Between the Poles

Product-as-a-Service:a paradigm shift in low cost access to advanced underground mapping technology

Gyro or inertial mapping provides an effective technical solution to the challenge of accurately mapping underground utility infrastructure. Gyro mapping solutions are available for any type of underground utility including water, gas, electric power, communications, ducts, and wastewater. Using this technology it is possible to map networks of pipes or ducts with diameters ranging from 29-34 mm to 90-1500 mm for distances of up to 2 km. Solutions are being offered globally by quite a large number of service providers.

Product-as-a-Service (PaaS)

Gyro mapping is perceived to be expensive because it is currently offered in a very inefficient manner with high mobilization and de-mobilization costs. Whereas the reality is that that if you provide the service efficiently gyro mapping has a price advantage over other technologies. That is why we're introducing the Product-as-a-Service (PaaS) concept which represents a true paradigm shift in how gyro mapping is offered.  

Erratic per unit pricing, which is actually the norm in the mapping industry, is a problem that inhibits new advanced mapping technologies like gyro mapping technology from competing more effectively with traditional underground mapping methods such as GNSS. The way pricing currently works is that service providers ask a fixed rate for a day's work. The fixed cost covers mobilization, de-mobilization, the crew, the equipment, the planning, and the actual mapping. But the actual amount of mapping conducted may vary from a very small portion of the daily rate to virtually all of it. As a result a crew is often inefficiently deployed because the volume of mapping is fairly low, but it still takes a day. As an example, a service provider whose standard rate is $3000 per day is requested to come in and do a 300 foot mapping. That translates into an effective of cost of $10 per foot, which is very expensive. On the other hand if they had mapped 1,500 feet that day the average cost would have been $2 per foot.

 

Enabling the efficient delivery of gyro mapping services requires two things; reducing mobilization and demobilization rates and a pricing model that reflects the amount of subsurface mapping actually conducted.

The solution is to bring the technology closer to the job sites to reduce mobilization and de-mobilization costs and to adopt a pricing model that more accurately reflects the amount of gyro mapping that is actually performed.

The foundation of Product-as-a-Service (PaaS), is that Reduct makes its equipment available to the service provider free of charge.  On its side the service provider commits to conducting a certain amount of underground mapping using gyro technology per year. The service provider either conducts the underground mapping work itself or subcontracts a contractor, engineering firm, or surveyor to conduct the pre-construction survey work. In either case the service provider provides the Reduct gyro mapping equipment used in mapping the location of underground utilities. 

Of course this will require that a lot more service provider people (or your subcontractor's people) be trained in gyro mapping to perform this work. But you no longer need to travel half way around the world to acquire Reduct training. Reduct have set up the Reduct Academy where you can find video-enabled training packages for all of our tools, how to assemble them, operate them, process the data, and generate the 3D models.

Transparent competitive pricing

PaaS makes pricing transparent and enables gyro mapping to be offered competitively to traditional technologies. Service providers can provide prices to their customers based on the rates that Reduct charges for the PaaS service. PaaS rates are simply a balance between the value of the equipment that we provide and the feet of subsurface mapping that is committed by the service provider.

A key benefit of the Product-as-a-Service concept is that it provides for complete flexibility in your choice of equipment. PaaS allows you to select, exchange or return technology depending on your current requirements. Because the technology is provided free of charge you have complete flexibility in choosing the equipment you want to use.  Reduct hardware solutions are available right now for pretty much every type of utility including water, gas, electric power, communications, ducts, and wastewater and for a wide range of pipe and duct diameters ranging from 29-34 mm to 90-1500 mm for distances of up to 2 km.  The pipe or duct can be any material and any shape; round, rectangular or X -shaped. 

Here's some practical examples of the advantages of full flexibility. We supply you with an ABM-30 for an inch and a quarter data duct, and an ABM-90 or a DR-4 for doing larger diameter pipes - anything up to 500 millimetres. Now, if you commit to mapping a hundred thousand feet with those two units on an annual basis, then you will be charged 52 cents per foot for mapping done using this equipment. That is a flat rate, it doesn't matter which of the two units you use. There is zero investment risk because tools are exchanged and replaced as they become obsolete.

Now if your projects change and your latest job requires that you map a 210,000 foot pipe, you find you need to upgrade your ABM-90 to a DR-4. Your rate per foot drops to 27 cents because on the one hand, you have a slightly more expensive tool, but you have a lot more volume. So your rate goes down to 27 cents.

For service provider's people in the field the benefit of the PaaS model is full hardware flexibility and transparent, competitive pricing.

Cloud-based services

We've also set up cloud-based services, where your experts can monitor everything that happens to all the tools that are within your portfolio without needing to be on site. With that online service, you can see performance statistics, quality statistics of the tool such as spread and length scaling, that give a very good indication of how well the tool was pulled through the pipe and how accurate the result is likely to be.

You can centralize all the activities that are happening, reporting, quality control and other activities. You can monitor the quality of an individual tool or of an individual operator. For example, if an operator is performing less efficiently than another operator with the same equipment or if you detect data quality issues with a particular operator, you may consider giving some remedial training or monitoring specific operators more closely.

Reduct community

We have created a community of Reduct users. Within that community, we have pipeline owners, service providers, and service partners. Service partners are people and organizations with relationships to Reduct.  For example, they are a local place where you can go to exchange your tools or have them re calibrated. So every region will have service partners.The community will be a place where you can basically interact with your partners or your service providers.  If, for example, you have a very interesting reporting template for a certain customer, which is very specific, and you know other people could benefit from that, you can offer it through the community.

This sums up the new concept we call Product-as-a-Service, or PaaS that provides a way to bring gyro mapping into the mainstream technologies, just like any of the other ones.

Implications for introducing other advanced technologies

We believe that this approach has implications for subsurface mapping in general, because it provides a way for other new advanced subsurface mapping technologies to be offered into the market competitively with traditional mapping technologies.

 

This post is based on Otto Ballintijn's (Reduct) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

 

Automated feature recognition helps democratize utility mapping for producing infrastructure digital twins

In jurisdictions worldwide the low quality of underground location data has been identified as a top cause of underground utility damage. Existing underground utility data, most of which is stored in network owners' records, is often inaccurate, out of date or simply missing.  In North America in a survey of 402 locate professionals better facility maps received nearly unanimous agreement as a top strategy for improving the efficiency and quality of locates.  Singapore's Digital Underground project is unequivocal about the economic and social impact of low quality underground location data stating that "virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities."

Challenges facing the utility mapping industry

The root cause of the low quality of underground utility data is how utility mapping is structured as an industry. The primary activities of the utility mapping industry are data collection and how we keep and share this data with other parties.

Data collection

The way we collect is slowly changing, but predominantly we're still using traditional survey and locate methods. The survey methodology we are using hasn't changed in a hundred years. It's a very time and materials based, manual and analog process. Survey processes are slow and expensive to execute and require specialized equipment and skill sets, and safety is a big concern.

