The process of digitizing physical environments is called reality capture, and it comes with some challenges that every AEC professional should understand. This toolbox outlines common reality capture technologies, how to select the right method, and basics concepts of 3D modeling and documentation.

Your download is sponsored by Hawk Ridge Systems.

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The infrastructure industry has no shortage of engineering challenges. As we move closer to the midpoint of this decade, resources, workforces and supply chains are all strained to their limits. Nonetheless, aging assets and growing populations are producing demand and investment into infrastructure — causing a backlog. Meanwhile, new projects are becoming more complex and are taking longer to complete as societal expectations shift towards smarter, greener, optimized and even AI-connected structures.

Julien Moutte, chief technology officer at Bentley Systems, explains that engineering firms can overcome many of the current engineering challenges and gaps by gaining what he calls “infrastructure intelligence” — the capability to leverage data from engineering technology, information technology and operations technology (ET, IT and OT) to improve project delivery and asset performance. “Increasing infrastructure intelligence starts with data. By unlocking data from silos, sharing it with all teams and leveraging it into daily workflows, organizations can gain infrastructure intelligence. Additionally, data can be reused in multiple projects by creating libraries of pre-made components or generating components automatically with artificial intelligence. Data is the foundation on which digital twins can be constructed.”

A digital twin is a model and/or collection of information that is continuously updated to correspond with a real-world asset. The data is integrated into one platform, accessible throughout an asset’s lifecycle and updated automatically. This smarter infrastructure framework helps stakeholders optimize projects for resources, workforce, supply chain, maintenance, operations, future proofing, energy usage, water usage and much more.

Moutte notes that Bentley has worked with industry leaders that are ahead of the curve and reaping the benefits of digital twins. Many are breaking down silos by creating digital twins that span an asset’s lifecycle. Here are three infrastructure examples he was able to share.

 California automates its second largest dam for safety monitoring

California’s New Bullards Bar Dam, operated by the Yuba River Development project, sought to modernize its dam monitoring system by collecting continuous, real-time operational data.

Since the dam is located in an isolated area that experiences frequent earthquakes and inclement weather, assessing the dam’s health is a high priority to maximize safety, power generation, water supply, fishing and more. However, the current costs and risks to collect this data were unsustainable.

The dam holds back 1.19 cubic kilometers of water, forming the New Bullards Bar reservoir. So, if the structure were to fail, it could be highly dangerous to several local communities and wildlife downstream. This means dam operators needed an easy, automated, affordable and safe method to assess the dam.

Yuba Water worked with Niricson to capture a 3D reality mesh from thousands of drone-captured images and process it in Bentley’s iTwin Capture. Yuba then uploaded the photorealistic model to Bentley’s cloud-based iTwin IoT platform, where the model was associated with the monitoring devices to visualize the sensor data in real time.  

Collecting this data wasn’t enough; dam operators also wanted the data to be accessible and processed into useful information. The best method to do this was with digital twin technology. The digital twin enables users to visualize, analyze and gain automated decision support with thorough dashboards and reporting on structural integrity and reliability of the dam.

Moutte added, “iTwin IoT incorporates sensor data within the model so that Yuba Water can view the sensor locations within the geospatial context of the dam, determine if they have reached any alert thresholds and monitor deformation and propagation of the dam structure.”

The system now collects 1,000 times more data than previous methods. Using this data, the digital twin can manifest a 3D photorealistic model of the dam, which can be assessed easily and safely.

 EchoWater’s wastewater digital twin helps reduce Sacramento’s drought

Digital twins don’t just improve inspection projects, they can also be a big help when it comes to construction. For example, the Sacramento Regional Waste Treatment Plant, operated by Sacramento Regional County Sanitation used a digital twin to upgrade their facility on time and $400 million under budget.

Project Control Cubed managed the ten-year project’s planning, scheduling and cost. It was also the company’s idea to use Bentley’s SYNCRO to simulate construction, ensure safety and improve efficiency throughout the project. The digital twin was built using iTwin Technology to synchronize the changes to the physical site with the digital model. This helped to optimize situational awareness and decision making.

