Connected Data Environments for Digital Twins The first step towards industry data standards and the implementation of Connected Data Environments

What is a digital twin?

What is a Common Data Environment?

Digital twin maturity

Connected and structured data: a must-have for digital twins

Benefits of digital twins

Conclusion

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Table of contents

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What is a digital twin? A digital twin is a digital representation of something that exists in the physical world (be it a building, a factory, a power plant, or a city) and, in addition, is dynamically linked to the real thing through the use of sensors that collect real- time data. This dynamic link to the real thing differentiates digital twins from the digital models created by BIM software—enhancing those models with live operational data.

Since a digital twin is a dynamic digital reflection of its physical self, it possesses operational and behavioral awareness. This enables the digital twin to be used in countless ways, such as monitoring operations, diagnosing problems, simulating performance, and optimizing processes. As digital twins become commonplace, their historical operational data will guide decisions regarding facility upgrades and new projects.

A digital twin also represents a unified, shareable source of information. This digital unification begins during the initial design and continues throughout construction and/or manufacturing, delivery, handover, and whole-life operation. As such, Common Data Environments are essential for the creation and use of digital twins.

— such as a system, process or object (physical twin). A digital twin is more than just a model of a physical asset; it provides context (i.e. the relationship between the asset and its environment), connectivity between digital and physical assets (in at least one direction), and the ability to monitor the physical system in a timely manner.

Digital twins are digital representations of something physical or intangible “

Hetherington, J., & West, M. (2020) The pathway towards an Information Management Framework – A “Commons” for Digital Built Britain, www.doi.org/10.17863/CAM.52659

1 Please note that throughout this paper, the word “facility” is used as a blanket term for the physical, real version of the digital twin (a building, an infrastructure system, a power plant, and so on).

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What is a Common Data Environment? A Common Data Environment (CDE) describes project information management processes supported by a CDE solution that provides the technology to support those processes and collects, manages, and disseminates relevant asset data. A CDE acts as a digital hub where information is amassed and united throughout an asset’s lifecycle (from design and production to operation and closure).

Representing an “agreed source of information” (per ISO 19650-1 ), a CDE helps to ensure that information is constantly and systematically updated and enriched throughout a managed process.

The British standard BS1192:2007 first popularized the concept of a CDE for the collaborative production of architectural, engineering, and construction information. The CDE was a core concept of the UK’s BIM Level 2 mandate that came into force in 2016 and is now at the heart of the ISO 19650 series.

A CDE extends beyond BIM data and information to include anything from contracts, reports, and specifications to warranty and servicing data. It also features tracking workflows such as approvals, requests for information (RFIs), and change orders. Since their first use in the late 1990s, collaborative working solutions that support a CDE process have evolved from extranet file-based retrieval systems to today’s cloud-based Common Data Environments built upon secure data and functional platforms.

The value of a digital twin rests squarely on data: design data, manufacturer’s data, as-built/installed data, operational data, performance data, and so on. And, by extension, this includes the ability to assemble and share federated data conforming to standards (like ISO 19650) for the organization of structured project and ultimately asset information.

Thus, emerging digital twin efforts rely on industrial-grade Connected Data Environments that link project CDE outputs with other asset data sources, while conforming to advanced data management technology and standards.

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2 ISO 19650 is a series of international standards for information management using BIM on building and civil engineering projects.

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The British industry standard BS 1192 (Collaborative production of architectural, engineering and construction information – Code of practice) used the term Common Data Environment (CDE) to describe a centralized data source that allowed information to be shared in a managed process between members of a multidisciplinary project team.

First released in 2007, the standard’s definition of a CDE repository could have been many things, from a project extranet to an electronic document management system or simple digital repositories like today’s Dropbox or Google Drive technologies.

In current information standards like ISO 19650, requirements for Common Data Environments have evolved to include sophisticated data structures and schemas for project information.

Common Data Environment (CDE)

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Digital twin maturity Like many technology innovations, digital twins have been subject to industry hopes and dreams. Gartner (the American research and advisory firm) categorizes these into a cycle of longings: from potential technology breakthroughs that rise to a peak of expectations, fall to disillusionment, and then rebound and mature to productive technology.

Autonomous:

Predictive:

Informative

Descriptive:

But what is often missing from the discussion are the maturity levels of a technology like digital twins. Consider the variations of a self-driving car. Some of us already have cars with advanced systems such as cruise control and parking assistance. But those are a long way from the vision of a car that drives itself.

The figure below is a simple categorization of the progression of a digital twin as intelligence is added: beginning with a descriptive twin (basically a pre digital twin representing a facility’s “as-built” context) and developing into an autonomous twin (with the ability to learn and act on behalf of users).

Most industry efforts today surrounding digital twins are centered on developing the starting level: a descriptive twin. But attaining even this level requires organizational zeal for data standards and oversight. This relates back to the importance of structured data that conforms to industry-accepted data standards and the use of Connected Data Environments.

learns from various sources of data and surrounding environment for

autonomous decision making

provides predictive maintenance, analytics and insights

integrated with sensors, IT, and business software systems,

maintenance, and operations data

editable version of design and construction data representing a facility’s “as-built” context

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3 Please note that there is currently no industry consensus regarding the names and definitions of digital twin maturity levels, and those in use today often borrow from descriptions relating to data analytics and AI. See the Arup and Verdantix sources at the end of this paper for other, more sophisticated descriptions of digital twin maturity.