Locating underground utilities is one of the biggest causes of delays in construction schedules, service disruptions are a common occurrence, safety is always a major concern. and locate operations are slow to execute. Accuracy and timeliness are major issues. Locate operations require specialized equipment and skill sets which restricts locators to a small number of industry insiders.  A major shortfall is that underground utility location data is rarely available during the crucial planning and design stages of civil engineering projects.

Democratization of utility mapping

The utility mapping industry needs some fundamental changes. We need to move away from the monopoly that exists right now. There is just a handful of companies that are able to go out and collect underground utility data. We need to democratize data collection so that many more people can collect this data, targeting a crowd sourced approach and enabling multiple creators or collectors providing this data. This distributes the responsibility for the data as well. The more distributed the responsibilities, the lower risk and the lower the cost of failures will be.

We also need to change the skill level required. With current technology, we should be able to automate much of routine locating and thus enable many more people to do it. Using inexpensive technology will lower the cost of collecting and aggregating locate data. As data becomes lower cost to capture, data retention will become less expensive which will encourage people to retain the location data they acquire rather than simply painting/marking the ground and not capturing the data digitally.

Democratization is the real future of 3D infrastructure mapping. Right now data is still very siloed. We'll always have the traditional mapping companies, survey companies, line locating companies and asset owners going out and collecting their own data. But through crowdsourcing data from routine day to day locating and mapping, infrastructure projects, and other sources, aggregating that data and distributing access to that data is how utility mapping will really come to the forefront.  It should be easy to upload data into a central register where it can be made accessible to multiple stakeholders. Compliance with Open Geospatial Consortium (OGC) standards provides a basis for full interoperability enabling open access to every single conduit, cable, pipe, or other piece of infrastructure in real time from your phone, your tablet, your laptop or other applications.

Infrastructure digital twin

We think the best paradigm for producing and capturing asset data is through collecting it into an infrastructure digital twin. Digital twins have made a lot of progress in a variety of industries. Most importantly, for the utility mapping industry, it should be noted that creating a digital twin is much easier than it was even just a few years ago. We think the time is right for an infrastructure digital twin and it can be just at the asset level for one particular company, but we believe it should be at a regional level. Cities, municipalities and regional governments around the world are beginning to develop comprehensive digital twins of all of their physical infrastructure; buildings, roads, trees, power lines, and subsurface utilities and they're pouring resources into it.

And we think that Canada and the United States should start doing the same thing. And for us in the utility industry, data collection costs are lower than they ever have been. There's clearly interest among stakeholders and governments to start dealing with infrastructure and utility mapping problems, particularly to lower the number and costs of strikes, injuries and the loss of life. We now know a lot more about how to create data from other data sets, using different types of analysis and artificial intelligence to piece disparate data sets together. Using AI machine learning in the cloud is much less inexpensive compared to just a few years ago. And nowadays one of the things that makes infrastructure digital twins readily accessible to the utility industry is the fact that storing huge volumes of data in the cloud has become very inexpensive.

What is infrastructure digital twin ? First of all, a digital twin needs to result in effective action. It's not just enough say we have a digital twin philosophy The purpose of having a digital twin is to result in effective action that improves your business. It needs to use real time and historical data, which is paramount for us in the utility mapping industry, and it's going to be motivated by outcomes. For us in the utility industry, it is to reduce and even eliminate utility strikes during construction and the associated risks of injuries and fatalities.

The infrastructure digital twin has to be stored and managed in a relational database so it can be queried, analyzed and improved on through time, and related to other data sets. Processes for collecting data and maintaining data must be simple so that we can break the barrier of restricting underground utility mapping to a specialized group of people. If we truly want to democratize and compile data from disparate data sets, we need very simple processes to get data into the database, analyzed and maintained.

An infrastructure digital twin should be captured and maintained in 3D, even though most utilities in the ground are within just a couple of meters of the top of the ground. The 3D component is vital, because part of analyzing a data set is that much ancillary data is 3D such as overhead power lines that come down the side of a pole into conduits below the ground,

Imagery that's taken looking straight down at the ground, like traditional satellite imagery, can be visualized on a map and incorporated directly into a GIS database. But other types of imagery such as oblique, 360-degree, street-side, and inspection imagery need to be treated differently.  Oriented Imagery has a range of possible applications. For example, a construction company might use it to review asset inventory photos taken by field workers at a construction site. A telecom company or electric utility would use it to examine close range inspection images of electrical and telecom towers from many different angles. Oriented imagery applications enable you to see exactly where the image is on the map as you pan or zoom in the image. There are spatial navigation tools designed to work specifically with oriented imagery. You can query oriented images using spatial and temporal filters.

Finally, it is very important to have standardized formats, including storage formats and cloud formats.  It improves our ability to inexpensively use machine learning to do associative analyses of disparate data sets to make a larger, more conflated data sets.

Automated feature extraction

Maintaining accurate, up-to-date, comprehensive data about assets, above ground and below ground, has been a challenge for utilities for decades. Now new technologies including machine learning are being applied to automate real-time maintenance of utility network information and provide a foundation for infrastructure digital twins.

Automated feature extraction has advanced dramatically in the last few years.  This is an example where high density LiDAR and ultra high resolution imagery was used on a very low, very small diameter gas distribution install from an open trench. You can clearly see the ease with which the LiDAR produces a 3D representation of all of the local utility infrastructure.

Much of the the advancement in in automated 3D mapping have derived from the autonomous vehicle industry which have trickled down to other parts of the GIS industry.  The hardware and software are vastly easier to use and produce vastly better data. You can see from these ortho images collected from our system and the LiDAR overlaid on it. Feature extraction was used to automatically define top of pipe, top of ditch and actual joints in the pipe. Automated classification of LiDAR removes the need for someone to go out there with a Pogo stick and find the top of pipe. It makes it possible to simply drive alongside and scan with a mobile LiDAR system an open trench containing a newly installed pipe to produce a detailed 3D model with centimetre accuracy without having a person present. It is much safer - no boots on the pavement - and it drastically reduces the cost.

We prefer mobile mapping systems as opposed to handhelds because mobile mapping systems produce a tremendous amount more data and its registration tends to be vastly superior. It is much safer because it eliminates a need for someone to get out of a vehicle - so no boots on the pavement. Anytime you're still getting out, whether you're using handhold laser handhold mapping system, or a Pogo stick, you still have a person outside the vehicle walking around a street. With a mobile system you eliminate that risk, and you also drastically decrease the time required to do the collection. Here is another example where automated feature extraction was used to identify the electro fused coupling. You can see the butt joints, you can see the Ts and so forth. All this can be done very simply now with automated feature extraction.

Data registers

While surveyors may have had experience with a provincial or other types of survey register, utility location data identified during routine locate (one call) operations in general is only used to mark the ground and is rarely recorded, digitally or otherwise. Practically this means locators going back to the same location they were at just a few weeks ago to repeat the locate operation for another project. People in the locate industry are very familiar with this and the  amount of schedule and cost expense it causes. Exposing people to relocate or remap an area that was just done not too long ago is doubling their risk. Secondly, because the data is not retained or kept in easily accessible ways, we can't analyze it.  For example, we might want to confirm that we have correctly identified the type of utility, the accuracy of its location, or determine if there have been changes in its location, type, or current status since we last detected it. And because the data is not being retained and centrally managed at a global level or at a large scale level, there's no real opportunity for organic data enhancements or improvements.

