Between the Poles

Machine learning cost-efficiently creates high quality utility GIS data for smart grid applications

Many utilities' GIS contains only a small part of what is required for managing a smart grid.  Often even the data in the GIS is too unreliable to use for smart grid applications.  For example, utility GISs may not include which customers are connected to which transformer, or if they do include this information it is incomplete or incorrect.  Many utility GISs cannot identify the phase(s) each customer is using and they may not include network topology - what is connected to what.  Other missing data may includes street lights, distributed energy generation such as rooftop PV panels, the type and condition of poles, joint use of poles, and encroachment.  To correct these problems typically requires a foot survey which is expensive.  For example, a pole inventory costs at least $20 per pole.  At the Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, John Simmins of EPRI described the research he is leading at EPRI to use modern technology including machine learning to reduce this cost to pennies per pole using available imagery such as Google Streetview.

I began working in the utility and telecom sector in 1993, specifically in developing software for records management (called network documentation outside of North America), and at that time the magnitude of the AEC+geospatial cultural mismatch was just beginning to be appreciated by utilities and telecoms. Planners tend to use GIS tools, engineers and designers CAD tools, construction folks in the field paper CAD drawings, and asset managers FM, GIS or integrated tools. Fashioning AEC and geospatial data into an efficient data flow from planning through design and construction to operations and maintenance represented a challenge that remains a problem for utilities.  For many utilities the backlog of as-builts and updates waiting to be entered into the GIS stretches into months, with the result that GIS data is permanently out of date inhibiting management and field staff from relying on this data operationally.  The Between The Poles blog is over ten years old and one of the persistent themes from its very beginning in 2006 was the challenge of integrating CAD and GIS data in a common workflow (some examples; 2006, 2007, 2008 ).

There are signs that modern technology is beginning to offer a solution to the problem of developing and maintaining an accurate digital model of electric power grids.

I wrote recently about a utility in western Pennsylvania that intends to implement an Advanced Distribution Systems ( ), but realized that to this requires a network data model. Duquesne Light is typical of many North American small to medium electric power utilities.  It does not have an outage management system or an electrical data model which means it is difficult to connect customers to transformers and to determine upstream devices when customers report power problems.  Currently Duquesne Light's circuit maps are AutoCAD drawings.  Duquesne Light is moving to modernize its grid and plans to deploy an Advanced Distribution Management Systems (ADMS) including outage management (OMS), DMS and DSCADA.  But the ADMS will require developing a complete distribution electrical data model and to that in the traditional way can be expensive.

To quickly, safely and cost efficiently develop a network data model, Duquesne Light looked at several alternatives including reality capture technology.  A pickup truck was equipped with with side mounted photo and IR cameras and LiDAR laser scanners. The reality capture survey could be completed rapidly and safely, with no boots on the pavement  Actual development of the data model was completed in the office without the necessity of going to the field for quality checks because the imagery provided all the data that was required to create and verify the data model.  Duquesne Light found several significant advantages of the reality capture approach.  The first was asset accuracy - both location and asset attributes.  In addition the captured imagery could also be used for other purposes.  For example, it could be used for a joint-use audit.

But this is still basically a manual process requiring a human being to scan images.  At the Energy Summit John Simmins described his research using neural network technology to automatically identify poles and attached equipment from available imagery such as Google Streetview.  Normally applying neural networks requires large quantities of training data.  To reduce this requirement, John has evaluated "biologically inspired", forward pass, neural networks.  In this approach the algorithm first looks (foveates) for vertical things that have the characteristics of a pole, and then for the characteristics of equipment attached to the pole, for example, cross members, street lights, transformers and large cables.  From this analysis is possible to determine pole location, height and width, cross member position on the pole and projected length, street light support strut position and length, position of transformer and large cable connection locations.  The ultimate goal of the research is in addition to automatically detecting each element of equipment to identify the type, size and manufacturer of each piece.

