In 2015, Autodesk launched its cloud-based development API, Forge. Forge was a set of tools and services for building cloud-based software applications, with integrations to all the main Autodesk products.

For some unknown reason, Autodesk recently renamed Forge to the less impressive Autodesk Platform Services (APS), but the capabilities have remained unchanged.

The key aim is to expedite the development of new applications from these core Autodesk building blocks, such as DWG, viewers, file exchangers etc. If there was a poster child as to the benefits of ‘APS’, it is the digital twin solution Autodesk Tandem, launched in 2021, which is still being rapidly developed in front of our very eyes.

The initial release was very bare bones. It didn’t support IFC and was pretty much a place to ‘lightweight’ Revit BIM models and start renaming and filtering sets of building components for more complex capabilities yet to come.

Since then, Autodesk has continually added sizable chunks of workflow-centric capabilities every couple of months. Given so many models are in RVT format, Tandem could be the tool to bring digital twins to the masses.

Just looking at the last six months, it’s clear the Tandem team is on a mission. In January, Autodesk included the facility monitoring beta program, which enabled Tandem to display near real time IoT data. It was also looking to spot anomalies ahead of equipment failures. This is the most exciting technology for me, as it connects the digital twin database with the physical reality of operations.

In April, the new ‘Systems’ feature was released, which uses system tracing to identify and filter routed systems like MEP, which might be imported as just a bunch of individual components with no ‘intelligence’ associated. It also included new facility monitoring visualisations and heatmapping capabilities.

Now, this month comes dashboards, which you might think is very much at the test and consumption end of the digital twin process. However, Autodesk’s first dashboard offering is very specifically aimed at using dashboards to check the completeness of the twin data.

In the demo, dashboards worked off the facility template, which is a place where components get classified and tagged for tracking in Tandem. These can be assets, systems or subsystems, by discipline, and used for phases such as handover or commissioning.

Tim Kelly, senior product manager at Autodesk explained, “We want to be able to provide an experience where we ensure the delivery of both complete and accurate as built data.

“Dashboards are our way to allow customers to dig into specific datasets and review that comprehensively. I know that when the term dashboard is used, oftentimes people refer to a Power BI or Tableau experience where you’re curating all of the data. But we have worked to pre-build some framework around this experience.”

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Dashboards are filtered views, that can be easily custom made to create specific packages of information that are relevant to a given period of time or given delivery phase, specific disciplines, specific packages, or different stages.

Autodesk calls each of these defined elements a ‘card’ and each has chart display options like pie, donut, or starburst. These are stored in a library, so can be inserted into the display to create views with collections of dashboard cards.

The functionality so far developed on dashboards has come from conversations with Autodesk customers who are currently having to take data out into business intelligence applications or specific dashboarding software to create external workflows.

The first function of note is a filter bar which sits across the screen to provide a view of all of the comprehensive filtering across all parameters (sources, levels, Revit categories etc.). This panel reflects everything that is in the dynamic model viewer. Users have the ability to see connected information as components are selected, allowing interaction with the different metrics that appear on screen.

If we are ever going to build solutions that can do generative design of future facilities, we need to understand how facilities operate in the real world to really impact future design decisions

Table views act as a summary of parameter completeness. Parameters are broken down by classification and are applied to different components dynamically. The lower half of the screen displays the dashboard ‘card library’, which is where users build out and track ‘completeness’ of the twin data. This can be applied across different classification levels or on specific parameters which customers want to track. Data pops up when hovering the mouse over the display cards. Some let users drill down, offering additional information on a classification level or Revit type / level etc. and the display automatically updates.

While doing that, the viewer always reflects those selections made. It’s a very interactive experience. Tandem is still a highly visual tool for dynamic display of specific datasets.

Autodesk’s focus on bringing dashboarding into its product, with this specific style of experience, is aimed at providing interactive access to data but is specifically focused on the data collection during twin building. The next phase will be to expand this experience, perhaps with more preconfigured dashboards – facility monitoring, sustainability tracking and data validation are all viable options.

From the demo, the process flow, worked thus: load a Tandem model, use the filters to select categories you are interested in checking. The display updates with the elements that fit the filter rules and the dashboard ‘cards’ dynamically change given on what’s displayed / selected. These cards are ‘percentage of name status completion’, asset’s tagged status, and model number.

Each card shows what percentage of the elements have been classified as required. If you pick all the water heaters, you see total percentage unnamed etc. You can use the filters to isolate these objects and all the tables update with feedback on the selection. Here dashboards are a tool to help navigate through and identify the outstanding components that need classification. On big jobs I can see how beneficial these tools would be.

Conclusion

To give credit where it’s due, from the complete list of AEC software tools in development at Autodesk, I think Tandem is probably the one with the highest velocity. That possibly might be because it is the newest and has the most to add. But I am impressed with the development team’s monthly reach out to engage with customer (and non-customers) to either discuss digital twin issues or demonstrate upcoming or recently introduced functionality.

I think this is a template for all software development teams but, of course, this is way harder when you have tens of thousands, to millions of users for managers of mature products.

A lot of the earlier functions were centred on the core data parsing and visualisation that is required in the creation of digital twin datasets. While dashboarding might be thought of as more of a way to display real assets status, Autodesk is again approaching this as a tool set to further identify and isolate models to easily rectify omitted data and complete the digital twin.

