A user platform for digital twins of underground utility infrastructure

The foundation for digital twins of cities, regions and nations are digital geometric models. Developing these models have become a priority for a number of jurisdictions around the world. To date the emphasis has been on above ground models, primarily because of the availability of a variety of technologies for efficiently capturing high accuracy location data for above ground infrastructure. However, there is growing awareness of the critical importance of below ground infrastructure. Several jurisdictions are integrating below ground infrastructure into their digital models. Models of underground infrastructure are characterized by important distinguishing features that differentiate them from above ground digital models and that need to be taken into account when maintaining and using the geometric models. Digital twins are living models and need to reflect the changes in real time of the underlying assets. An on-line platform that that has been used primarily in large-scale simulations and games has a number of unique features suited for digital twins by enabling data providers including construction workers in the field, developers and users of analytics and simulations and citizen users to collaborate in real time in maintaining and using subsurface digital twins.

Background

Many cities (Rotterdam, Helsinki, Pilsen, Athens), regions (Flanders), and nations (Netherlands, Estonia, Singapore, UK) are developing 3D models as a basis for digital twins. 3D models of above-ground infrastructure include buildings, transportation networks, parks, and other infrastructure typically captured in 2D or 3D imagery from overflights or in 2D GIS maps. But for several reasons underground infrastructure is often neglected, even though it is recognized that water and wastewater, energy (gas, electric power, and district heating), and communications (fibre and copper) networks provide the life blood of the city.

There is a fundamental difference in what jurisdictions are able to do with representations of above and below ground infrastructure. Above ground, whatever can be photographed or scanned in the public space; from the street, from an airplane or drone, or from a satellite, in 2D or 3D, is generally unrestricted in application. It is necessary to remove recognizable people and vehicles for privacy reasons and there may be national security restrictions, but in general this data can be used without restriction to create an open, publicly available digital model or digital twin. The accuracy of the above-ground data in digital twins is generally high – for digitally LiDAR scanned data accuracy even reaches the mm level.

Underground the situation is different. Typically there are restrictions on access to underground infrastructure location data. In many jurisdictions some or all of the underground infrastructure in the public right-of-way is privately owned. Where cities own water and sewer services, this data may be open and publicly accessible. But electric power, gas, and telecom are often privately owned. Private ownership imposes restrictions on public access to this data. Accessing the data requires a legal data sharing agreement between the utility or telecom company and an entity requiring access to the data. For example, the City of Rotterdam has decided that a key requirement of its digital twin is that it is open and accessible to the public, but for below ground infrastructure there are restrictions. About 2/3 of the underground utility data is open and accessible, but for the remaining 1/3 there is restricted access because these represent privately owned facilities.

Another problem specific to underground infrastructure is that in general each utility has a different data model, vocabulary, symbology, projection, base map, standards for layout, and data protection guidelines and harmonizing these for a shared underground utility map requires collaboration among data providers.

Jurisdictions such as the City of Rotterdam, Estonia and the U.K. are already developing geometric models that include underground and above-ground infrastructure. For underground utilities the Open Geospatial Consortium’s MUDDI underground standards project has identified important use cases for underground infrastructure location data. The priority use cases are reducing utility damage during construction and efficiency of planning, engineering design and construction. Other use cases include emergency response, disaster planning, and strategic planning for smart cities. Each use case has different requirements for data access, quality, currency and level of detail.

Existing databases of underground geospatial data, most of which were developed and are maintained by utilities and telecoms, are not suitable as geometric models on which digital twins can be based for a number of reasons. They are comprised of a 2D topographic map, typically with road centre lines, water bodies, building footprints and other recognizable features. Utilities either use a national base map, such as MasterMap in the UK, or have acquired a base map from a commercial mapping company. In North America in general each utility has a different basemap on which the location of utility infrastructure is superimposed. Transferring underground location data to a different base map can be a complicated process (conflation). Furthermore, each utility uses its own terminology and has developed its own data model and symbology for its displaying infrastructure maps. Most underground infrastructure data is 2D and lacks depth coordinates. Access to utility infrastructure data is generally restricted to the utility’s own staff and contractors. In addition the accuracy of the data is low. It is low precision, often incomplete, and out of date. It is cumbersome to use in the field and is unintelligible to non-engineering stakeholders.