This is not a simple problem and there is no silver bullet.  Using a standard neural network approach requires large volumes of training data.  For poles it requires images of poles with different types of damage under different lighting conditions and in different types of weather.  An Australian company SiteSee claims to be able to do this for communications towers and transmission pylons - identifying the type and manufacturer of each antenna or insulator on a tower or pylon and comparing it to the manufacturer's specification to detect signs of corrosion and other types of damage.  When what you are looking for has a distinctive geometry that differentiates it from the surroundings, a biologically inspired approach may provide a way of identifying equipment without large volumes of training data.  Another area where several different approaches have been applied to automate feature extraction is electric power transmission lines for vegetation management.

Creating a digital twin of an electric grid is essential for developing and maintaining a smart grid.  Utilities have been wrestling with this challenge for decades and in many cases their GIS data is still not much closer to what is required.  If it would be possible to use the technology that John Simmins is investigating or other technology to reduce the cost of improving the quality of their GIS data, it would accelerate the adoption of smart grid technology at many utilities.  In his presentation at the Energy Summit Jared Green described his research in using smart meter and AMI network data to map secondaries from the transformer to the home, identify sources of unmetered load, and use impedance data from smart devices to correct utility GIS data.  Given the history of the challenge to develop and maintain accurate GIS data at many utilities, these approaches represent a beacon of hope which may enable utilities to cost effectively develop a digital twin of their networks that is an essential prerequisite for a  smart grid.

Pilot for sharing location of underground utilities underway in UK

Every construction project involves devoting significant efforts to locating underground utilities prior to and during construction. But this information is rarely shared and the location of underground infrastructure is recaptured over and over again.  At the Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, Carsten Rönsdorf of the Ordnance Survey (OS) described UK initiatives supported by the OS to address the challenge of sharing information about the location underground infrastructure.   In particular, he described a pilot that has gone live in Sunderland (Newcastle on Tyne) in North East England that includes almost full water, gas, electricity and a great deal of telco data.

North East Underground Infrastructure Hub (NEUIH) is the overall initiative name for OS's and Northumbrian Water's underground work in the North East of England. The first phase of this project was a ‘Common Infrastructure Map’ covering a few small sample areas around Newcastle.  This combined map is the first step towards creating the NEUIH.  The current phase of the OS initiative is called ‘Sunderland sand box’ and implements a dataset that contains almost full water, gas, electricity and a great deal of telco data for the city of Sunderland.

In July of last year the OS and Northumbrian Water undertook to create a map of underground infrastructure for Newcastle on Tyne at the Northumbrian Water Group Innovation Festival.  The objective was to compile in collaboration with utilities, local authorities and partners a combined infrastructure map for Newcastle and its surrounding area including water, wastewater, gas, electricity, telecoms and other underground services. The project benefited from a spirit of cooperation - all local utilities and telecoms in Newcastle were happy to share their data and had vector data available to contribute.  The result was a database where each utility was steward of its own data which was shared through a hub. Interoperability was demonstrated with three different GIS systems consuming the combined underground data. The data could be ingested into GIS systems with internet access, regardless of format.    As part of this experiment in creating a shared map of underground utilities, contractors were brought in to actually dig a hole so that participants could experience first had what is involved in excavating to determine the location of buried utility assets.  The OS created a web interface to enable excavation teams in the field to access the data relevant to them via a mobile device.  By the end of the week it was possible to demonstrate a working system that allowed excavators to query the map of underground utilities and use this information during excavation and construction.

At the innovation festival a data sharing agreement was developed that all parties agreed to – Northumbrian Water, Northern Gas Networks, Northern Power Grid, Openreach, Durham County Council, Newcastle and Sunderland City Councils, Cranfield Soil and Agrifood Institute, British Geological Survey and Ordnance Survey. Access to different data layers was made possible through an API and desktop and mobile device front ends were designed that provided quick access to integrated underground and above ground data to planners and field engineers .

The NEUIH is expected to reduce the need for potholing - it has been estimated that the total cost of a pothole is on the order of $30,000.  It will reduce customer disruption by minimizing street works duration with utilities working together more efficiently.  Northumbrian Water Group has estimated savings of approximately £1m from a fully integrated system. These savings will come from a combination of reduced back office effort to produce safe working maps and plans, reduced costs associated with utility strikes and avoidance of abandoned jobs due to lack of information.