Making dashboards requires a lot of filtering and check box ticking, together with an inherent knowledge of the twin’s integrated systems and components. It’s the first Tandem demonstration which really made me think just how much data preparation work, filtering and tagging the digital twin process requires. And all this needs to be done before you get anything valuable out.

I couldn’t help but feel that a ChatGPT interface would go a long way to simplifying the derivation of dashboards and it’s something that Autodesk is currently looking into but there are legal issues.

As its capabilities continue to expand, Tandem’s maturing capabilities will be a challenge to keep the interface simple, hiding the complexity of the process, especially when many of those ultimately digesting the output of the system will not be digital twin experts.

Issues with digital twins

Those who understand and fully support BIM and VDC methodology are typically very supportive of digital twins and the use of them – if the data has been created in the design process and refined through to construction, especially where COBie data has also been built up for all the serviceable elements of a building.  However, there is a lot more to transitioning that data into a useful environment.

The three primary challenges hindering the uptake of digital twin technology are 1) the lack of clarity on why it is better vs traditional 2D Facilities Management (FM) tools, 2) the complexity in creating detailed source data and 3) the cost associated with implementation / maintenance.

While you may think having a ‘fresh’ detailed BIM model would make the process relatively seamless and painless, BIM design and construction data is not the type of meta data the digital twin databases need.

The act of creating a digital twin requires meticulous and detailed mapping of physical assets and their data to virtual components, demanding expertise. It requires significant time, and the maintenance of this needs to be ongoing for the life of the building / asset.

It’s something that needs significant planning, resourcing, and budgeting for. The kind of costs associated with current digital twin technology mean that smaller companies, or projects, in particular, may find it challenging to allocate budgets to create and maintain digital twins of their assets.

There is also the problem with the lack of standardised frameworks and BIM’s inherent interoperability issues which multiplies as layers of data may come from other industries (oil and gas) which use diverse software and hardware systems. This presents a challenge for creating and integrating them seamlessly into one database.

Autodesk here does have the advantage of Revit supporting architecture, structural and MEP. The UK did have a five-year project running with the Centre for Digital Built Britain (CDBB) at Cambridge University, but this recently completed its five-year funding at the end of 2022 and has since and closed its doors. However, the Gemini Papers explaining the benefits of connected twin technology are still available as a useful resource.

Security is a potential issue because linking digital twins to IoT sensors means sending data from physical assets to the digital twin data model through the public internet. Organisations remain wary of entrusting critical data to this threat.

Furthermore, there is a widespread lack of awareness and understanding surrounding digital twins. Many decision-makers and industry leaders remain unfamiliar with the technology’s potential, perceiving it as an experimental venture rather than a tangible solution to real-world challenges. This lack of comprehension results in reluctance to invest in uncharted territory, slowing down adoption rates.

Why and how we are developing for digital twins?

Robert Bray, Vice President & General Manager, Autodesk Tandem

In the opening address of the recent Tandem update, Robert Bray, vice president & general manager, Autodesk Tandem, gave a great opening talk, concerning the workflow of making digital twins and offering some insight into how Autodesk is approaching the software development.

“As we think about a digital twin, there’s really two things we think about. The first is, that a twin is a digital replica of that build asset. And the important part of the replica is context- understanding of the equipment, the assets, the spaces, the systems in that facility, the interconnections between them.

“The other important aspect of the digital twin is, of course, the bi-directional connection between the physical and digital connections to all of those operational systems that give the twin the operational behavioural awareness necessary to simulate predicted informed decisions, based on real-world conditions.

“From a larger industry perspective, we really think about this, as how do we transform the asset lifecycle? That means thinking beyond what happens in design and construction, and structuring data that’s useful to an owner, beyond handover – a period in which it can be connected to existing solutions and used to build up a wealth of asset knowledge.

Why do we want to do this? Three things:

The first is to understand if the facility performs as it’s planned and designed, or not? And if not, what can we do to tune that facility to achieve its objectives? Those may be sustainability criteria, or outcomes in terms of throughput, facility, occupancy, whatever it might be.

The second is thinking about how do we detect issues and then improve that facility based on data insights? Facility management as a practice needs more data to do the job more effectively, and that means not just more data, but data that is informing those decisions, rather than just data for data to come. We need to take that data from all those operational systems and turn it into information that leads to actionable insight.

And finally, the reason we do this is to leverage that knowledge to build better in the future. If we are ever going to build solutions that can do generative design of future facilities, we need to understand how facilities operate in the real world to really impact future design decisions.

When we started this, we really bought into a maturity model around digital twins, and the purpose for this is really to start to provide a prescriptive framework for how to approach digital twins. It starts with the descriptive part, which is really that ‘as built’, what are all the assets and spaces, systems in their facility. All the metadata about them and the connections. The informative twin starts to add that operational behavioural awareness to connect some sort of physical and digital systems in that facility. This might be maintenance management systems or maintenance history, IoT sensors, building management systems, other automation systems – that type of thing – to bring that to life and provide that actionable insight.

Predictive twins then start to add ‘what if’ scenarios around looking at maintenance history of a component and predicting when it should be replaced, based on past-history of that component and failure of that component.

Comprehensive twins, starting to think about ‘what if’ simulation around what if I reconfigured this space, what’s the impact on occupant behaviour? If I upgrade the system, what’s the impact on carbon emissions and energy consumption? Autonomous twins, being the holy grail of the self-healing and self-tuning facility.