“Enhanced situational and logistics awareness provided by SYNCHRO’s digital twin created actionable intelligence that reduced cost and risk early in the design phase. Compared to previous projects, the quality and timing of the baseline schedule review and acceptance vastly improved, taking weeks instead of months. The digital twin streamlined contractor and stakeholder coordination and optimized construction sequencing, reducing overall program costs by USD 400 million.”

This information was essential to synchronize the 22 projects running on the site simultaneously. In the end, dozens of concrete structures, pump stations and electrical stations were made. This took 40,000 tons of steel and 225,000 cubic yards of concrete. To continuously optimize construction and anticipate issues, production and supply chains were simulated and choreographed through the digital twin.

Because everyone, from designers to contractors, could access the twin at any time, data siloes, and handoffs were eliminated. This also contributed to stakeholders finding issues earlier in development. This enabled Project Control Cubed to avoid and mitigate obstacles and shutdowns, saving time and money.

 Traffic to mountain resorts reduced along the I-70 using visualized data

Digital twins can also be used to optimize infrastructure during the design phase — as was done for Colorado’s I-70. The highway connects many popular resorts and acts as a major east-west artery for the trucking industry. This has led to the route becoming congested and dangerous at various locations.

To address this, the Colorado Department of Transportation hired AtkinsRéalis to engineer and design a $700 million project to address I-70’s bottlenecks, access, safety and environmental impact.

The mountainous topography, tight corners and waterways presented considerable design challenges for AtkinsRéalis. However, using digital twin technology, they were able to design a new tolled express lane, auxiliary lane and frontage-road (for emergency response). Bentley’s ProjectWise technology was used as a common data environment for all stakeholders, eliminating data silos and simplifying communications. The digital twin itself was built using iTwin; geometries were made in Bentley’s Open applications—OpenRoads and OpenBridge.

The digital twin also included 3D models which made it easier for stakeholders to intuitively understand how the highway would look in the 3D world. LumenRT was used to produce 360-degree static and video visualizations, which were key to getting the public onboard.

“The community is often a key stakeholder group,” says Moutte. “By showing realistic 3D models, stakeholders can easily visualize the designs, which reduce the overall risk of the project.”

Overall, the digital twin saved 97% of the effort needed to share data on the I-70 redesign. This reduced work hours by a total of 50,000 and saved a total of $7 million. As the project is set to finish in 2028, AtkinsRéalis and the Colorado Department of Transportation can expect to see more savings throughout the project’s lifecycle.

Open, scalable digital twins are the future of infrastructure

Digital twins connect the virtual with the real world through multiple data sources like scans, photogrammetry, lidar, IoT sensors and various software or engineering platforms. But Moutte believes that we are only just scratching the surface benefits of digital twins. Soon we will be able to extract much more data by leveraging open standards and interoperability, where users can integrate external data sources, third-party tools and their own analytics directly into a digital twin.

“We believe open data ecosystems allows data to flow freely, tools to be combined for more thorough analysis, and reusability, and allows infrastructure teams to work more efficiently, make more informed decisions and ensure long-term value.”

Bentley believes an open, scalable digital twin platform that ingests and integrates data from various sources and disciplines will enable infrastructure professionals to make more informed decisions at every stage of the asset lifecycle—from design and construction to operation and maintenance. This open approach drives innovation, fosters collaboration and enables users to break down data silos, leading to more efficient project delivery and improved infrastructure performance.

Digital twins are an important tool in the future of the infrastructure industry. To learn more, visit Bentley’s website here.

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Regardless of terminology, an accurate depiction of existing conditions is crucial for designing new projects. The information helps architects and engineers design facilities that fit into the surrounding area and meet the needs of people using the facilities.

