The subsurface is composed up man made infrastructure; utility, transportation, and other infrastructure and the natural environment comprised of geology and geotechnical properties of the subsurface. A comprehensive subsurface digital twin requires a digital twin of underground infrastructure and a geology digital twin.
Digital twins of the underground infrastructure; utilities, communications, and transportation networks and other infrastructure, of cities, regions and nations have become a priority for many jurisdictions around the world. Over the past decade the emphasis has been on above-ground models, primarily because of the availability of technologies for efficiently capturing high accuracy location data for above ground infrastructure. However, many jurisdictions are recognizing the critical importance of below ground infrastructure and are integrating subsurface infrastructure into their digital twins.
Geological information, such 3D models of geological and geotechnical properties, and borehole databases, comprise an information product that has not yet reached its anticipated level of usage. Geological information has the potential to be useful in a wide range of broad use cases such as city planning, infrastructure construction projects, geohazard identification, environmental impact assessment and the development of water supply facilities, but to date this has not become standard best practice in these application areas. However, there is growing awareness of the importance of subsurface geology (in general but also to the technology used to detect and locate underground infrastructure) and it is expected that in the near future geology digital twins will be recognized as a foundation digital twin equally important to infrastructure digital twins.
Subsurface infrastructure digital twins
Digital models of underground infrastructure is 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 models of underground infrastructure. These fall into the general categories of fitness for purpose, data quality, and accessibility.
Many cities, for example, Rotterdam, Helsinki, Pilsen, Athens, Rostock, Berlin and London to name a few, regions, such as Flanders, and nations such as the Netherlands, Estonia, Singapore, and the UK are developing 3D geometric 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 a number of 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 now a consensus that modeling a city requires not only information about buildings and transportation networks, but also about the utilities and communications networks. Utility and communications network operators maintain extensive records of their assets, including the location of their assets and operating, inspection, and maintenance data. For years they have used monitoring, control and simulation software for tracking and modeling demand, network energy, water, and gas flows, voltage, and pressure and other features of the modern utility networks. Utilities are expanding this capacity and moving in the direction of digital twins that can represent the entire utility system. Ultimately this will enable them to support digital twin use cases that extend beyond utility asset management and network simulation to incorporate models of the entire utility network including demand and the supply chain. Digital replicas of energy infrastructure makes it possible to undertake energy network sizing and routing process analytics to help understand and appraise loads and design optimum energy networks based on actual demand, consumer services and evolving technical solutions. In the UK electricity and gas networks operators have launched a National Energy Systems Map (NESM), a proof-of-concept project that brings together network data from all of Britain’s electricity and gas network operators into a digital, whole-energy system map covering Great Britain. The objective of the pilot project is to give customers information about energy network assets, where those assets are located as well as who owns them and is intended to provide the basis for a digital twin of the UK's energy network.
5G is increasingly recognized as an essential national utility. Knowing the location of all infrastructure is critical to understanding where fiber optic cables and 5G antennae intersect with the people and places that need them. This presents a major challenge because many telcos do not have current, comprehensive data on overground and underground assets in an integrated, easily accessible and shareable format. Operators need comprehensive, accurate and current location-based information on underground fiber-optic networks in relation to all the critical services that depend on them. This will require integrating live data from all assets in the field with the demand for services from customers.
Challenges of subsurface infrastructure
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. First of all, 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 and private ownership imposes restrictions on public access to this data. For example, the City of Rotterdam has decided that a key requirement of its digital twin is open access to the public. But for underground infrastructure the city has had to recognize restrictions on access. About 2/3 of the underground utility data is open and accessible, but there are access restrictions on the remaining 1/3 because these represent privately owned facilities.
