The last decade has seen the emergence of numerous digital technologies that redefine technology and the business landscape. Digital technology adoption is necessary for businesses to survive. Companies that have not adopted them either fade out or catch up with the new industry leaders to stay relevant. Data is the digital age oil and technologies around data are becoming increasingly important for industries.
One of the most often quoted technologies in the last five years is digital twin technology used in various industries to enhance operational efficiencies. Thanks to different new-age technologies like the cloud, the Internet of Things (IoT), and vast amounts of computing, digital twins are emerging as a critical technology in several industries. They are listed amongst the "Gartner Top 10 Strategic Technology Trends" for the last three years, and the industry is spending a lot of money to keep abreast of its developments.
One of the most often quoted technologies in the last five years is digital twin technology used in various industries to enhance operational efficiencies. Thanks to different new-age technologies like the cloud, the Internet of Things (IoT), and vast amounts of computing, digital twins are emerging as a critical technology in several industries. They are listed amongst the "Gartner Top 10 Strategic Technology Trends" for the last three years, and the industry is spending a lot of money to keep abreast of its developments.
What is a Digital Twin?
A digital twin is usually termed as a digital replica of a physical asset. Do you remember those miniature architectural models made of plastic and wood created to visualize the physical building before it was constructed? These earlier digital models only replicated the physical dimensions of the asset. Unlike these simple digital models, a digital twin encapsulates a plethora of data points corresponding to physical dimensions, its various properties, and processes. This initial digital twin is then connected to process real-time data of the asset in use, its operating conditions, etc. – allowing the digital twin to parallelly model the asset's various characteristics, processes, etc.
Digital twins are more dynamic than the digital models and are constantly evolving –analyzing and learning from the historical data and using other inputs to improve or optimize asset performance. They are based on massive, cumulative, real-time, real-world data measurements across an array of dimensions. These measurements and the twin's dynamic nature would provide real-time insights regarding the physical asset performance. As long as the system is being measured, it can be modeled in the digital twin, and the more data you capture from the real-life scenario, the more robust and accurate the digital copy can be.
Digital twins are more dynamic than the digital models and are constantly evolving –analyzing and learning from the historical data and using other inputs to improve or optimize asset performance. They are based on massive, cumulative, real-time, real-world data measurements across an array of dimensions. These measurements and the twin's dynamic nature would provide real-time insights regarding the physical asset performance. As long as the system is being measured, it can be modeled in the digital twin, and the more data you capture from the real-life scenario, the more robust and accurate the digital copy can be.
Origin of Digital Twins
How do various systems perform under conditions that could not be created on Earth? This problem of NASA, where the missions were to reach new planets or the great unknown, was the genesis of Digital twin technology. NASA used this concept to test a variety of systems for its various projects – and understand their performance in different conditions so it can accurately reflect and predict the status of the space vehicle & its numerous systems in operation. NASA now uses digital twins to develop next-generation vehicles, aircraft, and various systems.
It is widely quoted that the term "digital twin" appeared in the early 2000s as an evolution of PLM (Product Life cycle Management)[1]. However, with the advancement of technology and the evolving use cases – digital twin solutions have evolved from the earlier, simple simulated digital model to more connected, integrated & intelligent solutions integral to business decision making.
It is widely quoted that the term "digital twin" appeared in the early 2000s as an evolution of PLM (Product Life cycle Management)[1]. However, with the advancement of technology and the evolving use cases – digital twin solutions have evolved from the earlier, simple simulated digital model to more connected, integrated & intelligent solutions integral to business decision making.
Creating a Digital Twin
A digital twin can often comprise different layers, with each virtual layer representing an equivalent physical layer. These layers can include materials, structures, electronics, and so on. But how does one create a digital twin?
Depending on its maturity, a digital twin can range from a simple 2D or 3D model of a local component to a fully integrated and highly accurate model of an entire asset or facility with each element dynamically linked to engineering, construction, and operational data. The digital twin conceptual architecture (figure 1) may be best understood as a sequence of six steps, as follows:
Depending on its maturity, a digital twin can range from a simple 2D or 3D model of a local component to a fully integrated and highly accurate model of an entire asset or facility with each element dynamically linked to engineering, construction, and operational data. The digital twin conceptual architecture (figure 1) may be best understood as a sequence of six steps, as follows:
Create
The starting point of the digital twin is creating the digital model of the physical asset that encompasses the various physical dimensions, characteristics, and processes. This digital model is enabled to assimilate multiple data sources that can be broadly classified into two categories:
- Operational measurements about the performance criteria of the physical asset (including multiple works in progress)
- Environmental or external data affecting the operations of a physical asset, such as temperature, pressure, noise level, etc.
The various data sources may be augmented with process-based information from multiple systems such as the manufacturing control systems, ERP systems, CAD models, and supply chains management systems. This would provide the digital twin with a wide range of continually updating data to be used as input for its analysis
Communicate
The 'Communicate' step helps the seamless, real-time, bidirectional integration/connectivity between the physical process and the digital platform. Advancement in network communication is one of the foundation blocks that have enabled the digital twin. With the advancement in mobile technologies, data communication is much easier and faster.