Information included in existing conditions surveys

An existing conditions survey establishes locations (typically 3D, but sometimes 2D) of key features related to the project and other related information, including the following:

• The overall shape and elevation of the land in the area of interest — often referred to as the topography. This might include 3D point data identifying high points, low points and other key locations to represent the terrain, along with contours identifying areas of equal elevation.
• Streets, roads, driveways, sidewalks and other paved or graded areas. This typically includes 3D data defining the elevations of key facilities to which the new facility might connect.
• Permanent structures such as buildings, walls, bridges, fences, and other structures, with 3D data identifying building floor elevations, wall and bridge surfaces and other key elements.
• Railroads and other transportation facilities that may be located near the project.
• Lakes, rivers, streams and other water features on or near the area of interest. Wetland limits may be identified if delineated by qualified experts. Floodplain information, if available and applicable, may be added based on records.
• Trees and other vegetation. In some cases, tree sizes are identified by trunk diameter.
• Observable evidence of wells, soil borings, landfills and other subsurface features.
• Above-ground utilities, including power poles, light poles, overhead wires, guy wires, anchors and vaults.
• Underground utilities, including water, sanitary sewer, gas, electric, cable television and communications. Information may include surface features of underground utilities, such as valves, fire hydrants, meters, manholes and access structures, as well as marking information from the utilities or utility location services. In some cases, subsurface pipe elevations are obtained via access structures.
• Drainage facilities, such as storm sewers, combined sewers, culverts, access structures and other components. 3D information indicating sizes, flow directions, and elevations of pipes or culverts is typically needed to design new facilities. Pipe materials are also often identified during the survey.
• Visible rock formations or other geological features.
• Benchmarks used to conduct the survey.
• Boundary information for the property of interest. This might include physical markers such as property corners located in the survey, as well as approximate information collected from records showing property lines, rights-of-way and easements. An existing conditions survey should not be confused with other types of surveys, such as boundary surveys or title surveys, which must comply with certain standards.
• Other items as needed for particular projects.

The level of detail may vary significantly depending on the project. In some cases, information from other sources may be included with existing conditions surveys, though this requires additional attention to maintain consistent accuracy. For example, if information from a regional topographic map is being considered, it may not have the same level of accuracy as the project survey, or it may be based on a different survey datum.

How is survey information collected in the field?

Information may be collected in several different ways, as discussed in a separate article:

• LiDAR (light detection and ranging, or sometimes laser imaging, detection and ranging): Ground-based or aircraft-mounted lasers take multiple measurements encompassing the area of interest, establishing large point clouds of data.
• Laser scanning: Ground-based laser scanners focus on specific buildings, mechanical systems or other objects to build detailed 3D models of those specific items.
• Photogrammetry: Aerial photography and stereoplotters are used to analyze two or more photographic images taken from different positions to determine 3D coordinates of select points and create topographic maps.
• GNSS (global navigation satellite system): Satellite data is used to provide a wide range of geospatial information. The U.S.-operated global positioning system (GPS) is one of many GNSSs in the world.
• Conventional survey equipment: Tape measures and instruments such as theodolites, levels and total stations are used to establish locations at key points.

How do existing conditions surveys inform design work?

Existing conditions surveys often serve as the starting point for design work, providing a base map for designers in establishing initial design concepts and advancing designs into construction. Some examples include:

• New buildings: Surveys help determine the location and sizing of new buildings of various types, such as residential, commercial, industrial, government and healthcare. New structures typically need to be located within certain boundaries and sited at elevations that enable appropriate access, provide adequate drainage and meet applicable building codes. Building designs also need to consider locations of natural features, existing utilities and other improvements in the area.
• Building renovations or additions: As with new building projects, surveys for renovations or additions identify applicable key features, particularly existing structure locations and other factors that influence the design of additions or improvements.
• Infrastructure projects: Roads, bridges, utilities and other infrastructure projects require detailed information on existing conditions to enable proper design and integration of the new facilities. Facilities need to tie into surrounding property and consider topography for proper drainage and operation of gravity systems such as sewers. Topography may also determine locations of pipes and underground facilities to meet certain depth or cover requirements.
• Land development projects: Surveys establish overall site topography and determine potential subdivision of property into smaller parcels or lots, as well as potential building pad locations.
• Preservation projects: Surveys of historic buildings and sites provide documentation to guide restoration and maintenance efforts.
• Facility management: Surveys help facility managers maintain accurate records of facilities, guiding ongoing maintenance, repairs and future upgrades.
• Real estate: Surveys provide key information for assessing property conditions before completing transactions.