Another major challenge in most jurisdictions is data quality. Utility network documentation and records are not collected or maintained consistently, the level of accuracy varies, data is often out of date and for abandoned infrastructure frequently missing. Over the past decades electric, gas, water, and telecom utilities have poured millions into digitalizing the graphic design/work management and GIS/records management processes. But the decades-old as-builting process remains marred by manual paper-based processes that results in low data quality and which has ramifications for the construction industry and for society in general. One of reasons for low data quality of network documentation is that utility business models provide no direct incentives to improve their data at the scale required for a full solution. This is a complicated problem that may require government involvement through regulation, changes in industry best practices and new technologies. Jurisdictions such as Colorado, Montana, and Singapore have implemented regulations focused on improving data quality. New technologies such as reality capture with LiDAR or a handheld phone together with RTK and other technologies for accurate positioning and mobile+cloud solutions for capturing and sharing data in the field are enabling real-time high accuracy data capture during construction.
Return-on-investment studies of improving information about underground infrastructure
Return on investment (ROI) studies of the benefits of improving the mapping underground infrastructure on highway construction projects conducted since the late 1990s have consistently revealed a large return-on-investment. A U.S. Department of Transportation sponsored survey conducted by Purdue University in 1999 of quantifiable and qualitative savings estimated a total of US$4.62 in avoided costs for every US$1.00 spent on accurately locating underground infrastructure. Although qualitative savings (for example, avoided impacts on nearby homes and businesses) were not directly measurable, the researchers made the case that those savings were significant, and many times more valuable than the quantifiable savings. In 2007, the Pennsylvania Department of Transportation commissioned Pennsylvania State University to study the savings on Pennsylvania highway projects and found a return on investment of US$21.00 saved for every US$1.00 spent for SUE when elevating the quality level of subsurface utility information using SUE. This significantly higher return on investment when compared to Purdue study is the result of maturation of process and a consideration of some of the qualitative savings in the Purdue study.
Fitness-for-purpose for subsurface utility models
For underground utilities the Open Geospatial Consortium's MUDDI underground standards project has identified important use cases for underground infrastructure location data including planning, reducing underground utility damage during construction, construction efficiency, asset management, emergency response, and disaster planning. Each use case has specific data requirements. For example, the planning use case may not require survey-grade location accuracy, it may only be necessary to refresh the data once or twice a year, and the level of detail may be low. Fitness-for-purpose for each use case will determine the quality of the data to be fed into the digital twin, the algorithms that model urban processes, the simulations that need to be supported and the analytics and visualization technology that makes the model cognitively easy and accessible to users.
The Netherlands, Helsinki, and Singapore have developed master plans for the subsurface that support multiple use cases. As an example, a number of years ago Singapore recognized the urgency for a map of its underground utilities for a number of purposes; planning, design, construction, ownership and maintenance of underground infrastructure. The Singapore Land Authority (SLA) initiated the Digital Underground project aimed at establishing an accurate, current, and complete map of subsurface utilities in Singapore that could be used for multiple use cases.
Priority use cases for subsurface utility models
However, the priority use cases for subsurface digital models in most jurisdictions are reducing underground utility damage during construction and construction efficiency. The simple reasons for this are economic. The cost of damage to underground infrastructure during construction amounts to tens of billions of dollars every year, injuries and fatalities of workers and the public, and delays and budget overruns on civil engineering construction projects. For this use case data accuracy, currency and completeness are essential requirements. Another issue specific to underground infrastructure is integration. In general each utility and telecom has a different data model, vocabulary, symbology, base map, standards for layout, and data protection guidelines. To integrate data from multiple organizations requires harmonizing these features in a common standard agreeable to all data providers.
Thus current geometric models of underground infrastructure will require important changes to enable them to be used as a basis for a subsurface digital twin. Digital twins are living models and need to reflect the changes in real time of the underlying assets. The key is to integrate live data from all subsurface assets and ensure that the geospatial systems modelling these networks share a common standard and are a ‘living document’ constantly drawing on current data and intelligence from the field. Databases of real time, high precision underground infrastructure data compliant with an agreed on standard are required. This requirement and new technology advances are driving fundamental changes to how data about underground infrastructure is captured and maintained.
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