The CGA Next Practices Report to Industry has made the case that a comprehensive national GIS map of buried infrastructure is among the opportunities for systemic improvement with the greatest ROI for industry. The benefits of a national register are greater safety, enhanced construction efficiency; reduced design, construction, and maintenance costs; greater regulatory compliance; improved locate efficiency; fewer project delays and budget overruns; fewer service disruptions to the public, less traffic disruptions and loss of custom and other inconveniences for the public.

Sharing maps of underground infrastructure is not totally new to Canada. For example, the City of Calgary's Joint Utility Mapping Project (JUMP) system, the ICI Society in British Columbia, the City of Toronto's Digital Map Owners Group (DMOG) and others have been sharing underground utility information for years.

The Canadian Underground Infrastructure Register (CUIR) is a new national initiative started at the beginning of the year. The CUIR initiative is intended to enhance current practices in a way that enables all stakeholder groups to benefit from the initiative; construction contractors, network owners, government transportation agencies, design engineers, subsurface utility engineering (SUE) engineers, surveyors, locators, workers in the field and the public. The CUIR initiative has been inspired by the National Underground Asset Register (NUAR) project currently being implemented in the UK. The objective of NUAR is to develop a register of location data for all underground infrastructure in England, Wales and Northern Ireland that is accessible to all network owners and government agencies.

This post is based on Joseph Hlady's (Lux Modus) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

An infrastructure digital twin provides a dynamic environment for managing underground assets of a multi-utility campus

For organizations managing a campus such as educational facilities, healthcare facilities, commercial real estate, utilities, and municipalities managing public works, storm sewer systems, and parks which are characterized by having many different utilities and and other infrastructure, an infrastructure digital twin provides owners, operators and third-parties with a streamlined, cloud-based solution that maps, manages and shares a wide range of subsurface and above ground assets, including fibre, pipes, electric power cables, lighting, conduits, and other structures enabling campus asset owners to not only track their assets but to provide all the information about their assets; their history, their replacement cost and their current cost without which you cannot justify your financial demands, be it a budget request for next year or a bid bond for a major project.

Infrastructure digital twin

A major challenge for legacy asset management systems is that documents are hard to access. Problems in the field are hard to identify because it takes too long to access the relevant documents and other information in the field. It's hard to share architecture and utility drawings with contractors and other stakeholders. People are really copying large blueprints or as-builts and delivering a set of documents to a new contractor who's just been onboarded and the contractor has to spend weeks studying these documents and comparing this information to what's there, what's underground, what's above ground and matching it together.

The way we address this challenge is by creating an infrastructure digital twin which provides accessible information to all stakeholders through a subscription service. A full digital twin of your campus, is a big, costly project, but an infrastructure digital twin is simpler and much faster. It involves site verification and infrastructure mapping as a service including drone imaging, site surveying, walking the campus and verifying objects. If the customer is using some type of infrastructure detection, the accurate information from that system plugs into our system, so it remains live. We support Open Geospatial Consortium standards to ensure interoperability. We onboard all of the data and we do that for any new updates on the campus. If something is wrong in the map and it's not in the site, or in site and not in the map, or if the drone gave us a better accuracy, we detect that and make corrections. For example, customers often hire utility engineering firms to conduct subsurface utility surveys (SUE).  We load that information and match it with everything we have in the system about their underground and other assets. Full onboarding requires 40 days or less. And more importantly, all the data is owned by the client all the time.

We've loaded over four million assets for our customers. Those assets are being used by thousands of users, including facilities, teams, construction managers, engineering and architecture firms, local utilities, fire departments and others.

You can share data immediately with anyone you want. And there are no limits on the number of users in any of our systems. If you onboard a new construction company for a new project, you just provide them with a login, specify what they can access and see, and they are onboard.

Dynamic view of your assets

The system provides a dynamic view of all your assets. It's not a static image, GIS or CAD files which you update every several months. It's real time, live presentation of information coming from a database and it is integrated with all your other systems. You can drill down on every single conduit, cable, pipe, or other piece of infrastructure and query all its attributes and see the sources of the data. You have access to the data in real time from your phone, your tablet, or your laptop because it is a fully cloud solution. We are able to do that because we are compliant with the Open Geospatial Consortium (OGC) and follow OGC standards. We are using open systems in terms of representing all the attributes about every item.  For a typical campus environment, ranging from a few blocks to a college campus or hospital complex, for example, you may have 20,000 assets. All of those assets and their associated attributes are captured and live in that database. We're building IOT integration, so you will have real time data on top of their various asset layers. You can see the water flow and the temperature and the pressure from the sensors. To go back to the updates. We are building integration with augmented reality. so that all underground assets can be visualized in 3D AR.

We provide a dynamic way of having base maps. You can use Google Maps, Esri World Imagery, or OpenStreetMap or even a mix of these. You can add your own imagery. 

In addition to the dynamic view of the locations and attributes of all your assets, you also have a plan room. All your documents are there, your engineering drawings, as-builts, survey and SUE reports, legacy maps, and other sources of underground data. You can search them by date or by the contractor or surveyor who supplied this data to you. These documents are accessible to you at any time from any device.

Managing asset budgeting

More importantly, the system provides an executive summary of all your assets. For example, in this site, which we have built as an example (we cannot share real data publicly) we have included about $4 million worth of assets. FM people need to deliver projects on time, minimize disruption of services to those people who are using these facilities, and improve the sustainability of their facilities. To do this requires that you can justify your CAPEX and OPEX budget. For some capital projects, you might be able to look at the total cost for adding a new building. But for budgets for operations and maintenance, retrofitting a fiber network, changing your irrigation network, it's really difficult to do that without having access to everything about your assets, their history, their replacement cost and their current cost. Without this information you cannot justify your financial demands, be it a budget request for next year or a bid bond for a major project.

The key to managing budgets is by replacement costs. If you want to replace your electric network, you need to know how you built the electric network in the first place. The database provides access to all this information. You can break your assets down by type of structure; electric power, conduits, lighting, water, wastewater and gas pipe networks. You can see their life expectancy, aggregate them based on their expected time of expiry and then decide on remedial action. You also need to worry about the impact of potential accidents and most importantly your liabilities. If a major underground gas line is damaged, then you might face heavy liabilities because it might interrupt gas service not just to your campus but for the entire area or even an entire municipality.

Our system has an ROI calculator so you can compute your potential savings. This has helped customers secure grants because they were able to compute the information needed to submit a bid bond.

Conclusion

An infrastructure digital twin provides immediately accessible information to all your assets for all stakeholders through a subscription service. It's a real time, live presentation of information coming from a database and it is integrated with all your other systems. For critical budgeting operations and maintenance, you have immediate access to everything about your assets; their history, their replacement cost and their current cost that enables you to justify your financial demands, be it a budget request for next year or a bid bond for a major project.

