “Internet of Things” or IoT, sensors are a fast-emerging market and are just beginning to demonstrate their potential impact on buildings. At the simplest level, sensors record things such as room temperatures, HVAC fan speeds, door entry access, water leaks, indoor and outdoor air quality, etc. When IoT sensors are coupled with simple algorithms or even artificial intelligence, they gain the potential to do extraordinary things. Examples include counting room occupants and monitoring their engagement with the speaker, inspecting the health of people queuing on an airport jetway, monitoring environmental health and airborne or waste-stream-borne pathogens, predicting maintenance needs, and much more.

Value propositions for a digital twin

Performance analytics platform

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    At its most basic level, a digital twin serves as a live dashboard providing real-time data on the utilization of building assets. Through a user interface, building operators can query any object represented in the digital model. If the object has sensors, the live data is displayed in real time (historical data can be examined, as well).

  • Deeper uses include applying data analytics for predictive purposes — such as maintenance needs, maintenance schedules, pre-ordering of parts, and elimination of waste in labor and materials. These predictive capabilities decrease interruption, expense and risk to building operations.

Data warehouse for a material passport

  • A material passport is the equivalent of a food nutrient certificate for building materials. It documents all the materials used in a product or its construction. Since a digital twin is — by all intents and purposes — a database, this is the perfect platform to collect provenance data from the original material supply chain and associate the records with their virtual equivalent. This enables building operators to plan for maintenance and obsolescence; or to transfer material data to an exchange; or a new material passport, when the asset is removed from the property. If we reimagine buildings as material banks, this type of data warehouse would serve the emerging market of material exchanges that trade the value of recovered materials for reuse after upcycling or recycling.

Planning tool

  • As a virtual simulacrum of a built property, a digital twin can be used by architects to explore remodeling scenarios and their potential performance under different lenses — such as energy use intensity, material reuse, construction cost and re-fit schedule. In advanced applications, new sensors can be tested virtually before being deployed in real-world conditions.

Occupant metrics and feedback

  • By utilizing advanced sensors and incorporating third-party software applications that measure or solicit occupant feedback, a building owner may gain valuable information regarding how a building is experienced. This collated data may lead to changes in environmental conditions, reconfiguration of amenities or wholesale remodeling of spaces, or mediating distasteful elements such as smells or finishes. Other environmental factors such as air quality or temperature; or lighting can be monitored virtually and adjusted for optimum human performance, health and wellness in real time.

Proof of performance database

  • Ultimately, a digital twin is a database to be used by the real estate industry, where buildings are increasingly valued according to their real and potential performance. Through the data embodied by a digital twin, a building owner can provide proof-of-performance when a building goes to market. Additionally, cities and other jurisdictions are starting to require periodic recommissioning of buildings to certify that performance remains within the original design specifications or within a codified standard. With a digital twin, embedded sensor data could potentially measure building performance in real time and provide certification-level data on a continual basis, thereby bypassing the need to engage in the intrusive process of commissioning.

Digital twins are just now beginning to be recognized for their potential to inform users of a building’s performance in real time. For those companies with building projects on the horizon, now is the time to expand your deliverables to include a building model that is prepared for this new, data-driven approach to sustainability.

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