As we think about this, what’s important is we define our data standards in those early stages. Well defined Tandem models lead to downstream capabilities. If we don’t have those normalised data standards, it’s very hard to build machine logic that can lead to predictive, comprehensive, and autonomous site capabilities in the future. So really drilling in on these normalised data standards is very important.

As we think into Tandem digital twins, we think Tandem in terms of two sets of workflows.  Building workflows, which is really about how do we harness all of the design and construction data, or the ‘as built’ data of that facility, to create that digital twin, complete with connections to those operational systems and data.

The second workflow is operations, how do we take that insight we’re gleaning from those that operational data and use it to inform decisions? These two things work in concert with each other. A facility is an ever-evolving thing, they’re not static, they change [every] day. We need to recognise that twins change as well, in terms of adding new workflows and capabilities. Hence, the two are interconnected.

When we think about twin building, we really think about it as a three-step workflow, really defining the data requirements and the outcomes through transparent collaboration. This is really about getting those data standards well-articulated and captured for not just a facility but a portfolio of facilities. Being able to contribute all of that data from the data from submittals, from other types of documentation, maybe from as-built documentation for the facility. And the piece we’ve talked about a lot, but we haven’t shown before is the idea of who ensures complete, accurate ‘as built data’. This is what our dashboard capability is built around, really verifying completeness and accuracy.

As we talk to customers, one of the big things we hear is that we don’t trust the data we have, we need to be able to provide capabilities to ensure that completeness and accuracy of that digital twin data, to ensure it matches and reflects the ‘as-built’ facility.

Our twin building capabilities are built on a lot of the capabilities we have at Autodesk in terms of our Autodesk Construction Cloud platform, our Autodesk Docs platform, our design products like Revit, standard formats like IFC. We have AutoCAD and Navisworks in beta, but we are working towards Autodesk Build integration, we know that’s important for capturing information about things as they’re installed and commissioned on the job site. And of course, we do support that universal tool of construction, Microsoft Excel, which is often used to capture data through the construction process.

As we move downstream into operations, we think we have come up with something a little bit different. The twin building experience is clearly very much a member of the AEC-like experience, but for downstream operations. We need to provide facility managers, facility operators, an experience that is more tailored to their needs – the ability to monitor their facility through a dashboard.

We need to be able to give them the ability to drill in and investigate anomalies, again, providing more of a curated experience rather than a free-flowing 3D experience. And then, obviously, surface that information that helps them take a proactive action based on the insights they’re gaining.

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Preoptima has launched its ‘Carbon Twin’ software for creating dynamic carbon replicas of as-designed and as-built assets.

The cloud platform and API combine transparent and geolocated carbon coefficient data with real structural modelling, regulatory compliance, and accurate material quantities to create Carbon Twins that facilitate ongoing optimisation and embodied carbon avoidance of building designs.

Users can import their own geometry or use the built in generative design tool to analyse ‘hundreds of thousands of low-carbon design iterations in minutes.’

According to the climate tech startup, unlike other life cycle assessment (LCA) software which requires material quantities information to perform an assessment, Preoptima’s early-stage material quantities generation lowers the barrier to entry for all building stakeholders to mitigate the carbon in their projects.

Other new features include the integration of building services (MEP) and operational energy modelling using UK and US grid decarbonisation scenarios.

Preoptima expects the software will be able to model all required building systems and types, as well as existing buildings by the close of this year.

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Opening out onto the city’s imposing and historic Piazza Garibaldi, there has been a rail way station on the site occupied by Naples Central Station (Napoli Centrale) since the mid-nineteenth century. The current station was designed and built in the 1950s and today handles around 400 trains per day and 150,000 passengers, making it the sixth largest train station in Italy for passenger flow.

It is also the site of a pioneering BIM pilot project, exploring the use of BIM for facility management on a massive scale. The BIM manager for this project was Minnucci Associates, an engineering company based in Rome, Anguillara Sabazia and Milan, working on behalf of the station owner, Rete Ferroviaria Italiana (RFI), which is responsible for the management and safety of railway traffic on the entire national network, including tracks, stations and installations.

Faced with the need to develop new buildings while simultaneously keeping existing buildings and equipment in good condition, RFI initiated the project with a view to capturing the station’s assets. Through surveying and modelling, a digital twin would be created and then converted to an open and collaborative format, supporting integration with RFI’s facilities management system in order to improve the whole-life value of assets.

A heavyweight pilot

For the team at Minnucci Associates, this challenge represented one of the most data-heavy projects it had ever worked on. To give an idea of the complexity involved, Naples Central Station is spread across five buildings, covering around 400,000 square feet and containing some 12,500 components subject to maintenance. These components include electrical, HVAC, hydraulics and vital safety equipment.

The main challenge was the vast size of the model. Graphisoft BIMcloud came to our aid, as we were able to divide the survey data and the resulting model into federated files, then remerge later either within Archicad, or using the exported IFC models Daniele Piccirillo, BIM Manager

The work began with a survey of the site, using laser scanners and orbital pictures, resulting in a massive point cloud of 380 GB in size. From there, the team at Minnucci Associates developed a digital twin of the station, including equipment needing maintenance.

The station was modelled by comparing and combining survey data, existing drawings and census outcome, with all the input data and output models organised in a common data environment (CDE).