This post is based on Moufid Charafeddine's (BetterGIS) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

ASCE standards reduce risk and expedite public highway construction projects

What the engineer does during planning and design has a multitude of impacts: traffic disruptions to the public, safety for workers on construction sites, avoiding delays and budget overruns on projects, decreasing risk to contractors, and reducing cost to the rate paying public. It really is a matter of public welfare and that's why the ASCE 38 and 75 standards are engineering standards. What we'd like to do is enter into a world where we we have as reliable information about the subsurface as we do about the above ground and the location of underground infrastructure is well understood in the design stages.  Then we can start applying modern 3D BIM design technologies, virtual design, and new other technologies to enable the construction process to proceed as rapidly as possible with the least amount of risk and rework. And that's going to save everyone money and a lot of headaches.

Regulating the public ROW

With respect to underground utilities the key idea is to avoid, accommodate and then relocate. This will require changing the mindset of agencies such as DOTs that should be regulating work in the public right of way. Currently many DOTs allow the utility operators free rein to build and operate in the ROW. They take a hands off approach saying it is not their responsibility, it's the utilities operators/owners problem. At this point it is generally recognized that this approach no longer works because everybody is accountable when things go wrong and a gas main gets broken and a building explodes. If we can accurately locate, map and accommodate underground infrastructure during planning and design, just as we do using above ground surveys, we expedite the project by taking the risk arising from underground utilities out of the critical path. By speeding up these jobs you can really save a lot of money on major construction projects.

ASCE 38-22 for SUE Surveys

In contrast to the 38-02, the latest version of the 38-22 standard for SUE investigations is both prescriptive and a performance standard. It is prescriptive in that it specifies the minimum actions that must be undertaken to achieve (or attempt to achieve) a particular quality level. As a performance standard it describes the professional judgment that needs to be exercised to determine the appropriate timing, sequencing, location and scope of a utility investigative effort to achieve the goal of reducing utility issues during planning and design by achieving Quality Level B or A for all underground utilities. ASCE 38-22, unlike 38-02, specifies levels of positional accuracy. 

We wanted to provide better definitions so that there's greater clarity in what constitutes a SUE survey.  The SUE survey must be stamped by a professional engineer and In addition be accompanied by an engineering report that goes with the submittals to help lay out what has been found, where issues and discrepancies were encountered, and the things you need to be especially careful about. It includes our recommendations going forward on the project, what you need to consider and  where you may need to do more advanced geophysical investigations or vacuum excavations to better define the design, where you need coordinate closely with utility owners, how to prepare construction documents and plan construction. The liabilities behind this document is significant. To protect the owner, the contractor, and the engineer whoever has provided the SUE data and service must be able to document what they did sufficiently to be able to stand up in a court of law and say, "We met the standard of care."

ASCE 75-22 As-installed

We define the spatial and the attribution properties of the facilities that we want to record, so that at the end of the day, we can actually create a digital twin in a user friendly format and consistent format. We have defined minimum, optional and conditional attributes and the minimum requirements, list the information we must have to create a digital twin or very useful 3D model of our infrastructure in a standardized manner.

In Montana we have a permitting system for work in the public ROW and we can collect these attributes and an approximate alignment at the time of applying for the permit. The person filling out the permit application understands the required attributes and can plug them right in. And then when the actual work is performed, the approximate alignment's upgraded to the actual observation via the survey data. ASCE 75 has been implemented in Montana for several years now and has won both Montana and national engineering awards.

We also support absolute spatial positioning and have have a framework for data exchange by having a standard datum. At the National Oceanic Atmospheric Administration (NOAA), this is the same thing we do for nautical charts and hazards to navigation. We're just applying it to hazards to excavation. Everything is referenced to the National Spatial Referencing System. That gets rid of all the distortion that happens when you're trying to take something on a sphere and flatten it on a flat plane. You get distortions, there's different projections, state plane, county coordinates, UTM and they all distort the image. By having everything referenced to the National Spatial Referencing System, when you bring it together, it's going to go in the same spot every time. And it accounts even for plate tectonics and crustal drift ln places like California, Whenever you're turned on your GNSS system, you're tied into the National Spatial Reference System. It is being upgraded and the next version should come out in another year.

ASCE 75 specifies levels of positional accuracy. There's an accuracy table that goes with the standard, which we basically piggybacked off CSA S-250, the Canadian standard. We have CSA S-250 people on  our 75 standards committee. They also told us three things that they wish they had included with CSA S-250; make it a digital standard, secondly make it 3D and thirdly include above ground infrastructure with the standard. So we made sure we brought those into ASCE 75-22.

The Open Geospatial Consortium (OGC) has adopted both ASCE 38 and 75 as important input to their Model for Underground Data Definition Initiative (MUDDI ). The MUDDI model provides a base conceptual data model for interoperability and data exchange.  It is being adopted world wide including by the NUAR initiative in the UK, the UNUM project in New ,and other jurisdictions.

Securing sensitive information

Data stewardship includes issues of sensitive and secure information.  Here in the U.S, we're using 49 CFR Part 1520, which talks about how to manage sensitive secure information that TSA has determined to be Sensitive Security Information, as defined in § 1520.5. (This part does not apply to the maintenance, safeguarding, or disclosure of classified national security information.) It's a big issue with many utility owners. They don't want that information available to everyone. The 49 CFR Part 1520 guidance defines what information needs to be kept secure and that has helped alleviate the data protection concerns of the utility owners.

Conclusion

With the imminent availability of the ASCE 38-22 and 75-22 standards, every underground utility segment that's depicted can be assigned Quality Levels based on ASCE 38-22 for existing infrastructure or from the ASCE 75-22 "As-installed" standard for newly installed infrastructure. For example, in Colorado both draft standards are mandated for all work in the public ROW. This enables a world where we we have as reliable information about the subsurface as we do about the above ground and the location of underground infrastructure is well understood in the design stages. Then we can start applying modern design technologies to produce better designs which enable the construction process to proceed as rapidly as possible with the least amount of risk and rework.

This post is based on Phil Meis' (UMS and ASCE) talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Combining traditional data with advanced GPR provides a complete picture of the subsurface

When we look at some of the major projects that have gone sideways, it often can be attributed to the lack of planning for underground infrastructure. This is a global issue and we know that the best way to manage the risk from underground infrastructure is to map it before construction begins. For example, the Sydney light rail project in Australia was problematic with a serious budget overrun because there was so many underground unknowns discovered during construction. Another example is a hospital project where what was thought to be a complete inventory of underground utilities missed a 20 inch gas pipe and a six inch gas pipe that were encountered on the project site during construction.  Neither was documented and they were not on the asset owner's records. The challenge is to find these assets before construction in the early stages of planning and design so as to be able to mitigate any project impacts and safety concerns. Very few major projects move forward without an environmental investigation and a geotechnical investigation, so it's not clear why there's resistance to doing a subsurface utility investigation when the immediate risk to the project and the public is considerably higher.