Next steps include a harmonized data model (such as the MUDDI model under development by the OGC).  The system will also support a feedback loop where the excavator in the field can update location information to correct errors for underground utilities that are encountered during excavation and construction.  Such a mechanism would be expected to lead to improved quality of location information over time.  The system will also make it possible to upload supplementary evidence such as open trench photos.

An important issue for any shared dataset of underground utilities is liability.  In the UK, as in North America, the liability for any damage to and resulting from underground infrastructure is generally the contractor's who is required to employ safe excavation practices.  In contrast Carsten reported that in Belgium where the KLIP system for providing location information about underground infrastructure has been in place for a number of years, legislation has been passed that shifts the liability to the utility owners.  In France liability is shared between the contractor and utility owners.

This is a remarkable achievement, thanks in large measure to the high level of cooperation among the local utilities and telecoms.  It will be interesting to see the quantified benefits realized by the shared underground infrastructure database.  Another important metric to monitor is how well the database is maintained.  One of the most important metrics is the backlogs of as-builts and updates.  The longer the queue of backlogs, the less people will rely on the information in the database.

EPRI curates open utility-related training datasets for deep learning applications

Automatic feature extraction or image classification using artificial intelligence (AI) algorithms has been successfully applied in several domains, for example, in recognizing landmarks, dogs, cars and other objects in consumer photos and detecting building footprints in high resolution satellite imagery.  The existing algorithms for doing this are often available in open source implementations and have been democratized to the point where they are available to anyone with a modicum of programming skill.  The challenge is creating and maintaining (curating) the training datasets that are required to turn the basic algorithms into models that can automatically recognize and classify different types of objects.  At the December Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, Andrew Phillips of EPRI described EPRI's role in the new world of big data which includes curating open training datasets for the electric power industry to be used by the AI industry including universities, startups and established software players to create applications for electric power utilities.

Deep learning is increasingly finding practical applications using different types of georeferenced imagery.  For example, convolutional neural networks (CNN) have been successfully applied to real world problems including detecting blocked canals, differentiating different types of roofs, identifying building footprints and recognizing transportation networks.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded. The basic process in creating these models is to find or create some training data, some data to test the model, and then play with the parameters to optimize the model for the test data. Google is pretty good at recognizing dogs, cars and other objects that are commonly found in consumer photos.  But if you present Google with photos of utility equipment such as a photo of a decayed wooden pole with an insulator precariously hanging off it or a cracked fuse, Google will not recognize these objects correctly.  It is not a problem with Google's algorithm, but a lack of training data - photos whose content has been "labeled" by electric power experts.

An essential piece that is required to open up the data and technology of deep learning to a broader audience is open labeled data, what has been called an "open labeled data commons". There are analogies with OpenStreetMap initiative - something like this is required for labeled data - it needs a repository of open data, tools to capture and manage the data, and a community around this to capture and curate that data.  For example, for automatically classifying land use in satellite imagery the System for Terrrestrial Ecosystem Parameterization (STEP) has 2000 manually labeled sites covering 17 different land cover types that can be used a training data for deep learning applications to land use.

EPRI has already been active in collecting large volumes of training data from members and curating training datasets that can be used for common industry use cases.   For example,  EPRI has pulled together dissolved gases data for 45,000 transformers from 27 utilities which includes a few thousand failures.  The data has been provided to AI vendors to train their models.  Some of the data has been held back to test the models to see how well the vendors have done.

In another experiment EPRI asked 12 experts to review inspection imagery to identify and rank problems with common electric power equipment.  Thousands of labeled images were provided to nine vendors including IBM, Intel, Harris and others to train their deep learning models.   EPRI held back 500 of these images for testing and have just completed running them through an evaluation process to see how well the models do in identifying problems.  For example, it was found that the best models could identify cracked and damaged insulators with 88% confidence (using Matthews correlation coefficient), but cavities in wood poles such as woodpecker holes could only be identified at the 50% confidence level.  The results have revealed more images are needed under different lighting conditions. EPRI is going back to its members to get more images. Another area of interest to EPRI is monitoring transmission lines including vegetation encroachment using imagery from BVLOS UAV flights. 