The importance of existing condition surveys cannot be overstated. An inaccurate or incomplete survey can lead to later issues, such as location conflicts with existing structures or utilities. A thorough, accurate survey can guide the project throughout all stages of design and construction and also provide valuable information for conducting later surveys, such as construction surveys and as-built surveys.

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For example, a door may be moved on a building project. A drainage structure on a roadway project may be relocated to avoid underground utilities. A different pump may be installed in a manufacturing facility as a cost- or energy-saving measure.

Construction industry professionals have used various approaches to document as-built conditions of projects. Many projects have employed the redline approach, where design or construction plans are marked up (usually in red) to show the finished conditions of a project. These are often prepared by the construction contractor and referred to as “as-built drawings.”

Some organizations make a distinction between as-built drawings and record drawings. The American Society of Civil Engineers, for example, in its policy statement 290 – Post-construction drawings of civil engineering projects says: “Record drawings are used to verify substantial compliance with the design documents for inventory, asset management, maintenance needs, and for record keeping purposes. Record drawings are distinct from ‘as-builts’ because they should be and often are sealed by the engineer or surveyor of record that provided oversight during construction. As-builts are typically completed by the contractor and are without a seal.”

Other organizations, such as the American Institute of Architects and the Construction Management Association, offer additional guidelines on record drawings and other types of as-built documentation. Governmental agencies may have additional requirements and contract terms regarding such documents.

Regardless of terminology, as-built documentation is valuable in documenting final project conditions. The availability of accurate information can help project owners manage their facilities, guide future renovations and provide reliable records for various parties with interests in the project. For example, a new project located adjacent to a recently completed project will often benefit from accurate as-built information.

Digital technology changes landscape

With digital technologies such as computer-aided design (CAD) and building information modeling (BIM) used on a growing number of projects, the approach to developing as-built documents has been evolving. Instead of marking up paper plans, many project teams use electronic redlining tools to update drawings or directly update digital models to reflect field changes.

Field information gathered with electronic tools such as laser scanners and drone-based imagery has also changed as-built documentation processes. Instead of taking manual measurements, teams can use laser scans or digital imagery to establish actual locations of project elements and compare them with design locations.

Digital twins — virtual representations of real-world entities — have added another twist. Many projects employing digital twins call for updating the digital models on an ongoing basis to reflect physical conditions, making the redline markup unnecessary or a bit of an outlier. In addition to laser scanning data, additional data from sensors, digital photographs and other sources can be used to document as-built conditions. Instead of redlining drawings, teams often rely on drawing revision records to track changes.

With updated and synchronized digital twins, owners and construction teams can make more informed operation and maintenance decisions. Models can be used to track asset performance and predict future behavior via simulations and mathematical modeling. Instead of guessing when pumps or motors might need replacement, actual data can be used to help make those decisions. Updated digital models can also provide access to a wide variety of data, such as part numbers, material details, shop drawings and photos, often accessible via mobile devices.

Immersive technologies such as virtual reality (VR) and augmented reality (AR) can also be used to view project updates. If a door was moved in a building, an interested team member can view the change remotely using VR/AR technology.

Other developing technologies, such as artificial intelligence (AI), may also impact how as-built information is collected and documented. AI tools can guide the collection and processing of drone-based data and create 3D models representing site conditions. These models can be used as a basis for creating as-built documents or as a reference for comparing the design and the actual conditions.

Common data environment (CDE) key to success

When working with digital as-built information, a common data environment (CDE) is key to building a single source of truth. A CDE enables all parties — the owner, designers, contractors, surveyors and others — to access one federated model for all project information.

According to the international standard ISO 19650, a CDE is an “agreed source of information for any given project or asset, for collecting, managing and disseminating each information container through a managed process.” CDEs often use unique, standard identifiers to track all project information. Data can be categorized and assigned a specific suitability status to guide anyone accessing the data regarding its reliability, accuracy, and intended use. A revision control system can make only specific revisions available for use by the project team, to make sure everyone is working from the correct information. Security measures are typically employed to maintain data integrity and controlled access to project information.

