The arrival of the digital twins

Imagine Fornite meets Minecraft meets the real-world city you live and work in – what a computer game that would be. Imagine working in a data-rich 5D on-screen world that updates in real time and in which you can play ‘what if’ scenarios. We’re actuallythere right now. By Chris Kirchhoff*

Take an accurate 3D representation of reality, be it your factory, sewage works, a portion of the CBD or an entire city. Now add to this the fourth dimension of how this world and its surrounding environment – such as weather, traffic, population growth and supporting services – drive changes over time. Then, layer behind this income, expenses and flow of money, as the fifth dimension, and you have yourself a digital twin.

The growing internet of things (IoT) enables data to stream backwards and forwards between our real world and our computers. That enables this ‘game’ to work. The IoT enables us to livestream data from the urban environment. This is what’s moved our old-fashioned 3D maps into live digital representations of the real world.

This is how a digital twin becomes a dynamic everchanging representation of reality, providing us with both reactive and predictive feedback. For reactive feedback, we can consider that as live traffic data comes in, we can adjust traffic flows to ensure better peak-time use of the current roads in our city. Predictive feedback is when we use our digital twin to do future planning. Examples include simulations of population growth or cost increases or droughts to understand how best to allocate the finite resources available in the most sustainable manner.

You can look at a digital twin as a Wi-Fi-enabled Lego set. Are you considering a new taxi rank? Imaging putting together a Lego model of the planned rank. As you make changes, you have a dashboard of data showing you how many people you can move through the rank, how peak-time traffic flows around the rank will change and, critically, how your residents can save time moving through the rank.

Once the taxi rank is built, you can add sensors such as traffic count, temperature, water and sewage flows, and cameras. This allows for your digital twin to suggest, in real time, how to reactively use the resources in the rank most efficiently.

Modelling evolution

In the last century, the first digital 3D models of our environment began to emerge. Over the past 40 years or so, the 3D CAD model has grown into a building information model (BIM).

The latter includes a 3D illustration of our building or environment, as well as implanted metadata about the components that make up the model. So, what are the tools needed to create a digital twin? As a foundation, you need accurate, up-todate geospatial data – the ‘where’.

The upside is that mapping has become super-efficient. By combining laser or lidar scanning with aeroplaneor drone-based aerial mapping, a 3D model of the built environment can be efficiently collected.

There are three main mapping tools to consider. Within the subsurface world, ground-penetrating radar is the main one. Radar and electromagnetic pulses reveal the location of services such as electrical and communication cables.

Above ground, terrestrial laser scanning enables you to collect accurate street- and ground-level information such as roads, building footprint service access points, street furniture and terrain models.

For larger areas, traditional aerial mapping – using a combination of lidar (laser) and photogrammetric techniques – is the best way to build the 3D model upwards from ground level. Recent advances in cameras now allow for the vertical view and the sides of buildings to be captured. This means the digital model can now accurately depict building façades and provides more understanding on how a building interacts with its outside world.

The ‘what’ and ‘why’

The beauty of drone mapping is its costefficiency and data collection simplicity, as well as the continual updating of the digital twin as the environment changes. Without a strong foundation of good, continually updated 3D data, the trust in the predictive ability of the digital twin will be eroded. The next step in the twin is the ‘what’. What is changing in the data flow? To understand this, a collection of sensors and data-gathering tools are positioned in the real world.

The digital twin is then updated with these sensor positions. Again, this highlights the importance of good 3D mapping. If the 3D model is poor, it will be hard to understand where the sensor data is coming from. Sensors will measure parameters like weather information, pedestrian foot flows, traffic counts, services use (such as water, sewage and electricity) and internet data use, which are all fantastic for determining human density and rate of movement.

This data can be combined with social media monitoring for feedback on how the cityscape is being used, as well as user behaviour. Think of these as the nerves of the twin. The third step is the ‘why’. Why is the world changing? The resultant changes, shown in the sensor information, are the basis for informed decisions taken by the users of the digital twin model. This is where the changing relationship between people and the built environment is being modelled in real time.

A combination of machine learning and artificial intelligence allows for the insights being collected by the geospatially positioned sensors to be converted into actions. This is the last step in the data value chain that has been created by the building of a digital twin. Multiple data sources with position, time and cost information are now combined and synthesised into actionable data. This is the ‘brain’ of the twin.

“The influence of a digital twin is in its ability to help decision-makers derive new insights and inform better decisions, providing a holistic visualisation of infrastructure asset information and performance,” says Robert Mankowski of Bentley Systems.

Benefits for municipal infrastructure

To appreciate the influence that digital twins can have on improving municipal management, consider the following possibilities:

• Clear insights for improved infrastructure: instead of different departments each having some form of spreadsheet that shows costs and work done, this is all combined and the historic data can be used to predict growth bottlenecks and the allocation of limited resources. The decisions made are based on data-driven insights.

• Make infrastructure last longer and be more resilient: by understanding how the built environment is being used and where the pressure points are, it is possible to plan for early maintenance that will allow for the infrastructure to last longer. Rather than reacting to failures in infrastructure, the digital twin will model the cost of early maintenance and assist in the justification of an improved maintenance budget.

• Public feedback: municipalities can export the digital twin dashboards into a public forum where communities can provide feedback in an interactive manner that promotes buy-in from residents.

• Improved planning: by using the historic data collected by the digital twins, with predictive modelling methodologies, far better scenario planning can be carried out. Using 3D visualisation techniques from virtual gaming, it is possible to provide a far clearer future vision and to test these models with different financial and resource inputs. Virtual testing of scenarios minimises wasted physical materials, labour and energy.

• Improved team coordination and communications: by having a central realtime data source, all departments can look at and understand operational decisions as they are made.

In closing, by breaking down existing silos and combining them into a single source, digital twin information is democratised and becomes a facilitator. A recent smart cities report prepared by Sue Tabbitt perfectly summarised this, stating that “A state-of-the-art digital twin is essentially an integrated, centralised platform (or ‘nerve centre’), where diverse information about assets and associated services is combined, monitored, analysed and acted upon. It can be a critical facilitator of transformation – delivering benefits across all phases of the life cycle of designing, running and maintaining/ improving local infrastructure, whether within a single organisation or across an entire city.”

*Chris Kirchhoff is a professional land surveyor (PLS0962) at 5DGEO Professional Land Surveyors. Email: chris@5dgeo.co.za