EPRI's strategy is to be the bridge to the AI community by making the AI community aware of use cases that are important for the electtric power industry, helping the AI community with the physics of electric power and supporting structures, helping its members understand how AI can be used in the electric power industry, and collecting and curating open training datasets to be used by the AI community,

Progress toward a unified model for underground infrastructure and geology

TNO, Geological Survey of the Netherlands

Information about the location, condition and interdependencies of the infrastructure and geology below our feet is poor or lacking.  Even though information about what is below ground is revealed by every excavation, it is rarely shared.  At the December Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, Josh Lieberman of the OGC presented an overview of the progress of OGC's underground information initiative, with the appropriate acronym MUDDI, which is intended to provide an open standards-based way to share information about the below ground.

Unreliable information about what is underground including infrastructure and supporting structures and their interdependencies; soil, sediment, fill, and interstitial water and air; and physical, biological, and chemical properties leads to risk of injury to workers and the public and produces a significant drag on the construction sector.   Josh quoted an estimate from the the Institution of Civil Engineers (ICE) that about 50% of cost and time over-runs on civil engineering projects are caused by 'unforeseen ground conditions. If we are going to optimize where and how we build, we need much better information about what is below ground at the planning stage. To give a practical example close to home, in the middle of the Ottawa LRT project, a huge sink hole which unexpectedly appeared in the middle of a major street in Ottawa was blamed for a significant delay in the project as well as serious traffic disruptions.

There are a number of data models for different types of underground infrastructure and geology which raises the question; why do we need another ? The answer is twofold; in the context of a smart city we need to be able visualize and analyze all this information together, not just one network at a time.  Secondly MUDDI is not an attempt to replace existing standards, but to build on and augment existing standards to create a unified model supporting multiple perspectives.

Use cases

The MUDDI project has identified several different broad use cases that the model is intended to support including routine street excavations (EX), emergency response (ER), utility maintenance programs (OM), large scale construction projects (AE), disaster planning and response (DP), and smart cities programs (SC). For each of these, several basic requirements that the model needs to satisfy have been identified.  For street excavations the requirement is location of all entities with high horizontal, medium vertical accuracy (2.5D) of underground infrastructure; for large construction projects, detailed 3D geometry of underground infrastructure and detailed 3D geology;  for emergency response, interdependencies between different networks; for utility maintenance, network topology and facility location and condition; and for smart cities the ability to monitor and relate streams of data from sensors.

To provide a way for the model to be used by different types of users, the concept of profiles has been introduced. Profiles have been used for other OGC standards and allow for different levels of complexity for different domains and applications. The proposed profiles include asset, excavation, emergency, planning and integration profiles.

Reference standards

The MUDDI model is intended to build on and be compatible with many existing reference/target models.  For infrastructure these include CityGML with Utility Network ADE (Application Domain Extension) , INSPIRE Utility Networks, IMKL (Information model for cable and pipes), BIM-IFC, Land and Infrastructure Conceptual Model (LandInfra), Singapore Underground Geospatial Model, PipelineML, Underground Pipeline Information Management System, CIM (Common Information Model), Multispeak, ESRI Utility Model, and GEOfeature.  For geology, the reference/target models are GeoSciML, INSPIRE Geology, GroundwaterML, BGS National Geological Model, EarthResourceML, GeoTOP, SoilEML, IFC Geotechnical Extension, MINnD, and BoreholeIE.

Next steps

Josh outlined the next steps in the development of the MUDDI model which include implementations, experimental and prototype deployments with real world data, and estimates of returns on investment (ROI).

Growing evidence of the benefits of an integrated BIM+geospatial full lifecycle approach to construction

Building information modeling (BIM) has been applied to design-build construction projects for many years. A growing number of countries are mandating BIM for public projects.  While the UK government has said that "...we know that the largest prize for BIM lies in the operational stages of the project life-cycle", until recently there has not been hard data to support this conjecture.  Similarly there has been only anecdotal support for an integrated BIM and geospatial approach for design, build, operate and maintain projects.   Now we are beginning to see data from real world projects that offer evidence for the benefits of an integrated BIM+geospatial full lifecycle approach for construction projects.