Aggregate
The 'Aggregate' step can support data ingestion into a data repository, processed, and prepared for analytics. The data aggregation and processing may be done either on the premises or in the cloud. The technology domains that power data aggregation and processing have evolved tremendously over the last few years in ways that allow designers to create massively scalable architectures with greater agility and at a fraction of the cost in the past.
Analyze
In the 'Analyze' step, data is analyzed and visualized. Data scientists and analysts can utilize advanced analytics platforms and technologies to develop iterative models that generate insights and recommendations and guide decision-making.
Insight
In the 'Insight' step, insights from the analytics are presented through dashboards with visualizations, highlighting unacceptable differences in the performance of the digital twin model and the physical world, analog in one or more dimensions, indicating areas that potentially need investigation and change.
Act
The 'Act' step is where actionable insights from the previous steps can be fed back to the physical asset and digital process to achieve the impact of the digital twin. This interaction completes the closed-loop connection between the physical world and the digital twin.
The computation power of big data engines, the versatility of the analytics technologies, the massive and flexible storage possibilities of the aggregation area, and integration with canonical data allow the digital twin to model a much richer, less isolated environment. In turn, such developments may lead to a more sophisticated and realistic model, all with the potential of lower-cost software and hardware.
The computation power of big data engines, the versatility of the analytics technologies, the massive and flexible storage possibilities of the aggregation area, and integration with canonical data allow the digital twin to model a much richer, less isolated environment. In turn, such developments may lead to a more sophisticated and realistic model, all with the potential of lower-cost software and hardware.
Digital Twin Use Cases
Digital twins can come in many forms depending on the industry and use case. For example, a production line may have a digital twin that shows the line's status, which may be connected to the feeder and transfer lines that show any stoppages. The same digital twin may even be connected to a finite element analysis platform, showing how changes to a product's design can affect the facility's real-life operations and efficiency.
At the other end, operations and maintenance monitoring of flight engines by companies like Rolls Royce, GE, etc. – where every component and process of the flight engine is mapped through a digital model. This model will be enriched with the real-time flight travel data that would enable the model to estimate the wear & tear of various components and any predictive maintenance if required. Flight maintenance teams are armed with this information by the time the flight touches down at its destination. It enables them to straightaway address issues, if any, reducing the maintenance time – ensuring a quick turnaround.
Digital twins are being used in diverse industries across many use cases from manufacturing, self-driving cars, wind turbines, aircraft engines, etc. They are also being used across various life cycle phases – with design optimization, design verification being done through digital twins resulting in huge cost and time savings. They are predominantly being used for the operations and maintenance phase. Real-time data is fed into the system to analyze the wear and tear of components for predictive maintenance. Overall, digital twins are emerging as the cornerstone in the asset life cycle management and redefine the business processes associated with it.
At the other end, operations and maintenance monitoring of flight engines by companies like Rolls Royce, GE, etc. – where every component and process of the flight engine is mapped through a digital model. This model will be enriched with the real-time flight travel data that would enable the model to estimate the wear & tear of various components and any predictive maintenance if required. Flight maintenance teams are armed with this information by the time the flight touches down at its destination. It enables them to straightaway address issues, if any, reducing the maintenance time – ensuring a quick turnaround.
Digital twins are being used in diverse industries across many use cases from manufacturing, self-driving cars, wind turbines, aircraft engines, etc. They are also being used across various life cycle phases – with design optimization, design verification being done through digital twins resulting in huge cost and time savings. They are predominantly being used for the operations and maintenance phase. Real-time data is fed into the system to analyze the wear and tear of components for predictive maintenance. Overall, digital twins are emerging as the cornerstone in the asset life cycle management and redefine the business processes associated with it.
Digital Twins in Construction
Although construction is one of the world's largest sectors, the industry suffers from significant productivity issues. The sector is also behind on technology adoption and is one of the lowest-ranked in productivity improvement in the last 5-6 decades.
However, with the introduction of BIM and other digital solutions, the Construction processes are becoming increasingly digital. These solutions have helped digitize some of the processes – but the industry couldn't leverage significant value out of such implementations. Digital twins hold the potential to change workflows and enable the workforce to do more complex tasks faster and better. Digital twin records every single step of the construction cycle, so lots of data are packed into each one. This data includes:
However, with the introduction of BIM and other digital solutions, the Construction processes are becoming increasingly digital. These solutions have helped digitize some of the processes – but the industry couldn't leverage significant value out of such implementations. Digital twins hold the potential to change workflows and enable the workforce to do more complex tasks faster and better. Digital twin records every single step of the construction cycle, so lots of data are packed into each one. This data includes:
- BIM and 3D models
- Project Plan
- Reality capture from images sourced through drones, 360-degree cameras, CCTVs, etc.
- Construction documents (i.e., procurement, change orders, RFIs, etc.)
- Operational data collected by the embedded sensors
- Insights from AI and machine learning technology
Though digital twins in construction are in infancy, it is expected that they will be completely embedded with the industry workflows within the next decade.