Legacy data

Legacy data is often proprietary asset owners' "as-planned" engineering records. Relying solely on these legacy utility asset records for location information is unreliable, but augmented by GPR and other remote sensing data they provide valuable information.

Modern GIS also cannot be relied on to always provide reliable data. This image is from a GIS with an xref in the background. The middle of the street is where the asset owner believes the water line to be, but a subsurface survey actually found lines on the top and the bottom side of the street, which is nowhere near the placement according to the asset owner's records.

Even more disconcerting is that some asset owners have stopped updating their records. In one example we encountered, the last update and revision to a telecom owner's documents was in 1998. Obviously by 2022, we know there's a lot more utilities added into these streets.

The challenge for practitioners is how to find this information.  Fortunately, advanced technologies, such as multi-channel, multi frequency ground penetrating radar, can augment the available legacy and EM data to provide a complete, accurate and current picture of the subsurface.

Underground locating combining traditional and advanced techniques

At DGT Associates our typical workflow starts with collecting and compiling legacy maps.  We collect all legacy data that is relevant including client information and all the asset owners' maps. We add our institutional knowledge from decades of experience with construction in the particular region where we're working. We then perform our traditional EM pipe locating and add that data to the legacy data we have compiled. Together this provides the background for our ground penetrating radar scanning.

Advanced ground penetrating radar

This is a picture of DGT Associates' new ground penetrating radar setup, a drivable rig with a mobile mapping unit. This is a 24 channel system, so it captures 24 slices every time we scan. It is also a multi frequency system. Low frequencies penetrate deeper with less resolution, while higher frequencies have better resolution but don't penetrate as deeply. This system can collect about 10 miles of data per day with 24 channels for a total of 240 miles of GPR data.  With Boston's soil conditions this system is collecting vertical slices down to about 8 feet. In addition with multiple frequencies we are simultaneously collecting horizontal slices. 

Value of GPR in subsurface surveys

GPR is not magic and is not replacing any of the previous techniques. What is the value of including advanced GPR in locating and mapping subsurface utilities ? The first thing is safety. With our rig we do not require boots on the pavement.  Secondly we get fast data collection compared to traditional locating. Thirdly, we can determine depths with GPR really well. When we see the top of the pipe, we're able to trace it in the cross section. GPR is also good at confirming the location of facilities shown on legacy maps or detected by EM. But GPR can't see everything, For example, we often can see a trench where there is likely a utility even if we can't see the actual pipe or cable. We typically need to determine from other techniques, legacy drawings or EM scanning, what type of utility we are seeing. 

A major strength of GPR is finding unknowns. Going through the depth slices, we can see each one of the lines representing a utility. The type of utility is derived from legacy and EM maps and is colour coded according to the standard with blue being water, orange telecom, purple unknown, and so on. Something that's really striking in this GPR scan is the purple utility buried only a foot and a half deep, crossing diagonally across the street. Nobody knew about this utility before we went out and scanned it with the GPR.

GPR data showing rectangular pits

One of the best examples of how combining GPR with legacy data enables us to detect unknown utilities, determine what they are and confirm they are still present is in Cambridge. We had compiled comprehensive legacy background information before we went out to do a complete GPR scan of the site.  Among the things we found were these unique rectangular pits, shown as purple circles (colour coded for unknown). Each pit is about one foot by two feet wide, and they're regularly spaced at 12 feet. If all we had was the GPR data, we would have no idea what these were.

Background legacy data revealing abandoned gas pipe

But because we had the legacy background maps, we were able to see that these pits line up  with old legacy data showing an abandoned gas pipe. Without the background map, we would have had no idea what the GPR scan was showing. The GPR scan confirmed the location of the abandoned gas pipe and showed that it was still there.

A typical subsurface survey deliverable is colour coded according to American Public Works colour coding. All of the lines are annotated to conform to the ASCE 38-02 quality standard. Every line is represented not only by its annotations for reliability, but also includes the number of conduits and other measures of capacity, and asset owner information. Together it provides as complete a picture of the underground assets for the project site as can be assembled from existing data.

Conclusion

The light rail in Sydney went from a $2 billion to a $3 billion job because of the lack of a comprehensive subsurface survey during planning and early design. There's plenty of proof including ROI studies that there are very good reasons to use SUE surveys. There are good practitioners all over the world who can carry out subsurface surveys. Just as with above ground surveys knowing accurately the location of underground utilities and other infrastructure enables good designs because you can make informed decisions based on information collected by professionals who know what is underground and where it is.

What we see in the coming years is that subsurface surveying is going to become even a bigger business and it's going to become more lucrative. We expect to see more practitioners and more investment in innovative technologies for detecting and mapping underground utilities.

This post is based on Mitchell Lidell's & Michael Twohig's (DGT Associates) talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Accurately locating and mapping utility infrastructure underwater

In New Jersey there was a $320 million project to replace a bridge connecting Stafford Township to Long Beach Island. There are many utilities involved including telecom, Comcast, water and sewer, and electric and conduits. They have carried out a SUE survey prior to construction to try to determine where the utilities are and have also come up with mitigation plans to reduce risk. 

However there was a complication. They need to build a new drain line because there has been increased flooding during the rainy season and also because of high tides. It is planned to build the drain line into the water parallel and fairly close to an existing water main. But the water main that provides water to the island runs under water and all they had for locating the water line was an "as-planned" map from the water company engineering department, which is not reliable data. Cross-boring into the water main would leave the whole island without water. Traditional locating equipment, such EMI and GPR, cannot be used under water so an alternative locating technology was required. 

PRISUM Technologies provides a technology called inertial navigation technology, gyro mapping or inertial measuring unit (IMU) that relies on a battery powered autonomous probe with on-board gyroscopes, accelerometers, and odometers. The mapping tool records changes in direction and speed at a hundred samples per second (every foot) to create an accurate, high frequency 3D profile of the pipe segment being traversed. Inertial mapping avoids the major limitations of surface-based EM and GPR techniques; it is not sensitive to soil conditions and extraneous electromagnetic noise, is water resistant, works with all types of pipe materials and is safe because it does not require boots on the pavement. It can map sewer and water mains, electric power or telecom conduits, gas and oil of any material, plastic or metal.  Pipe diameter can range from an inch and a quarter to 30 inches. The key to the technology is that the IMU is completely autonomous. There is no tethered cable and it does not communicate a signal to above ground equipment. All the data is stored within itself.  Basically it is comprised of a battery, memory and various inertial devices such as accelerometers and odometers in a compact device that can be pulled or pushed through a pipe or conduit. Once the IMU tool has been run through the pipe or duct, it is removed from the pipe and connected to a laptop to download and process the data.  The output is a report and a CSV file or a standard GIS format such as a shape file. Because of its autonomous nature, it can map utilities which are going under rivers or other obstructions, even a hundred feet or deeper. The IMU tool has the capability to map up to 5,000 feet recording X,Y and depth every one foot.  To georeference the collected data, accurate GPS coordinates are recorded at the insertion point and and at the end point.  With that information the entire path the autonomous unit travelled can be determined. 