The McKinsey Global Institute estimates that the world will need to spend $57 trillion on infrastructure through 2030 to keep up with global GDP growth. This is a massive incentive for the  construction industry to transform productivity and project delivery through new technologies and improved practices.  McKinsey reports that large construction projects typically take 20 percent longer to finish than scheduled and are up to 80 percent over budget. Construction productivity has actually declined in some markets since the 1990s and financial returns for contractors are relatively low and volatile.  Construction has been slow to adopt process and technological innovations. R&D spending in construction lags most other industries - construction R&D  is less than 1% of total industry revenue compared to 3.5-4.5 % in the auto and aerospace sectors.  McKinsey & Company suggests that the construction industry is ripe for disruption and two of the technologies that it believes will be key in that anticipated transformation are geospatial and BIM. 

The UK Government as part of its building information modeling (BIM) initiative has said repeatedly that it expects the big payoff of a digital model will be during operations and maintenance, which typically represents 80% of the cost of a facility.  Companies who take on design, build, and maintain and/or operate projects find significant benefits from a full lifecycle BIM + geospatial strategy. For example, EllisDon in Canada considers BIM+geospatial as best practice for its PPP projects, Rijkswaterstaat pioneered design, build, finance, and maintain (DBFM) projects in the Netherlands, Crossrail, which is currently the largest engineering project in Europe, has adopted this approach, Malaysian firm PESTECH International Berhad employs this approach for build, operate and transmit (BOT) projects, and major U.S. construction firm AECOM is applying BIM+geospatial on large design, build finance and operate (DBFO) projects. But to date there has been little reported quantitative evidence supporting the benefits of this approach.  This is changing with several projects providing estimates of benefits including ROI of an integrated BIM+geospatial full lifecycle approach to construction projects.

Integrated BIM+geospatial full lifecycle approach to construction

On many projects information provided by the construction contractor is handed over months after completion of the project. It can take the operator of the facility a year to go through this information to find the information required to operate the facility. This period is what is called the "blind spot" corresponding to the time the facility operator is managing the facility with limited information. Since for many types of equipment the highest probability of failure is in their first month or two of operation - just during the period that the building operator often doesn't have access to information about warranties and extended warranties - this increases the risk of equipment and even facility failures. In addition there is a cost associated with just finding the information required to service equipment. A NIST study across several industries estimated that the cost associated with this is 23 cents/square foot which adds considerable cost to operating the facility.

A full lifecycle facilities information flow starts with a facilities information specification from the owner identifying the building information to be included as part of the final deliverables at the time of commissioning.  The facilities information specification is part of the contract between the owner and the designers and contractors who will construct the building.  The facilities information required by the owner's specification are entered into the BIM database during design and construction by the designers and contractors.  At the completion of construction the facilities information data collected by the designers and contractors and stored with the BIM model in digital form represents a key deliverable to the owner together with the BIM model.   The facilities information can then be used to populate the building manager's asset management/facilities management system database including links to the BIM model of the facility to identify where each element or piece of equipment can be found. Most of the spatial and non-spatial data collected during construction is useful during operations. 100 % of floor plans and most of the 3D model (geometry and associated data) are needed in operations. Some owners find that just the BIM model including geometry and associated data is adequate for managing the facility. Others augment the BIM model by a Computerized Maintenance Management System (CMMS) which adds financial, occupancy, maintenance history and other information.

In an award winning paper at a 2011 conference organized by Britain’s Association for Geographic Information (AGI), Ann Kemp, then head of GIS at Atkins Global, the design and engineering firm, asked the question ‘BIM isn’t geospatial — or is it?’ and then argued that integration of geospatial and BIM was essential to address the challenges of the 21st century.  The need to integrate geospatial and BIM has been gaining traction for some time.

BIM+geospatial integration provide greater value to projects that involve not just design and construction but also operations and maintenance.  A leader in this space, Rijkswaterstaat, the Dutch transportation authority, began offering design-build-finance-maintain (DBFM) projects a number of years ago which has motivated private Dutch engineering and construction companies to adopt an integrated geospatial+BIM approach to construction.