Digital Twins in Construction
Indeed, the real power of a digital twin—and why it could matter so much—is that it can provide a near-real-time comprehensive linkage between the physical and digital worlds. It is likely because of this interactivity between the real and digital worlds of product or process that digital twins may promise richer models that yield more realistic and holistic measurements of unpredictability.
Using data from multiple sources, a digital twin continuously learns and updates itself to represent the current working condition of the object or process. This way, businesses can save huge costs across the project lifecycle right from the design phase to construction and then during the operations & maintenance phase – thereby reducing the cost and time for project execution while driving faster operational turn arounds. New use cases are being published where there is a clear Return on Investment, accelerating the adoption of such solutions.
Below are a few top-level benefits of digital twins:
Using data from multiple sources, a digital twin continuously learns and updates itself to represent the current working condition of the object or process. This way, businesses can save huge costs across the project lifecycle right from the design phase to construction and then during the operations & maintenance phase – thereby reducing the cost and time for project execution while driving faster operational turn arounds. New use cases are being published where there is a clear Return on Investment, accelerating the adoption of such solutions.
Below are a few top-level benefits of digital twins:
- Reduce risk Digital twins can help identify, predict, and analyze risk. In addition to reducing risk on a job site, digital twins can also help facility managers identify potential risks for building occupants and visitors
- Improve Efficiency Thanks to data collected from AI and machine learning, digital twins can help project teams streamline operational efficiency and improve construction quality.
- Aid Effective Decision Making Simulations help project managers and other project stakeholders make better overall decisions regarding changes.
- Lower costs Digital twins can reduce construction costs, which can help improve a project's profitability. Thanks to better risk mitigation and predictions, certain costs can be avoided entirely.
- Enhance coordination Improved coordination potential is another benefit. Digital twins can allow project teams to understand better how systems interact and where there are potential conflicts. They can also improve sequencing for installation and help connect the office with the field, improving overall day-to-day operations.
- Augment security Using live imagery and real-time analysis of the site could help in the early identification of security risks. These safety hazards could be flagged to the concerned for taking quick evasive measures to safeguard the workforce.
- Better Visualization Digital twins enhance BIM. Models on their own often cannot provide the same level of response that digital twins can. When a BIM-centric digital twin is used, the model has all the same data as the physical construction site would, which improves knowledge on constructability.
Conclusion
Human nature is to be resistant to change – but if you don't change, you will find that similar companies are prepared to embrace those changes and become more productive. We need to embrace change, but we need to do it more carefully – it is going to be an evolution rather than a revolution.
Gone are the days of designing a part in CAD, only to throw that 3D model into an archive. Digital twins can transform previously dumb CAD models into dynamic and living system components at the heart of the twin. A digital twin has many applications across the life cycle of a product and may answer questions in real-time that couldn't be answered before, providing kinds of value considered nearly inconceivable just a few years ago. Perhaps the question is not whether one should get started, but where one should begin to get the most significant value in the shortest amount of time, and how one can stay ahead of the competition. What will be the first step, and how will you get started? It can be an overwhelming task to get there, but the journey begins with a single step.
The digital twin may drive tangible value for companies, create new revenue streams, and help them answer critical strategic questions. With new technology capabilities, flexibility, agility, and lower cost, companies may be able to start their journeys to create a digital twin with lower capital investment and shorter time to value than ever before. There are so many different use cases across industries for digital twins that one thing is for sure: digital twins are going to evolve dramatically in the next few years, based on the divergence of solutions to the different challenges observed by each company.
Although the promise of these capabilities has yet to be fully realized, we are excited about what they will mean for the construction industry. At 3RDi, we are continually developing our platform and finding ways to make this technology applicable to the challenges faced in the real world.
Stay tuned for our next post, where we'll walk you through how 3RDi uses digital twins to automate construction progress monitoring & simplify construction management.
Gone are the days of designing a part in CAD, only to throw that 3D model into an archive. Digital twins can transform previously dumb CAD models into dynamic and living system components at the heart of the twin. A digital twin has many applications across the life cycle of a product and may answer questions in real-time that couldn't be answered before, providing kinds of value considered nearly inconceivable just a few years ago. Perhaps the question is not whether one should get started, but where one should begin to get the most significant value in the shortest amount of time, and how one can stay ahead of the competition. What will be the first step, and how will you get started? It can be an overwhelming task to get there, but the journey begins with a single step.
The digital twin may drive tangible value for companies, create new revenue streams, and help them answer critical strategic questions. With new technology capabilities, flexibility, agility, and lower cost, companies may be able to start their journeys to create a digital twin with lower capital investment and shorter time to value than ever before. There are so many different use cases across industries for digital twins that one thing is for sure: digital twins are going to evolve dramatically in the next few years, based on the divergence of solutions to the different challenges observed by each company.
Although the promise of these capabilities has yet to be fully realized, we are excited about what they will mean for the construction industry. At 3RDi, we are continually developing our platform and finding ways to make this technology applicable to the challenges faced in the real world.
Stay tuned for our next post, where we'll walk you through how 3RDi uses digital twins to automate construction progress monitoring & simplify construction management.
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