In New Jersey the IMU was inserted in one end of the water pipe and pushed through to the other end and then pulled back.  This was repeated providing four runs. The data from the four runs showed a high degree of repeatability providing confidence in the accuracy of the data. According to the New Jersey DOT, the locating standard tolerance is plus or minus 2.74 feet horizontal and 1.09 feet in depth. The 3D mapping data collected by the IMU was much more accurate, 0.26 ft in XY, and 0.3 ft in Z.

This project entailed a one day activity where we were able to both run the IMU tool through the water main collecting data and also process and show the results to the New Jersey DOT on the same day. The overall operation time for the IMU run was two hours.

It was found that the water main actually had been installed at an angle.  If the team had not had the accurate location from the gyro mapping run, shown by the the red line, they would have risked cross-boring into the water main with major consequences for the project and the island's water supply. With the accurate location of the water main in hand, the team went back to the drawing board and redesigned the new drain line ensuring a safe separation from the water main.

This post is based on Santosh Saride's talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Developing a subsurface digital twin for Singapore requires data quality management

Singapore has severely limited land, but unlimited space and has started to build many types of activities underground to save the land above ground for the better living of citizens. Many of the traditional utilities such as electric power, water, wastewater and others, as well new type of utilities, such as the Pneumatic Waste Conveyance System are almost one hundred percent underground. Parts of the transportation network, such as the metro, are also underground.  In addition some storage, waste disposal and national defence facilities are underground.

Data quality

As underground development expands in Singapore reliable data about the location of underground infrastructure becomes increasingly essential. Data quality is a huge issue. Virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities.  From the perspective of planning and land administration, infrastructure development, and planning and design there are important benefits in the form of reduced uncertainty and risks to be derived from reliable underground data.  These include reduced costs, avoiding unnecessary rework, minimizing destruction to the environment, avoiding disruption of consumer services, and greater safety for people working in the environment as well as the general public in general.

To address this issue the Digital Underground (DU) initiative was started in 2017. The objective of DU is to develop a reliable map of Singapore's subsurface infrastructure for planning and land administration. The DU has four phases. The objective of Phase 1 was to identify the problem and create a road map. The second phase involved looking into what was required to create a utility mapping ecosystem. The current phase involves looking at the underground utility workflow which has many components that will contribute towards reliable data.  The final phase is the operational phase when we implement a new utility mapping ecosystem.

In Phase 1 an assessment was made of the reliability (locational accuracy, completeness, and currency) of the existing data. It was concluded that the data quality of the data is seriously deficient. In terms of location, it is  inaccurate, much of it is out of date, and it is also incomplete. It was also found that the quality level of the data is undefined, in other words, no quality level has been assigned to each facility for example based on a quality standard like ASCE 38 or PAS 128. We do not know which piece of data is of higher quality or which piece of data is sufficiently reliable to be relied on for planning and land administration.

Road map for improving data quality

Addressing data quality involves fundamental survey, locating and mapping issues. In addition to assessing data quality an objective of Phase 1 was to develop a road map for addressing the data quality issue. First we wanted to ensure that going forward data for all newly installed utilities would be reliable. In 2017 we implemented a strategy requiring certified survey quality as-builts. Secondly, we started to look for opportunities to improve the data quality of existing below ground infrastructure. We are looking at several new technologies, such as ground penetrating radar and gyro mapping for detecting underground utilities and LiDAR and photogrammetric capture with a smart phone of trial trenches. We are also exploring ways of capturing not just vector data, but integrating 3D point cloud data of an open trial trench in our GIS so the next person interested in this section of road doesn't have to open up the trench to see what's there.

A key part of the road map is governance, especially accountability and transparency; assigning responsibilities for data capture, data quality improvement, and data quality management. We want to introduce a new framework so that we can ensure high quality utility data ongoing in the future. We are suggesting a process that would not interrupt or change the current utility workflow, but add a new data quality workflow on top of the current workflow. Asset owners will be able to continue to do their work the way they have in the past, but will be required to submit their data to a Data Quality Hub for quality control to ensure that data quality standards are complied with. We intend to implement quality control on the front end when data is submitted to the database, not at the tail end of the information supply chain. Eventually this process is intended to ensure that we are getting reliable and high quality data that can be shared back again to the end user, the planners and land administrators.

Community of practice

Creating a utility mapping ecosystem requires buy in from network owners, transportation agencies, excavators, and others. The Digital Underground Connect is a platform where we provide people to meet, to learn and to improve.

 

This post is based on Victor Khoo's talk on the Singapore Digital Underground Project at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Standards-based subsurface data model key objective of New York's underground infrastructure resilience project

Improving disaster resilience requires accurate data about subsurface utility and other infrastructure. New York has has experienced several disasters over recent decades, foremost among them 9/11 and Hurricane Sandy. The experience of 9/11 and Hurricane Sandy showed that communities interested in addressing utility resilience currently have little access to underground infrastructure information, which is typically siloed in network owners/operators' proprietary systems, is rarely integrated with other data sets, and is inaccessible to the excavators, disaster planners, emergency responders and others who need it. The UNUM project, funded by the National Science Foundation, is designed to address the challenge of creating a single accessible map of all of New York's underground infrastructure to support emergencies and disasters such as 9/11 and Hurricane Sandy.

Underground infrastructure mapping in NYC

Over the past decades the City of New York has initiated several projects involving underground utility networks. The first project was the conversion of citywide paper maps of water mains into CAD format. The water supply map was near completion when the city began the development of the citywide base map in a GIS. The base map provided locationally accurate platform, including block, lot, street, buildings and photogrammetric imagery of the entire city. Since then most agencies have contributed their data to the base map project which is virtually all open and publicly available. The city's sewer paper maps were entered into the GIS and the water main maps were converted from CAD to GIS.  Both sewer and water main maps were integrated with the city base map, demonstrating the ability to use city's cadastral map data for underground mapping as well.

9/11 emergency

Within hours of the World Trade Center attack, an interim site was equipped to response to the emergency. The Emergency Mapping and Data Center was set up to respond to map requests. The map grid of the site was developed by the Fire Department of New York to enable first responders to navigate the site safely. In addition, maps were developed to advise citizens of street closing and outages of utilities. Maps were submitted to the media and newspapers with daily updates of these conditions for citizens. Thousands of maps were created and distributed within a six week period.The operation was nonstop for more than 18 hours a day for three months. The Deep Infrastructure Group (DIG) team digitized data of the World Trade Center site provided by infrastructure agencies and utilities. The digitized maps enabled first responders to navigate the site safely.  The combination of photogrammetry and LiDAR, revealed the location of fires burning underground. Freon becomes a poisonous gas when heated and locating the underground tanks enabled safe removal of the freon and the tanks themselves, preventing loss of life of workers on the site and residents in surrounding areas. This is a classic example of the value of knowing the location of  underground infrastructure.

The 9/11 emergency clearly demonstrated the value of compiling an integrated data set with a common base map that can be shared easily and quickly among all players. But the 9/11 experience also revealed the difficulty in achieving an integrated map with the currently available data sets and integration tools and was a primary motivation setting the directions for the UNUM project.