A few firms such as Parsons Brinckerhoff, Atkins Global, and several Dutch firms including Arcadis and Royal BAM, in the construction sector realized a number of years ago that BIM+geospatial integration provide greater value to projects that involve not just design and construction but also operations and maintenance.  For example, the firm Royal BAM Group nv/BAM Infraconsult adopted integrated BIM + geospatial because of market developments including more complex construction assignments and an increasing demand from customers for service provision throughout the entire life cycle of a project. 

Geolocation plays a key role in integrating GIS and engineering data and applications for optimizing asset management workflows.  One of the foundations for an integrated system of this type is Bentley's decision many years ago to geospatially-enable all of their products Bentley calls this capability geocoordination. It provides a location-aware foundation for workflows that require integrated engineering and geospatial data and applications.

Benefits of Integrated BIM+geospatial full lifecycle construction

Recently, Microdesk has reported case studies including hospitals, a medical research facility, an airport and a university. For each facility several use cases were included in the ROI analysis.  For example, dealing with plumbing leaks, electric power shutdowns, passing on tribal knowledge from senior staff to new hires, and conducting infectious risk assessment (ICRA/PCRA), and so on.  After introducing BIM models for facilities and asset management, FM staff were surveyed and asked whether they found dealing with a plumbing leak, for example, using BIM easier, the same or more difficult than the traditional approach. The analysis quantified the time required to resolve a plumbing leak before and after the introduction of BIM.

Two very interesting examples of the ROI analysis performed by Microdesk relate to maintenance operations in a hospital. Passing tribal knowledge on from the older generation of FM staff to the younger involves a junior FM staffer accompanying a senior staff member walking the building for about a month. This requires about 200 hours of staff time. The hospital hires about 12 people per year so this amounts to 2400 hours per year. With BIM and virtual reality (VR) this was reduced to 22 hrs per person, a very significant staff time savings.  In another example, a review of infectious risk assessment (ICRA/PCRA) analysis in a hospital takes about 152 hrs and this has to be repeated 200 times per year. With BIM the time required to conduct these analysis was reduced to 74 hrs per review saving 15,600 hrs annually.

At the conclusion of five years running on the projects, the ROI analysis found a positive BIM in all cases, and estimated that introducing BIM for FM saved on average 5 % of operating costs per annum. It was estimated that introducing BIM reduced the time looking for things by 83% of the 23 cents per square foot cost for doing this from the NIST study. Since operations and maintenance are roughly 75% of the total cost of a facility, these results represents a substantial savings over the full lifecycle of a building.

The Napgur Metro, which is a US$1.3 billion project underway in Nagpur, is the first project in Asia to integrate a digital twin with an asset management system to eliminate information loss about assets during design and construction.   A 23 digit asset code was used to link 3D models developed using Bentley BIM tools with SAP financial management making it possible to track assets right from design through to operation and maintenance. BIM best practices followed the British PAS 1192 guidelines. Since the Nagpur system implements a full-lifecycle approach to project management, the location of each of the 500,000 assets comprising the systems is recorded making it possible to click on an asset in SAP and be shown the location of the asset in a 3D map.  Final deliverables are digital models rather than paper drawings providing a basis for digitalizing operations and maintenance.  The benefits of a 3D BIM+geospatial approach have been projected based on a 25 year lifetime for the project.  It is estimated that this will result in US$400,000 savings during plan, design and build, a reduction in operating manpower requirements by 20%, and increased availability and reliability.  The really big payoff is an estimated savings of US$222 million over the lifetime of the project.

 

PESTECH International Berhad, the winner of a Year in Infrastructure award, used geocoordinated digital design for the development of a new substation Kratie and Kampong Cham, Cambodia.  The decision to use geocordinated design technology for this project was partially motivated by the requirement that the company would not only be responsible for designing and building the substation but also for operating it for a period of 25 years before transferring it to the utility.