Graphisoft’s Archicad was the chosen BIM authoring tool. In total, 44 models were federated and then imported into Solibri in IFC format. By mapping the BIM authoring tool with IFC open standards, Minnucci Associates was able to create automated workflows and instant asset recognition.

sing Graphisoft’s BIMcloud, the team organised survey data and models into a single, catalogued database. Formalised as an BIM execution plan, the RFI team was able to approve changes and request information remotely throughout the entire process. This system also allowed them to reincorporate individual files, both in Archicad’s authoring model and later in IFC models. Today, on-site tasks are supported through the use of a mobile app directly connected to the CDE.

Scale and complexity

“The main challenge was the vast size of the model,” says Daniele Piccirillo, BIM Manager at Mannucci Associates. “Graphisoft BIMcloud came to our aid, as we were able to divide the survey data and the resulting model into federated files, then remerge later either within Archicad, or using the exported IFC model.”

Given the scale of the site, the complexity of the models and the numerous processes supported, this is a pioneer project for the use of BIM for asset and maintenance management. It also offers a compelling glimpse of how improved facilities management might look for organisations across a wide range of industries. A digital twin approach could work for many.

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TwinUp is a new AI-driven architectural software tool for creating, managing, optimising, and presenting 3D design models and project images.

The platform is powered by ‘Arch-e’, an AI-driven personal assistant that processes ‘vast amounts’ of data in real-time to transform the design process from beginning to end.’

The software suite is currently in beta and includes three integrated apps: TwinUp Community, TwinUp Building, and TwinUp World.

TwinUp Community is a free virtual design portfolio and social media platform just for architects. Users upload, enhance, organize, and share images and videos of their design work with private groups and the larger architectural community at their choosing.

TwinUp Building is a 3D digital twin maker app that helps architects convert their 3D models (BIM, et. al.) into what TwinUp describes as digital twin models. TwinUp Building’s visualisation features and simulation capabilities allow users to render, analyse, optimize, present, and share their models with peers and clients.

TwinUp World is a 3D virtual digital twin model of the earth whereupon users can place their 3D project models for analysis, enhancement, rendering, and presentation in their proper local site context. Features include advanced navigation tools, data layers, rendering tools, multi-party collaboration interfaces, and simulation features. An App Store of plug-in modules adds a selection of ML-based design simulation capabilities, from simple daylight studies to more complex carbon emissions simulations for single and multiple buildings.

TwinUp Community is slated to launch early this summer (2023), and TwinUp World is expected to launch later this Autumn (2023). The TwinUp Beta Program is currently accepting applications from practicing architects.

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CupixWorks, the spatial digital twin platform from Cupix, has joined the Bentley Systems powered by iTwin Program.

The CupixWorks platform is designed for decision-making and collaboration through all stages of a building’s lifecycle.

It allows project managers, general contractors, architects, and owners to remotely view, track, and manage on-site progress via 3D spatial contexts and ‘life-like’ 3D navigation.

The platform can be used to synchronize and visualize 3D, 360° capture data — along with BIM models plus customized data — and use georeferencing to place the capture in the site’s physical location.

“We are excited to add CupixWorks to Bentley’s powered by iTwin program,” said Simon Bae, CEO at Cupix. “The iTwin Platform’s visualisation and synchronisation capabilities help CupixWorks to deliver life-like 3D navigation that feels like you’re on site. This improves collaboration for stakeholders—wherever they are—based on what’s actually happening at the site.”

“CupixWorks is a great example of how 3D reality capture data and BIM can be synchronized for virtual site navigation and progress tracking of complex construction projects,” said Adam Klatzkin, Vice President, iTwin Platform, at Bentley Systems.

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The definition for urban digital twins is too vague — so it is important to create a clearer picture of the types of urban digital twins that are available. Not all digital twins are the same and each one comes with features and capabilities, strengths and weakness, as well as appropriate and inappropriate use cases. Based on my recent presentation of my SIMO software at the Smart Cities World Expo in Barcelona and many exchanges with clients, customers, designers and project managers, I believe it is time to create a better understanding of urban digital twins.

As shown in my proposed Urban Digital Twin Taxonomy above, I propose that we classify these products first based on their Main Functionality (the Use Case), then based on their Technology Platform. I highlight some of main products within the different categories and their product scope. Next, I detail the different types of twins and offer some brief strengths and weaknesses for each type. This taxonomy could apply to other industries such as architecture or manufacturing, but it is specifically applied to cities and urban development projects.

The main functionalities can be grouped by:

• Modelling Twin
• Computational Twin
• Scenario Twin
• Operational Twin
• Experiential Twin

The technology platforms can be grouped by:

• Computer Aided Design (CAD)
• Web GIS
• Geographic Information System (GIS)
• Gaming

BIM is not included since it is limited to building scale objects and does not have any data model for the broad spectrum of urban systems. Please let me know what you think! Did I miss anything?

Modelling Twin

This is the most common type of urban digital twin because it provides basic functionality and workflows common to traditional CAD software. These can be based on either CAD technology (running mostly on the desktop computers) or Web GIS technology.

These products support mostly manual design modelling with automations in the background. In some cases parametric inputs and form generation algorithms are exposed so that it is more interactive.

Strengths
CAD
• High accuracy modelling and data.
• Common design process and easy to learn.
• Very powerful general purpose software readily available.