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Connected and structured data: a must-have for digital twins

The creation of a descriptive digital twin involves the aggregation of a lot of as-built data sources: 3D models from BIM processes, 2D construction documents, plans, and drawings (that remain the industry standard contract deliverables), geospatial records, as well as submittals, change orders, RFIs, schedules, and contracts.

Even today, project teams spend a lot of time putting data into files that isn’t useful to the owner; sometimes it is wrong, at other times too little, or in other cases an overload of unnecessary data. This handover of unstructured data leaves owner/operators with siloed data and systems, inaccurate information, and poor insight into the performance of a facility. Data standards such as ISO 19650 directly target this problem.

Industry is already embracing the advantages of digitalization during construction. The next steps are applying data standards and connected data environments to curate structured data for the development of a digital twin (see image below).

• Capture legacy assets • Leverage asset records • Define requirements

• Create digital models • Control data processes • Collaborate with common

data platforms

• Connect IoT data • Integrate other data • Develop and mature

digital twin

• Update and maintain data • Analyze data for insight • Communicate with data

Define the data

Connect the data

Create the dataUse the data

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Structured data requirements from the project developer are crucial for the development of a digital twin. Implementing a project CDE helps ensure that data and information is managed and flows easily between various teams and project phases, through to completion and handover. An integrated Connected Data Environment can subsequently leverage this approved project data alongside other asset information sources to deliver the foundation of a quality “descriptive” digital twin.

Progressing beyond a descriptive digital twin requires the inclusion of operational data. This involves the integration of data collected by embedded system sensors (such as IoT devices) and relies heavily on cloud-based data and custom systems integration efforts. The importance of structured data and adherence to information standards is paramount for success.

At the center of many of these emerging technologies is data. CIOs planning their IT investment strategies should consider leading a concerted data management and governance effort to ensure the success of digital strategies.

“ Alia Mendonsa, Senior Director Analyst, Gartner Comments relating to the 2019 addition of digital twins to the Gartner Hype Cycle for Digital Government Technology

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Benefits of digital twins Digital twins help owners and operators better understand, manage, and optimize their facilities. The real-world conditions and operational data embodied within the digital twin lead to better informed decision-making and better outcomes. Simulations of facility performance help improve predictability and reduce risk. Comparisons of current operations to historical data provide predictive insights into performance and safety.

The benefits of digital twins relate to the operation (and ultimately closure) of a facility. As it is well accepted that the cost of a facility’s operation far exceeds its construction cost, the cost benefits of digital twins are reaped by owners, operators, and (for government-funded facilities) the general public. Moreover, the benefits of digital twins can extend beyond the cost of the physical management of the facility. They can lead to safer and healthier working environments, improved environmental footprints, more efficient public services, and better informed policy decisions.

Those considering an investment in digital twins should start by determining the benefits they might derive. For instance: a facility owner that has challenges with managing their built portfolio might identify a legacy of poor asset data management as well as reactive operational maintenance.

Moving to a digital twin environment can increase understanding of the as-is while predicting and avoiding failures through simulation. Similarly, a utility owner that understands their network assets but invests significant sums in operating the asset could use a digital twin to inform their decision-making regarding the expansion of system automation. Thus, reducing ongoing costs, improving performance and raising customer satisfaction.

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Conclusion The promises of digital twins are certainly alluring. Data- rich digital twins have the potential to transform asset management and operations, and give owners new insights to inform their decision-making and planning.

And although digital twin technologies and industry adoption are still emerging, it is clear that the ultimate success of digital twins relies on connected, common, and structured data sources based on current information management standards.

Therefore, the first step towards digital twins is the adoption of industry data standards and the implementation of connected data environments that link all aspects of a facility’s data, as it is created.

More about Connected Data Environments for Digital Twins

To learn more about Autodesk solutions for connected data environments and digital twins, please visit Autodesk BIM 360, Autodesk Tandem, and the CDE Info Board at:

CDE INFO BOARD

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https://construction.autodesk.com/tools/construction-document-management-iso-19650/

Further reading

Centre for Digital Built Britain This Gemini Principles paper proposes principles to guide the national digital twin and the information management framework that will enable it: cdbb.cam.ac.uk/DFTG/GeminiPrinciples

The pathway towards an Information Management Framework A “Commons” for Digital Built Britain, CDBB, May 2020: doi.org/10.17863/CAM.52659

Digital Twin, towards a meaningful framework, Arup, November 2019 arup.com/digitaltwinreport

Verdantix, levels of digital twins verdantix.com/newsroom

Gartner Group, Gartner Hype Cycle for Digital Government Technology gartner.com/smarterwithgartner

Autodesk construction blog: constructionblog.autodesk.com/digital-twin

DNV GL: The Digital Twin in oil and gas: How far have we come? blogs.dnvgl.com

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