 

A number of projects in the Netherlands have been initiated by Rijkswaterstaat (RWS), the Dutch highway authority, using a design-build-finance-maintain (DBFM) approach. The Rijkswaterstaat (RWS) board is convinced of the benefits of BIM and BIM/geospatial integration and rolled out BIM/geospatial for four DBFM projects with the intention to require BIM on all DBFM-contracts in the future. Royal BAM Group nv / BAM Infraconsult was responsible for several of these projects.  A DBFM project at BAM starts with 2D geographic data that is visualized through a GIS web portal. The web portal gives everyone on the team an early perspective on the project site including underground utilities and aboveground structures before design begins. During the tender process the geospatial data is combined with 3D data including 3D representations of nearby natural and man-made features as well as proposed designs to create a 3D environment for assessing alternative designs. The 3D environment improves collaboration among the team members as well as enabling better communication with the client through 3D visualization of proposed designs. After award, the first step is conducting LiDAR scans of the site/proposed route. At completion of construction, the data collected during design and construction is migrated to an integrated GIS + asset management system system to support maintenance activities. 

 

In Canada public–private partnership (P3) have been remarkably successful for building and maintaining infrastructure. For EllisDon, a major construction and building services company, integrated BIM + geospatial is considered best practices on full lifecycle projects P3 projects in Canada.  EllisDon was involved in its first P3 project in Canada fifteen years ago. Since then the P3 business has grown and now P3 is a big part of EllisDon's business. For P3 projects EllisDon builds a wide variety types of structures including hospitals. In the P3 world the contractor often shares the risk of operating and/or maintaining a facility for an extended period. This means that people who are going to be running and managing a facility need to be involved in designing the facility right from the beginning. It is necessary to cost out the lifecycle of each piece of equipment whether it be a carpet, baseboard or cooling unit because you have to be able to maintain it and at the end of the 30 year period you are expected to turn over a hospital that looks brand new. For this reason P3 projects are very different from design+build projects.

 

AECOM, which is a US$18.2 billion a year firm in the construction sector and has been ranked for eight years running it has been ranked #1 in Engineering News Record‘s “Top 500 Design Firms”, uses BIM + GIS on design, build, finance and operate (DBFO) projects around the world.  At AECOM, the discussion has shifted from CAD/BIM alone to include geospatial and its capabilities specifically for design, build finance and operate (DBFO) projects. This mirrors the Dutch experience with design, build, finance and maintain (DBFM) projects encouraged by the Dutch highway authority. AECOM has found that by adding real world coordinates they increase the value and types of data for all stakeholders across the entire lifecycle. Integrated geospatial + BIM connects data types together. BIM holds the utility content and GIS is a visual platform for maintenance linked to a maintenance workflow. AECOM has applied this approach to the external campus of Denver International Airport, and to lease management at Orlando, Hong Kong, and South West Florida international airports. For the West Kowloon Cultural District (WKCD) in Hong Kong AECOM developed and maintains a central repository with district-wide data from various sources including all operational data - architectural planning and engineering- for the district. The advantage of an integrated BIM+GIS approach based on a centralized integration of information is that it allows the client to make strategic decisions during the design, build and operate phases of the construction lifecycle.

BIM+geospatial interoperability

Fashioning AEC and geospatial data into an efficient data flow from planning through design and construction to operations and maintenance represented a challenge that remains a problem for owners.  The Between The Poles blog is over ten years old and one of the persistent themes from the very beginning in 2006 was the challenge of integrating CAD and GIS data and applications in an efficient workflow (some examples; 2006, 2007, 2008 ). It is indicative of the importance of the integration of BIM and geospatial data and technologies that Autodesk and ESRI, 800 lb gorillas in their respective areas of AEC and GIS, have announced an agreement (for the second time) to collaborate to help bridge the gap between CAD/BIM and GIS/geospatial. 

 

Over the past decade, there has been impressive progress in developing open standards for the integration of geospatial and AEC (architecture, engineering and construction) views of city infrastructure which provides a standards-based basis for full lifecycle management from design through to operations and maintenance of infrastructure projects. 

 

The Netherlands has led in the development and application of open BIM+geospatial standards‎ in the construction industry. The Netherlands has created a BIM Gateway that is intended to be a national portal for information about open BIM standards including related geospatial standards. 

In the AEC world Industry Foundation Classes (IFC) are the open and neutral data format standard for the exchange of building information models (BIM) that is widely used in the AEC (architecture, engineering, and construction)  sector.  The IFC standard was developed and is supported by buildingSMART International.   The IFC BIM standard is used in the construction industry in many countries and is mandated in countries such as Finland.  The first versions of IFC were intended for buildings (vertical BIM).  Recently buildingSMART initiated the IFC for Infrastructure project to extend the IFC standard to support linear transportation infrastructure (horizontal BIM or BIM for Infrastructure) such as rail, road, bridges, and tunnels. 