Web GIS
• Can integrate external geospatial data layers.
• Common design process and easy to learn.
• Easier for multiple users to view the twin (but does not mean collaborate in real time).

Weaknesses
CAD
• More difficult to integrate external geospatial data layers.
• Mostly limited to desktop modelling.
• Real-time collaboration is difficult.

Web GIS
• Low data accuracy for both the model and external data layers.
• Mostly limited to web-based modelling and requires continued internet connection to run.
• Risk of high (or very high) operational costs.

Scenario Twin

The majority of these urban scenario twins are based upon GIS or Web GIS technology. Due to their GIS origins and data sources, this is especially 2D geometry and analysis, with some products working on better 3D integration.

Due to their GIS origins they tend to implement well known geospatial tools and analysis to the product.

Strengths
• It can allow for diverse testing of development scenarios, but this depends on the product itself.
• Potential to integrate many geospatial data layers.
• Web GIS — common design process and easy to learn.
• Web GIS — easier for multiple users to view the twin (but does not mean collaborate in real time).

Weaknesses
• Limited to a specific geographical region (always).
• Limited to geospatial data sources.
• Limited geometric detail and accuracy.
• Web GIS — risk of high (or very high) operational costs.
• It is well known and documented that urban development scenarios are far more complex than any simulation can offer, so there is an illusion of control, accuracy and intelligence that is very risky to decide urban policy on.

Computational Twin

Urban Computational Twins are extremely rare and are the most exciting for us at Spatiomatics. Our SIMO software is based on CAD technology and a unique product offering in relation to the other types of digital twins. I would say that SIMO is probably the only Urban Computational Twin and the only one that provides a standardised Urban Information Model.

SIMO provides computational design workflows that fully integrate into the McNeel software platform of Rhino 3D and Grasshopper 3D. You can integrate all methods of computational design, generative design, evolutionary design and an endless amount of custom project workflows developed in-house by design teams across the globe.

Strengths
• Full control over the design process, generating form, automating tasks, full data-driven design.
• Possibility of advance coding, scripting, and data analysis.
• Speed of design iterations and evolution of the final design.
• Enormous ecosystem of extra tools, plug-ins and workflow optimisations as part of the Grasshopper ecosystem.
• High accuracy of both the geometry and data with access to very powerful computational geometry algorithms.

Weaknesses
• You need to understand how to work with computational design software which adds some extra learning to the process.
• It is more challenging to create a stable data set due to the volatile nature of computational design, but it is possible.

Operational Twin

Urban operational twins tend to be built with either GIS or Gaming technology platforms. Their main purpose is to monitor urban assets in real time based upon Internet of Things (IoT) sensor data overlaid on an urban model. These software are not intended to be modelling, computational or scenario planning tools. They are for the most part management tools for constructed elements. Operational twins are very common in buildings (e.g. manufacturing plants, sports stadia, transport hubs, etc.) and less so for urban environments. They typically contain a lot of information both in terms of geometry and data.

Strengths
Gaming
• High quality representation of models (sunlight, textures, detail).
• Possibility to see the model in VR or AR.

GIS
• Standard workflows based on decades of development.

Weaknesses
Gaming
• The technology was not intended for general purpose use and tends to be overcomplicated. Developing software on these technology platforms is challenging.
• The technology is designed for representation and not creation, so it does not support modelling, data management to general users of the software. Skilled technical teams need to build the digital twin and then deploy it for public use.
• No system for data governance, user administration.

GIS
• Legacy systems which make them difficult to modernise to current needs.
• Is not designed for general purpose modelling and data management. Skilled technical teams need to build the digital twin and then deploy it for public use.
• Has limited software providers and Esri has a monopoly on commercial tools and is not known for innovation.
• Difficult to scale to urban areas while maintaining detail and accuracy. With both gaming and GIS technologies it is challenging to combine diverse data sources, formats and technology standards.

Experiential Twin

Experiential twins are fairly new to the market since they are fully based on gaming technology platforms which have only recently begun repackaging their products for more general use. They are used to create a more ‘realistic’ experience of an urban environment which is especially helpful for stakeholder communication (e.g. communication with a non-technical community affected by a project). Likewise these twins often allow for both VR and AR experience of an urban development. I teach an Urbanism studio at TU Delft where students use VR to design and analyse their projects — it is a fantastic tool. These are very helpful for assessing development and design scenarios (e.g. new streetscape design, density proposals, etc. The image right shows how Amsterdam uses gaming technology. You can zoom into the model and see more detail, depending on twin’s Level of Detail (LOD).

Strengths
• Helpful in creating a ‘feeling’ of the environment if you detail the model.
• Helpful in communicating an urban development to non-technical stakeholders.
• Gaming technology is very advanced and develops quickly, so models will become more realistic with time).

Weaknesses
• Difficult to create these models, you need lots of technical expertise.
• Limited data integrations and user interface options for seeing project data or technical analysis.
• The technology is demanding on the computer and the models which are shareable online will have some data restrictions.
• These are not made for modelling, analysis or scenario planning. They are purely ‘representational’ digital twins and have limited functionality.

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US engineering design and consultancy firm WSB has launched a digital construction management solution and advisory service, based on Bentley Systems Synchro.

The aim of the service is to help civil infrastructure firms overcome the challenges of adopting model-based digital workflows and harnessing the power of ‘construction digital twins’.