An effective CDE is typically geared around a specific collaboration platform and cloud-based data storage. Design collaboration platforms have advanced significantly in recent years and enable information to be tracked and tied to specific project elements, with the system automatically notifying and delivering information to people as needed. Rather than waiting for hardcopy as-builts that show field changes, team members can be informed of changes in near real-time, helping keep future work on track.

Cloud-based data storage enables project team members to access current data regardless of workers’ physical location. Shared network drives have also been used for collaboration but are often limiting in speed and accessibility. As-built documentation on the cloud provides a central, accessible location for data.

Regardless of methodology, as-built documentation forms a vital part of project information. It provides accurate records that aid owners, designers, contractors, governmental agencies and other parties. In the future, it may even guide AI tools that learn lessons from previous projects and apply those lessons to future designs.

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Transportation and infrastructure designers — often working on long, linear projects — generated plan or profile sheets with the top and elevation views superimposed on the same sheet. A series of cross-sections was often used to provide views perpendicular to the project alignment or at other select locations. More artistically inclined AEC professionals created 3D renderings of designs, often using isometric (orthographic) or oblique (non-orthographic) perspectives to depict designs. Initially drawn manually, these renderings helped clients and non-technical audiences visualize designs without having to interpret technical drawings. Some designers also built scaled-down physical models of projects to help convey 3D concepts.

Fast-forward to the 21st century and the use of CAD to design and draft projects. As CAD became more widely accessible, most CAD tools included basic 3D design and modeling tools, and many added animation or fly-through capabilities. Add-on products allowed the creation of more photo-realistic images, setting the stage for advanced tools such as digital twins, building information modeling (BIM), and virtual reality/augmented reality (VR/AR).

Definitions of 3D modeling vary, but for the AEC industry, it can be considered the creation of a mathematical representation of one or more 3D objects or shapes. The digital model can be used to design, analyze, visualize and communicate project concepts.

Common types of 3D models include wireframe, surface, and solid models. Wireframe models depict the skeletal framework of objects using points (vertices) and edges. Surface models use polygon meshes to depict surfaces. Solid models represent both the exterior and interior of 3D-modeled objects.

Let’s take a look at how 3D modeling is used in the AEC industry in various project stages.

Modeling existing conditions

In most AEC projects, one of the first key tasks is gathering data on existing conditions. This often includes performing a project-specific survey to map site topography and existing facilities. AEC professionals have several options for data collection, such as conventional ground-based surveys, LiDAR (light detection and ranging, or sometimes laser imaging, detection and ranging), photogrammetry and GNSS (global navigation satellite system).

Regardless of the data collection technique, some type of 3D information is needed to design most facilities. It may be a basic topographic map with contours and spot elevations or a digital model that can be viewed from different perspectives and used to obtain detailed location information at any selected point.

A triangular irregular network (TIN) is often used to model ground surfaces. TINs are constructed by connecting a set of points with edges to form a network of triangles. The edges of TINs can be used to capture the position of features such as ridgelines or valleys, as well as the location of random points interpolated between triangle vertices.

Designing projects in 3D

3D modeling has become increasingly important in AEC project design since the turn of the century. Instead of drawing project features in three different views, modern designers can build project components in 3D using CAD and BIM tools. Those components and systems can then be viewed in 3D from multiple perspectives and used in multiple platforms to collaborate with other designers, builders and project owners. Review agencies are also increasingly using 3D models to review and approve designs.

Early forms of 3D modeling started with CAD professionals drafting basic lines, arcs and polygons, then transforming them into 3D objects such as cubes, cylinders, spheres and other forms. The CAD professional could then use 3D modeling tools to develop and refine the design, adding points and adjusting their placement to manipulate object shapes.

As technology advanced, CAD and BIM software introduced intelligent objects, such as walls, doors, windows, beams and columns for buildings. Transportation-geared software offered components such as curbs, guardrails, drainage structures and other features. Designers no longer had to draw these components individually but could import them into design environments based on preset or custom parameters. The intelligent objects could often also be used in conjunction with design and analysis software to size the components and interact with other software, databases and asset management systems.