These current geometric models of underground infrastructure will require important changes to enable it to be used as a basis for a digital twin. Databases of real time, high precision underground infrastructure data are required and this is motivating fundamental changes to how data is captured and maintained. Live updates during construction and 3D reality capture with survey-grade precision are essential. Technologies that enable near real time precision data capture include LiDAR scanners, photogrammetry using handheld devices, ground penetrating radar and other underground remote sensing technology, with high precision GNSS using a base station or RTK. Being able to simultaneously capture above and below data facilitates connecting above ground visible features such as manholes, drains, risers, gas connections, hydrants, and so on with below the ground equipment. The database includes all planning, engineering and construction documents and support the integration of geospatial data from sources such as GIS, CAD and BIM.

User platform for subsurface digital twins

A platform specifically for data providers and users of a digital twin has been developed that is intended to be used after a comprehensive digital model has been built and not for the development of the geometric model or new designs. It supports importing digital building and infrastructure models and provides tools for multi-user collaboration to maintain those models and keep them up to date whenever there is a change, as a result of maintenance or if there has been damage. A key requirement for this platform is scale-ability, it must support a large-scale 3D environment on the scale of a city. The amount of 3D data is growing exponentially driving a requirement for tools that will allow massive volumes of 3D data to be manipulated and visualized. The new generation of engineers are familiar with and expect three dimensional virtual environments, whether it’s gaming or simulation software. Within the next 10, 20 years, they will be the ones who manage this infrastructure, so providing them with an interactive 3D environment including mixed reality where they feel comfortable provides them with an environment that best enables their skills, has to be undertaken now, because building out digital twins in three dimensional view is going to take years.

The platform does not limit the number of simultaneous contributors who could be construction workers on the ground who want to update the model, city planners and managers running analytics and simulations, and citizen users who want to visualize and understand the results of the analytics and simulations. For all users the platform provides the most up-to-date and accurate information to enable workers to use it in near real-time. It can connect to live feeds from IoT sensors in the field to be able to monitor conditions in real-time. Data is versioned to enable tracking changes in the data over time. The platform provides an interactive environment supporting mixed reality applications. It runs in the cloud where it is universally accessible in a fully protected, secure environment. In addition data protection rules protect data custodians and prevent viewing of network information that could provide competitors information about a company’s network.

Building a persistent environment is extremely important, because every participant, whether a user or a data provider, needs to be able to contribute to growing the environment and seeing in real time what everyone else is seeing. For example, if a telecom company begins installing a new fibre cable and at the same time the local water company is looking at that model when designing a new water pipe, the water designers will be able to see the new fibre cable in near real time. Being able to see edits in real-time makes it possible to edit the model with several users collaborating simultaneously. Everyone can see everyone else’s changes and conflicts can be avoided. Some utilities are collecting point clouds captured with LiDAR or photogrammetrically of newly installed equipment before covering the trench and these can be used in a mixed reality environment to maintain and make modifications to existing infrastructure.

The benefits of a living digital model are better and faster decision making. According to the CEO of one Swiss multi-utility company, not knowing exactly where the pipes are means that “As soon as we start digging, about 90% of plans have to be thrown away or redrawn, because we find stuff we didn’t know actually existed underground.” This refers to Switzerland where there is a very rigid replacement cycle requiring about 3% of roads to be replaced and refurbished every year, but even with this they are still missing quite a lot of information. A reliable, up to date model allows you plan for maintaining or replaceing existing infrastructure, precisely where there is free space to avoid unnecessary relocations and avoiding disruptions to existing infrastructure that is delivering vital services to the communities. Better planning means fewer disruptions of services to the public. Better planning also results in faster project startup, fewer and less extensive utility relocations during construction, fewer injuries to workers and the public, fewer project delays and more on time and on budget completions for construction projects. Over time this produces substantial cost and time savings. Reduced risk of utility damage also has indirect benefits. For example, it can lower insurance costs. A scaleable, 3D platform with high precision, real time data can also support other use cases such as proactive equipment maintenance, disaster planning, emergency response and other things that can improve the resiliency of infrastructure. Such a platform supporting users of a subsurface digital twin will minimize the impact on the local communities from any kind of disruptions that are potentially going to interrupt services.

Digital twins of underground infrastructure are living models of real world objects and require a platform and tools that enable a large number of data providers and users including construction workers in the field, developers of analytics and simulations, and citizens to collaborate in maintaining and using the models. The key is to integrate live data from all assets and ensure that the geospatial systems modelling these networks are a ‘living document’ constantly drawing on current data and intelligence from the field.

This post is on Alex Shalash’s and Antoine Castel’s talk at the Canadian Underground Forum (CUF). You can listen to all the talks at CUF on the GeoIgnite CUF Youtube channel.

This information was first published on https://geospatial.blogs.com/geospatial/2021/10/user-platform-for-digital-twins-of-underground-utility-infrastructure.html