WSB joined the Bentley Digital Integrator Program for construction to help develop the service.

“Owners and construction firms realise that new digital workflows are needed to meet infrastructure demands. Applying these digital workflows successfully requires a deep understanding of technology, processes, and data,” said Carsten Gerke, senior vice president of strategic partnerships with Bentley Systems.

“The Bentley Digital Integrator Program is built around combining technology with subject matter expertise for improved infrastructure. WSB joining the program provides a leapfrog opportunity for all our transportation users.”

Key services include enabling a single source of truth by connecting project, contract, and document management to the future of design—a 3D/4D/5D constructable model—as well as the ability to create constructable models from current 2D plan sets, which allows the transition to a single source of truth for all stakeholders.

Bentley Synchro is a construction management software that supports the entire civil construction lifecycle with ‘office-to-field’ workflows. It is designed to give firms insight into project performance, productivity, and financial health and forms the construction service of the Bentley Infrastructure Cloud.

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It’s nothing new to be able to model building sites, cities and countries. However, such activity traditionally requires substantial technical skill, special software, and a significant amount of data wrangling. Now, the availability of rapidly developing reality modelling tools that leverage LiDAR scans and photogrammetry techniques has drastically lowered the cost of producing large-scale models automatically. In turn, this has created an influx of demand for applications that are easy and efficient to use, prompting companies and governments to look for ways to digitise their infrastructure, however big.

Typically, the reality capture models might look stunning but are very dumb. Intelligent data needs to be added, combined with building information and geospatial data, such as cadastral surveys. Here the industry’s deep-seated problems with data silos just add to the level of complexity.

ArcGIS Reality is Esri’s new play to offer a tool where reality capture 3D data, BIM and GIS information can be accurately layered and enable functions such as analysis, asset monitoring and predictive simulation can take place. Esri is used to handling huge data sets and tiled information with its ArcGIS application. The company is certainly ambitious, as it believes that the ArcGIS platform, when mixed with BIM and reality capture models, and when connected to real-time sensor feeds, will become ‘digital twins of reality’, from buildings and utilities in real-word context, to trainlines and motorways at a country scale.

Questioning reality

AEC Magazine’s Martyn Day caught up with Esri’s Kurt Schwoppe, sales manager for Imagery and Remote Sensing and Marc Goldman, director of AEC Industry Solutions at Esri, to talk about the new product and the company’s interest in the built-environment space.

He asked what technology Esri was using to build its reality models. “Esri has had a drone solution for a couple of years but recently, we bought a company from Germany called nframes, which brought the ability to do high-end processing across city wide, and even country size areas, dealing with thousands and thousands of images, multiple terabytes of data,” explained Schwoppe. “It’s opened the door for us, so we can run that full spectrum of remotely sensed content to generate digital twins, or at least the visualisation component of digital twins (I know that digital twins are more than just a 3D visualisation).

“This is where we start with reality mapping, enabling organisations that have facilities or assets which were built before the days of 3D CAD to go out and do remote sensing collection with drones or aircraft and bring it into our ArcGIS reality software.”

Format

What is the company’s attitude to files? “3D models are now an inherent part of the ArcGIS platform,” explained Schwoppe. “ It’s been a significant focus of ours, and we now support many different industry standard formats. And we have an approved, open format called I3S (Indexed 3D Scene Layer Specification) which we helped specify. It’s a 3D standard which is specifically designed to package up and optimise large data models for streaming over the web or locally. It has support for 3D point, 3D object and 3D mesh layers.

“In the geospatial industry, there’s a there’s a group called the Open Geospatial Consortium (OGC) and they produce open standards for the GIS community. We worked with them to create an open 3D modelling format (i3S) which anybody can use or take advantage of. It’s what all our 3D models get published into.

“We take the raw sources, the drone/ aircraft captures, and push them into these i3S models. Of course, if we need to, we can also write these out in more common formats like OBJ etc. but Autodesk and Esri have embraced this format. We can bring this fusion of CAD with the remotely sensed data, to create a digital twin.

Use cases

Schwoppe thinks that Esri’s ‘special sauce’ is its ability to combine 3D captured data seamlessly with its complete range of 2D GIS capabilities and now this can be greatly expanded.

One of Esri’s flagship products is ArcGIS Urban, which enables cities to be modelled, but up till now had very simplistic 3D (extruded polylines). With the new reality capture technology, customers can now replace the low-poly model with a digital twin of any city. This means digital twins can be used in resolving urban mapping issues, for visualisation requirements or regional planning. City data that looks like the actual city can be combined with traditional GIS data, such as traffic lights, crosswalks, fire hydrants etc.

In oil and gas, Esri has customers that have extensive facilities together with long distance pipelines which might span miles or countries. ArcGIS Reality enables the assets to be modelled in 3D, spanning the whole length of the pipeline, including plant, pumping stations, encroachment areas and GIS mapping data over huge areas.

With this new technology, we’re now down to a level of accuracy that someone would really have to think twice about what they were looking at

One of Esri’s customers is the US Corps of Engineers, and they are working on a project in the Houston, Texas area called ‘Texas Coast’. Schwoppe explained, “It will be the largest civil works project in the history of the United States, in real dollars. Even larger than the Panama Canal, as they must build a series of dikes and locks to protect the coast of Houston, as it’s critical for us and our oil industry. The Corps need to 3D map the terrain of that whole area before they build the dikes.We can pull that all together in a single solution.”