Implementing BIM and operations

The introduction of BIM has added new capabilities in the AEC industry, as designers, builders and owners found value in associating more than just geometric information with CAD models. By associating part numbers, specifications and other data with CAD objects, models became even more intelligent. Tedious tasks such as quantity takeoff could be automated and streamlined using 3D design data and tools. Potential design conflicts could be identified in 3D models instead of in the field during construction.

More recently, the concept of digital twins has gained traction, where BIM data are used to build digital replicas of projects, helping owners and construction teams make real-time updates and drive operations and maintenance decisions. Mechanical systems and components such as pumps and motors can be modeled and analyzed in 3D environments, helping owners simulate actual conditions and determine when to replace or maintain equipment.

Combining mathematical and graphical modeling

Even before CAD and BIM came along, AEC professionals used various forms of modeling to design and analyze structures, water resources and other facilities. Bridge designers, for example, have used finite element methods to calculate stresses in bridges. With modern software, mathematical and graphical modeling can be combined to display 3D views of bridges, with components color-coded to indicate which members are in tension or compression.

Hydraulic analyses of rivers, streams and other water resources have also benefited from 3D modeling. Historically, water resource engineers have used software from the U.S. Army Corps of Engineers for watershed hydrology and hydraulic analysis, with results generating lengthy computer printouts that required detailed analyses to determine results. More recent technology has incorporated graphic outputs of these analyses to display the behavior of water resources under certain conditions. Combining the analytical data with 3D topographic models, engineers can more intuitively display areas of inundation during specific storm events.

Similar 3D modeling techniques are used to analyze water distribution systems in cities, energy use in buildings and embodied carbon analyses for infrastructure projects, enabling AEC professionals to explore multiple design choices faster.

Ongoing technology advances have added even more capabilities to AEC modeling. Technologies such as VR/AR enable designers to experience designs in an immersive environment, interacting with 3D modeling data to help refine designs. 3D models can integrate with schedule and cost data to build 4D models and 5D models, essentially building and tracking AEC projects digitally before building them physically. Artificial intelligence offers even more possibilities, as AI tools work with modeling software to generate new design concepts and analyze multiple scenarios.

Even with all the digital tools available, the role of humans is not likely to diminish. AEC projects remain largely site-specific and require human involvement and judgment to optimize solutions. 3D modeling and related tools are just that — tools that need human guidance for proper use.

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Today’s top story is Nemetschek Group’s new AI Assistant, a chatbot which will debut in both Allplan and Graphisoft Archicad.

In Archicad, the AI Assistant will be able to interact with BIM models in limited ways. For example, you could ask the chatbot to render your model in some particular style (such as with a wooden façade), and it will return an image generated with Nemetschek’s “AI Visualizer” powered by Stable Diffusion. You could also ask the AI Assistant to reveal some specific elements of your model, such as “the wall section at the East entry,” and it will bring up the proper view.

In Allplan, the assistant connects to the internet to help users find industry knowledge such as the minimum width of emergency exits in London.

You can see a brief demo of these capabilities in this video from Nemetschek:

This is the first manifestation of Nemetschek’s plan to launch an “artificial intelligence layer” across its portfolio this year, a plan which wasn’t so much a roadmap as a signpost declaring that Nemetschek has, in fact, heard of AI and does, in fact, plan to do something with it.

Well, this is something. The AI Assistant could prove to be a nifty feature for users of Allplan and Archicad, but by now chatbots are basically the “Hello World” of AI applications—the first step everyone takes when trying to figure out a new language. The real question is how far Nemetschek can go from here.

CAD in point: Acquisitions and updates

Here are some quick hits for your news radar:

• Software reseller GoEngineer announced that it’s acquired Canadian reseller CAD MicroSolutions, effective as of January 3, 2025. CAD MicroSolutions customers will retain access to their current software licenses and annual maintenance plans, and can call the same support line as before, according to an FAQ posted by GoEngineer.
• Jetcam released an update for CAD Viewer, its free software for viewing 2D CAD files. The update adds folder and file count display, window position and size memory, and other quality of life improvements.
• Hexagon has acquired CAD Service, an Italian developer of visualization tools. Effective January 21, 2025, CAD Service will join Hexagon’s Asset Lifecycle Intelligence division.
• Datakit announced version 2025.1 of its data exchange software, which includes enhanced support for 2D and 3D B-Rep geometry alongside other updates.