Unification for Underground Resilience Measures (UNUM)

To directly address the challenges experienced during these events the Unification for Underground Resilience Measures (UNUM) project was initiated to investigate subsurface data development, integration and interoperability options. Overcoming these barriers to data integration requires the design of an integrated 3D data system with robust security and data protection that is capable of integrating data from different network owners, municipal and other sources based on a standardized subsurface data model and a willingness to share data securely among all stakeholders.  One of the key goals of the the project is the development of a detailed road map for the implementation of a standards-based subsurface data model capable of integrating all of NYC's subsurface data from multiple sources into a single map.

UNUM involves two pilots in NYC neighbourhoods: Sunset Park, Brooklyn and the Grand Central Business Improvement District. These community test beds will be used to refine an infrastructure data integration strategy and to assess the vulnerabilities of utility infrastructure. The New York City pilot area are far apart in terms of demographics, diversity of their populations and their economic bases. They also differ widely in the types of underground infrastructure problems they have experienced.

Use cases addressed by UNUM

The UNUM project, which is supported by a National Science Foundation (NSF) grant, addresses emergencies and disasters that are related to underground infrastructure. Emergencies are typically characterized as localized incidents with limited damages while disasters are more wide ranging, cover a lot of area, cause a large amount of very expensive destruction. NYC has experienced two disasters in the last two decades: 9/11 and Hurricane Sandy.

There are many use cases that involve underground infrastructure. The UNUM project is focused on just two uses cases; emergency response and disaster planning and response. Other projects in NYC target other cases including excavations, excavations for new foundations, maintenance, smart cities, digital twin, and so forth.  For UNUM's use cases the data focus is on the subsurface utility networks, their features and attributes and their relationships to one another.

Quality and availability of subsurface infrastructure data in NYC

Over the past years extensive research has been carried out to determine what data is available in NYC and who is responsible for each data set.  It has been found that there is actually a fair amount of data. New York City itself, which manages the water and sewer utility systems, has very good data which is open and publicly available. In contrast for private utilities such as Con Edison, National grid and Verizon, knowledge about the structure, depth, and reliability (completeness, currency, and accuracy) of their data is uncertain because other than the through the 811 Street Markup (one call) system, there's very little sharing of this data. In general most of the underground utility and communications network data in NYC is within silos that are rarely, if ever, integrated into a single data set and are inaccessible outside of the networks owners themselves.

Single integrated map

The  The vision which motivated UNUM is a single integrated data set that can show all the different utility layers reliably (accurate, up to date, and complete) in 3D, together with other related GIS layers, registered to a common base map and georeferenced to a common, horizontal and vertical datum. This would reveal conflicts, interdependencies, and risks for cascading effects for NYC's utility network. Subsurface utility engineering (SUE) surveys are becoming more commonplace and data like this is becoming available for individual construction projects. This data is being captured in a GIS and compiled entire networks making it possible to see how different networks (electric, gas, water, sewer, steam, fibre,copper, etc) relate to one another.  Especially important are interdependencies such as telecom systems that require power, pumped water and wastewater systems, and virtually all utility systems that rely on digital communications because of the potential for catastrophic failure involving multiple networks.

An open standards-based model for underground infrastructure

To achieve the vision of a single map of New York's underground infrastructure will require developing a data model that encompasses all of NYC's underground infrastructure and that could form the basis for a subsurface digital twin for the city.  Bringing the relevant data together during 9/11 took weeks to do because the different data sets were not only in a variety of formats but each had a different data model. UNUM decided to take a standards-based approach and has adopted the Open Geospatial Consortium's Model for Underground Data Definition and Integration (MUDDI).  This model has the additional benefit that it is  is supported by the American Society of Civil Engineers (ASCE) 38-22 and 75-22 underground utility standards.  For NYC adopting the MUDDI model makes it possible to build on the work already done by other jurisdictions including Scotland, the Ordnance Survey of Great Britain, Denmark, Flanders, Netherlands, Singapore and others. Experience to date with the model has shown that the MUDDI model is comprehensive and able to describe all the key features that are found underground in NYC.

The illustration shows a conceptual picture of all the things that can be captured and integrated through the MUDDI model. At the centre are individual network features such as cables, pipes, valves, transformers, switches, and meters. Shown below the features it can be seen that each feature has attributes (age, material, location, dimensions, quality level, threat level, pressure, voltage, fiber capacity, etc) and lower right accessory features (access, support structure, container, serial#, etc). Underground facilities are surrounded by a natural environment (soil, geology, ground water, chemical contaminants). Together the network features, their attributes, supporting structures, and environment comprise the utility networks themselves.  Utility networks can be be represented in a GIS and related to other layers such as catchment areas, service districts, preservation districts, and so on.

New York intends to develop a detailed model (referred to as a profile) for the city's underground infrastructure based on the MUDDI conceptual model. The same conceptual model is being used in other jurisdictions. For example, in the UK the National Underground Asset Register (NUAR) project has created a UK profile for the excavation use case. Over the next few years based on this profile, all utility and communications networks in England, Wales, and Northern Ireland will be captured and integrated into a single map.

Conclusion

Improving emergency and disaster resilience requires accurate data about subsurface utility and other infrastructure. The 9/11 emergency clearly demonstrated the value of an integrated data set with a common base map that can be shared easily and quickly among all players. But the 9/11 experience also revealed the difficulty in achieving an integrated map with the currently available data sets and integration tools.The vision which motivated the UNUM project is a single, integrated and accessible map for New York City that integrates reliable location data for all of New York's underground utility layers in 3D, registered to a common base map and georeferenced to a common datum. To achieve this will require a shared data model enabling location and other data from different utilities and other sources to be integrated into a single map of New York's underground infrastructure  UNUM has taken a standards-based approach and has adopted the Open Geospatial Consortium's MUDDI model, also used by other jurisdictions such as the UK. The UNUM project is developing a road map for the creation of a shared data model encompassing all of the city's underground infrastructure based on the MUDDI model. 

This post is based on Rae Zimmerman's, Alan Leidner's and Wendy Dorf's talk on the UNUM project at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Real time underground utility detection and damage prevention during excavation

According to the Common Ground Alliance, excavation practices have been the root cause of between 30 - 50 % of annual damage incidents between 2015 and 2020. Now an innovative technology Live Dig Radar (LDR) has been developed by RodRadar which is designed to avoid damage by detecting subsurface utilities in real time during excavation is entering alpha testing of production systems with contractors and others.

Integrating GPR into excavation equipment

The Live Dig Radar (LDR) system integrates ruggedized Ground Penetrating Radar (GPR) antenna and electronics into a digging bucket that can be attached to excavating equipment such as backhoes. The GPR is connected to a rugged tablet installed in the operator's cabin. Immediate visual and audible alerts are provided in real time to the operator, providing him or her with usable information, including the position and depth of subsurface utilities detected during excavation. LDR can detect any type of utility including metallic and nonmetallic pipes, cables, communication lines in plastic conduits, and ducts. LDR's GPR is able to detect very shallow utilities, which is a known barrier for some state of the art GPRs.