Digital Twin

When is a digital twin not a digital twin? It’s a perplexing issue. A dumb 3D model of a whole city, with no smart information is just a model, even if it’s a seriously cool model. At AEC Magazine we conclude that one of the essential components of a digital twin solution is the ability to connect it to live sensors to bring the model to life, to help make those important facilities related decisions.

We asked Esri how far they had got with connecting up their models and how their tool would compete with Autodesk Tandem. Marc Goldman responded, “We’re connecting to SCADA (Supervisory Control & Data Acquisition) sensors and IoT sensors. We’re doing heat maps of spaces and spatial analysis.

There is an opportunity to add value to Tandem, and I’ve worked on both sides of the fence, I’m a (Autodesk) fanboy, is that it’s still a building floating in space, there’s no context. You don’t see the street, you don’t see the streetlights you don’t see the utilities underground, you can’t just turn on a layer and see the traffic flow or the weather patterns. So, imagine all the model capabilities of Tandem, with all the context and layers that Esri GIS can bring in.”

Another important data class in digital twins is laser scan data to capture the as-built and compare that to the as-designed, as Goldman explained, “Point clouds in GIS environments tend to be very ‘marshmallowy’, the corners tend to be rounded over and small details like a streetlamp would kind of be a blob. With this new technology, we’re now down to a level of accuracy that someone would really have to think twice about what they were looking at. Is that a 3D mesh? A point? Cloud? A photo? A NERF? It’s all coming together in ArcGIS Reality.”

Schwoppe added, “If it’s collected correctly and the surveyors dropped the right points, ArcGIS will display 3D geospatial data to sub centimetre accuracy.”

Scanning

A lot of firms that have come into this space have the technology and platform but don’t have the datasets of cities or countries. I asked the team on where the content is coming from.

Goldman explained, “We, Esri, don’t go out and collect data, we might take sensor data, and then our cartographers turn it into a format such that it can be a layer easily added. But our content play tends to be more of a partner play than us ourselves.

Schwoppe added, “Esri has a product called ArcGIS Living Atlas which is a collection of GIS data from around the world, supplying maps, apps and data layers for climate change analysis, farming information, flood prevention, land use, even COVID trends and other natural disasters. We are building our own 3D dataset of the world but it’s not at an infinite level of detail. It’s a start.

“Any engineering firm with a drone can go out and get this level of accuracy and visualisation and we can feed that into the GIS. We also have third partners, including Hexagon, which has one of the best cameras nearmap) which we support. Customers can commission a company to go out and fly a route to collect specific data and we can help by providing standards on how they should collect.

“We can also work with you and our partners to go out and collect an area of interest. We also have partners who just collect different areas on speculation, like nearmap. Their 3D models work within our environment directly. We want to provide a software solution that gives the customer the most amount of flexibility. We can work with just about any drone or drone camera that’s out there. We can work with LIDAR data, provided it’s in our supported list of file formats. And we can work with any type of aerial camera, whether that’s on our drone, an aircraft, or a satellite.”

ArcGIS Reality doesn’t come with technology to perform clean-up of imported laser scans. Schwoppe and Goldman explained that the best place for that to happen is in the specialist applications which come with the laser scanning systems like Leica Cloudworx, but they do offer tools to clean up errant meshes.

Conclusion

Esri is the long reigning champion of the GIS mapping world. Ever since Esri president and co-founder Jack Dangermond and Autodesk CEO Andrew Anagnost announced a development partnership at Autodesk University 2017, the company has actively pursued connection and convergence with the rest of the 3D AEC world. This was a significant change of mindset. At the time, Dangermond explained, “You cannot design in a vacuum. You cannot build in a vacuum. You cannot operate and manage infrastructure and assets in a vacuum.” Esri sees CAD and GIS as essential tools in managing the built environment. In many respects, with Esri’s strong penetration of the planning and city-facing end of things, its focus on combining all data sets in 3D should hopefully drive 3D tools and the understanding of 3D data into planning permission and permit departments which have resisted model-centric approaches, and BIM data (buildings and civil).

The Reality Modelling world is growing. We have established players like Bentley Systems,  Vu.city and recent entrants from Leica Geosystems (Hexagon) whose highly-capable HxDR solution combines a capture service and an online data store with web-based, highly detailed textured meshes as well as BIM model data. Every firm seems to be taking slightly different approaches which is handy, depending on the needs of individual companies. Esri brings this GIS part of the jigsaw to the table and with strong integrations with Autodesk will look to take a slice from the emerging digital twin market, at all scales.

Currently, the commercial list price of ArcGIS Reality Suite is $20K for a one-year subscription, which runs on any CPU, with no limit to the number of cores or GPUs. To support times of peak workloads, Esri provides discounted surge pricing for additional licences.

ArcGIS reality family

There are several distinct products in the ArcGIS Reality family. Some are extensions to already established GIS products, others are new tools aimed at data formatting, editing and production of Reality data.

ArcGIS Reality for ArcGIS Pro is the extension to connect Esri’s desktop GIS software, to pull in images from automated drones  manned flights to generate 3D outputs for reality mapping.

ArcGIS Reality Studio is a whole new application for collating and generating reality maps from aerial images of cities to even whole country-scale. The application delivers ‘survey-grade’ representations of reality.