In the geospatial world a widely used international geospatial standard for cities is CityGML.  Thomas Kolbe and co-workers are the developers of the CityGML standard that has been adopted by the Open Geospatial Consortium.  Kolbe et al. are translating a vision of the city as an interactive system comprised of functional components, utility networks connecting components, and interdependencies between utility networks into standards-based intelligent models that can be used to analyze urban environments for a variety of purposes including risk- and disaster management energy consumption, carbon balancing and city life-cycle management.

Currently CityGML includes 3D geometry, topology, semantics, and appearance for urban environments. CityGML also supports a standard mechanism for adding extensions, called Application Domain Extensions (ADEs).  There are several Application Domain Extensions (ADEs) that have been developed to extend CityGML to other domains.  For example,  I blogged about a basic extension UtilityNetworksADE that was proposed for city utility networks.  The INSPIRE Data Specifications Annex III contains use case encodings and a data model for Buildings the development of which was strongly influenced by CityGML. There have been attempts to integrate or at least develop a mapping between IFC and CityGML. 

A major breakthrough in bringing the architectural and geospatial views onto a common footing is the OGC LandInfra Conceptual Model developed by the OGC in cooperation with buildingSMART International and approved as an OGC standard in August, 2016.  LandInfra  was developed by Bentley Systems, Leica Geosystems, Trimble, Australian Government Department of Communications, Autodesk, Vianova Systems AS, and buildingSMART International and provides a unifying basis for land and civil engineering standards including the OGC's InfraGML and buildingSmart International's IFC for infrastructure standards.  

buildingSmart International's IFC-Alignment project uses this common conceptual model of alignments for roads, railways, tunnels and bridges.  The objectives of the IFC-Alignment project is to enable the exchange of alignment information through the full infrastructure lifecyle from planning through design and construction to asset management. On the geospatial side  InfraGML is the OGC's application schema supporting land development and civil engineering infrastructure facilities.

Open source support for AEC/geospatial interoperability

Where there are recognized open standards, there are more often then not open source projects supporting those standards.  mago3D is a new open source 3D geo-platform.  It is open source and integrates many well-known open source geospatial products and APis.  The importance of this technology is that it shows that it is possible using the available open source, geospatial APis and tools - together with some genuine innovative development - to create an open, non-proprietary 3D geospatial platform for integrating geospatial and BIM. Given the critical importance of addressing the cultural and technical divide between the AEC and geospatial worlds, a viable open source alternative is essential for developing the innovative solutions to the challenge of interfacing the two worlds. 

Bentley has announced that it intends to build its products supporting digital twins around an open source project already on Github called iModel.js.  Bentley is committing its resources to developing an ecosystem around iModel.js. To quote Keith Bentley this is a huge change in direction for Bentley although Bentley had puts its toes in the open source water previously.  Bentley used open source geospatial components to develop a new front-end for its leading road and rail asset management system AssetWise Lifecycle Information Management solution (ALIM).  Bentley has also partnered with AGI to support the open source Cesium project.

Conclusion

As owners see the advantages of design, build, operate and/or maintain projects and are beginning to changing their procurement practices, construction companies are changing their  business processes to optimize facility maintenance and operation.  Construction companies that have taken on the challenge of design, build, operate and maintain projects are realizing that there are significant benefits from an integrated BIM+geospatial approach to full lifecycle construction.   The advantages of an integrated BIM+geospatial approach have remained for the most part company internal.  Now it appears that the word is getting out.  Standards organizations in both the AEC and geospatial worlds are making progress on BIM+geospatial interoperability.  Major software vendors Autodesk and ESRI have announced an agreement to partner for greater interoperability between their products.  The open source community is also addressing the issue of BIM+geospatial interoperability. We are now beginning to see data from real world projects that offer quantitative evidence for the benefits of an integrated BIM+geospatial full lifecycle approach for construction projects.