How does it work ?

The process is simple; the backhoe operator sweeps the LDR bucket over the area where he intends to dig next. He gets immediate audio and visual feedback showing the location and depth of any utilities detected. If none are reported, he knows he can safely perform the next dig. Then the process is repeated until the hole or trench is completed or a subsurface utility is detected.

As an example, here the LDR operator screen shows that multiple utilities have been detected, two in this case, about 12 and 20 inches deep. 

Another example shows detection of fiber optics in conduits. The LDR detected in real time shallow communication lines in plastic conduits. These lines ran across a job site in a different position from what had been marked. LDR enabled the operator to detect and map these lines and change his excavation plan.

It is well known that radar penetration is affected by soil conditions such as moist soils and clay. But with LDR since it focuses on just the next digging area, the RF signal never needs to penetrate more than a meter into the ground so that soil conditions do not represent a practical limitation on the system. Experience with the LDR unit in very harsh conditions including wet soils and clay has shown that it is able to detect, for example, fibre optic cables buried shallowly in clay.

The hardware and software of LDR have been in development over the last few years. Rod Radar has worked In cooperation with some of the biggest excavation machinery companies to ensure that the ruggedized antenna and electronics are able to endure the harsh environment of excavations. LDR is now embarking on extensive alpha testing of the first production intent systems, which will be tested by contractors, utility companies, and solution providers in North America, Europe, Australia, and Israel

Conclusion

LDR is intended to be used within existing one call (811) and other excavation processes and does not require changes to these processes. What it provides is a final level of damage prevention by enabling the excavation equipment operator in the field to detect and avoid utilities in real time while he is excavating.

 

This post is based on Yuval Barnea's talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Subsurface digital twins: How Singapore plans to address data quality and accessibility challenges

TThe recently published Digital Underground: Imagining a digitally enabled future is full of insights derived from a four year effort by the Singapore Land Authority in developing a road map for a coherent national mapping strategy for underground utilities and, most recently, supporting an ecosystem to produce and deliver accurate and complete digital data to stakeholders..

The recognition that the quality of the existing data about underground infrastructure represents a major inhibitor to developing a reliable subsurface digital twin is a central theme. For example, the Singapore Digital Underground document states that "virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities."

Another challenge is accessing underground infrastructure data.  In many jurisdictions worldwide including Singapore records of the location of utility and telecom networks and facilities are often owned and maintained by private utility and telecom operators.  The result is that access to underground infrastructure data is often restricted and not available to all stakeholders on construction projects. Jurisdictions have addressed the accessibility problem through organizational structures that often involve government such as government mandated one call systems in North America and the DT DICT system in France. Systems that are not mandated by government have been developed, for example, the private one call system LineSearchBeforeYouDig in England and the industry voluntary Vault system in Scotland.

The Singapore Digital Underground project identified measures that it considers necessary to address these challenges. Current business practices for data production, data management, data sharing, and data use are largely dependent on individual land administrators, network owners, contractors and surveyors and are not consistent for all stakeholders.  The result is that every day significant effort and resources have to be invested to work around the lack of reliable information about underground infrastructure.

Data accessibility for all stakeholders

The foundation for addressing the data accessibility issue in Singapore is a new subsurface utilities data governance framework. Among other measures it provides a basis for making government and network owners' data accessible to all stakeholders. The UK's National Underground Register (NUAR) initiative has an analogous framework in the form of data sharing agreements that were signed among 40 or so utilities and government agencies during the pilots in the North East of England and Central London and enabled data sharing among all the participants.  Achieving a long term framework of this type for the nearly 700 network owners is a top priority of the NUAR implementation, currently underway in England, Wales, and Northern Ireland.

In Colorado enabling access to underground utility data initially involved requiring contractors working on public construction projects for the Colorado Department of Transportation (CDOT) to use a mobile+cloud solution developed for CDOT. The source of the data was SUE surveys and high accuracy as-builts mandated by recent state legislation and regulations. It did not include data of uncertain reliability maintained in utility and telecom GISs. Sometime later network owners with equipment and locators and others working in the state ROW were also required to use the same mobile+cloud solution providing all stakeholders on CDOT construction projects access to the same high accuracy data. 

Improving data quality

The biggest challenge with existing underground data, generally stored in network owners' utility GISs, is that the data is often inaccurate, out of date or simply missing. A key question for any jurisdiction attempting to create a subsurface digital twin is whether to rely on unreliable data from network owners' records or to create a new high accuracy database with new data, for example, from recent SUE surveys or survey-grade as-builts.

Some jurisdictions such as Vault in Scotland and the NUAR initiative in the UK rely on the network documentation captured and maintained by network owners. Utilities have processes for updating and improving the quality of their data. These are supplemented by the NUAR application support for feedback from the field - implemented as "observations" in the online application developed by the Ordnance Survey for the pilots.

Other jurisdictions such as Colorado have decided not to rely on utility and telecom GIS data but to build new high accuracy databases using the results of recent SUE surveys and survey-grade as-builts with known quality levels, defined by ASCE 38-22 and ASCE 75-22.

Singapore has had a contractual standard for survey-quality as-builts since 2017. The key provisions of the new data governance proposal are designed to improve data quality and to establish an authoritative single source of truth.  Specifically it introduces the concept of a national Data Quality Hub which is comprised of a consolidated underground infrastructure database and data flows that are designed to co-exist with Singapore's current utility data workflows but which enforce data quality management.  The Data Quality Hub requires the digital submission of all newly captured data on subsurface utilities, quality control of submitted data based on a common set of rules, and the consolidation and integration of newly collected and legacy data for legacy data quality improvement. 

Integrating new high accuracy data with legacy data

For jurisdictions with new sources of high accuracy data from new legislation and regulations that are developing high accuracy databases, a crucial question is whether to attempt to integrate the unreliable data from network owners into these new databases.  In Colorado, the data from the mandatory SUE surveys and high accuracy as-builts with assigned quality standards based on ASCE 38-22 and ASCE 75-22 that goes into the new database is also made available to network owners who can use it to correct and update their records, but are not required to.

Singapore's approach is to require all data going into the Data Quality HUB to pass the set of quality rules defined by the new data governance framework.  If combining legacy data with new data, for example, from SUE surveys or from surveys of recently exposed underground utilities, results in data that can pass the quality rules then it is expected that that data should be eligible to be integrated into the Data Quality Hub.

A second challenge is that most legacy data is 2D and does not include depth or elevation.  The Singapore Digital Underground project attempted to address this issue by suggesting several ways of generating 2.5D data from legacy 2D data, but none of these is capable of producing the reliable data that the quality rules would seem to require.

Conclusion

Singapore has recognized the implications of the problems resulting from inaccurate, outdated and incomplete maps of underground infrastructure and adopted a comprehensive approach for addressing data quality and access issues. Because of the approach that Singapore has adopted and the fact that the Digital Underground project has actively engaged the international community, many international jurisdictions are watching closely as Singapore moves into the implementation phase.