Site Scan for ArcGIS is Esri’s cloud-based end-to-end reality mapping software for drone imagery, simplifying drone program management, imagery data collection, processing, and analysis.

ArcGIS Drone2Map is a desktop application focused on reality mapping from drone imagery, enabling offline processing and in-the-field rapid mapping.

Main image: ArcGIS Reality City model of Frankfurt displaying textured and mesh data

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Vodafone has created a 3D digital twin of its UK mobile mast network to allow its engineers to visualise and create strategies for network improvements or expansion, without leaving their desks.

The digital twin involved mapping more than 40 million environmental features using GIS (geographic information system) software from Esri UK, including buildings, hills, valleys and trees.

Engineers now have access to a 360-degree virtual view of the network from their laptop or mobile device over a secure connection. They can plan where to position new mobile sites more effectively and identify which ones need upgrading or repositioning to meet increases in customer demand, or to compensate for the construction of new buildings or tree growth.

Dr Rebecca King, Geographic Information System (GIS) lead at Vodafone, explained, “A customer’s mobile phone might cut out due to what we call clutter. This is usually down to the construction of new buildings or seasonal tree growth interfering with the signal strength. We like to visualise these in a digital format so that we can better plan the expansion of our network around them.”

Approximately 500,000 network features such as antennas, along with billions of rows of network performance data can be visually presented. With a few clicks, an engineer can inspect a component of a mobile base station remotely before deciding whether to jump in their van and drive to the site to carry out any necessary work.

The company is now looking to test a similar digital twin service in other markets, including Germany and Turkey, and is exploring options to create an intelligent online replica of both its mobile and fixed broadband networks.

Minecraft for data scientists

“The digital twin gives us an unprecedented understanding of our entire UK mobile network – it is like Minecraft for data scientists,” said Boris Pitchforth, lead architect at Vodafone.

“We can be smarter and faster about how and where we add new 5G features, and target capacity increases with greater precision. There’s also the added benefit of being able to reduce our carbon footprint as our engineers won’t need to make as many site visits, especially to masts in remote areas.”

Working with the UK arm of Esri, Vodafone used satellite data to map the terrain, including land use such as crops, transport links and height data of neighbouring objects. “The digital twin doesn’t need to exactly replicate objects in the real world such as the individual bricks of a building, only its dimensions, so that we can angle the signal to give customers the best possible connection. The simpler the map is, the faster it loads,” added Boris Pitchforth.

Large-scale digital twin

Vodafone used Esri’s ArcGIS Enterprise platform which combines web mapping, image exploitation, real-time data handling, large-volume batch analysis and spatial data science.

“Using ArcGIS Enterprise has allowed us to add the spatial dimension to a lot of data we were already working with, resulting in new levels of location intelligence,” continued Dr Rebecca King. “Through our digital twin, data can now be visualised in 3D and shared easily with multiple teams.”

In addition to introducing the digital twin to other countries, Vodafone also plans to use it to support the rollout of new network features such as Massive MIMO – providing more capacity at a single cell site – to meet the proliferation of connected devices, which are predicted to grow globally to 30 billion by 2025.

“A few years ago, a national digital twin of this type simply wasn’t possible,” said Boris Pitchforth. “But the combination of ArcGIS Enterprise in AWS (Amazon Web Services) cloud means large-scale digital twins can be a reality, providing a secure, scalable cloud option for enterprise data visualisation and geospatial analytics. Similar projects in the utilities sector, for example, traditionally focus on smaller areas but we wanted a national model in line with our network.”

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Hexagon has acquired LocLab, a German company that uses proprietary workflow technology and AI-based object recognition to help automate the creation of reality models for digital twin applications.

LocLab’s in-house developed ‘toolchain’ can utilise several data input formats with a view to balancing speed, cost and accuracy. This includes terrestrial videogrammetry, survey data and point clouds, although the company states that photographs or videos are only required at a minimum.

When creating 3D models of buildings and infrastructure assets, the AI software compares detected objects with LocLab’s object library of 3D elements (street objects, building components, rail equipment, etc.) to semi-automate the creation of a ‘semantically enriched’ 3D model (i.e. adding context and descriptive elements).

“We continue to advance the utility and potential of the digital twin. We call it the Smart Digital Reality,” says Hexagon president and CEO Ola Rollén. “Like any digital twin, it provides an intuitive 3D hub for data management and information. But it has traits not always associated with digital twins: it’s data-rich, kept up to date in real-time, and workflow-driven, making it seamlessly accessible. And most importantly, it’s autonomously intelligent, meaning it can implement physical world actions independent of human intervention.”

“The foundation of any smart digital reality is the underlying 3D model. If not already part of one of our solutions or readily available via our content program, it can be time and resource-intensive to create,” continued Rollén. “LocLab’s semi-automated modelling process with AI-based object recognition from video data and its enormous library of 3D digital content offers tremendous time savings and cost efficiencies.”

“The integration of LocLab’s 3D digital content with HxDR, our cloud-based storage, visualisation, and collaboration platform, will drive HxDR’s expansion as a leading digital reality platform within transportation, construction, urban planning, and many other industries,” continued Rollén. “Altogether, LocLab’s capabilities nicely complement our leading reality capture and software portfolio while also offering LocLab global scalability opportunities through our expansive sales and partner network.”

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