One last link

You have to love it when CAD marketers get catty. Piggybacking on the popularity of Peter Brinkhuis’ blog post 37 things that confuse me about 3DEXPERIENCE, Onshape posted a blog of their own: 37 Ways Onshape Simplifies What 3DEXPERIENCE Overcomplicates.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

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For years, AEC teams relied on conventional tools such as tape measures, transits, theodolites and levels to collect data, build base maps and document construction projects. As new technologies were developed, AEC professionals gained several options for data collection, along with improvements to conventional technology. Let’s take a look at four common technologies used to collect AEC data: LiDAR (light detection and ranging, or sometimes laser imaging, detection and ranging), laser scanning, photogrammetry and GNSS (global navigation satellite system).

LiDAR and laser scanning

LiDAR and laser scanning are similar technologies, with some subtle differences. Both rely on laser technology, which gained widespread use among AEC professionals in the 1980s and 90s, primarily for measuring distances and establishing alignments and level surfaces. By directing a laser beam to an object and measuring the time for the reflected beam to return to the receiver, laser-based tools enabled users to measure distances accurately with the push of a button. And since laser beams do not disperse appreciably, they proved highly effective for establishing alignments and level planes.

More recently, LiDAR has been used to capture large datasets by targeting an object or a surface with a laser and taking multiple measurements encompassing the area of interest. In conjunction with geolocated control points, the measurements can be used to establish coordinates at each point of measurement.

LiDAR systems may be ground-based or mounted on aircraft, such as drones, also known as uncrewed aerial vehicles (UAVs). Equipped with a laser scanner, along with GNSS equipment and an inertial navigation system, airborne LiDAR is often used to create 3D models of ground surfaces over widespread areas. Airborne systems can also be equipped with high-resolution cameras to capture imagery.

Laser scanning, which also uses controlled deflection of laser beams to capture or establish surface shapes, is often used to build 3D models of buildings, mechanical systems and other specific objects. It is typically ground-based. Laser scanning is also used in 3D printers to build physical objects based on coordinate data.

Both LiDAR and laser scanning typically produce point-cloud images, which consist of numerous 3D points that can be used to depict objects in computer-aided design (CAD) and building information modeling (BIM) systems. Point clouds often need manipulation to be converted to surface models or aligned with other 3D models or point clouds. Because of the large quantities of data generated by point clouds, the resulting datasets may also need to be “thinned” or downsized for practicality in CAD or BIM models. Software utilities and artificial intelligence (AI) can help with this process.

Photogrammetry

Photogrammetry has been used for mapping purposes since the early 1900s. While multiple types of photogrammetry have been employed, the most common AEC applications have used aerial photography and stereoplotters to analyze two or more photographic images taken from different positions. Using this information, photogrammetrists can determine 3D coordinates of select points and plot contour lines to create topographic maps.

With the development of LiDAR and other technologies, photogrammetry has also been used in conjunction with these technologies to produce a wide variety of maps and datasets. For example, since photogrammetry is generally considered more accurate in the X and Y directions (horizontal coordinates), while LiDAR is generally more accurate in the Z direction (vertical), the two technologies can be combined. By georeferencing aerial photographs and LiDAR data in the same coordinate system, 3D visualizations can be created with optimal accuracy and contain a wealth of data.

GNSS

A GNSS uses satellite data to provide positioning, navigation and timing (PNT) services on a global or regional basis. The U.S.-operated global positioning system (GPS) is one of many GNSSs in the world.

The U.S. Department of Defense initiated the U.S. GPS program in the 1970s. The full constellation of 24 satellites became operational in 1993. Initially, the accuracy of civilian GPS data was limited by a deliberate error introduced into the GPS data so that only military receivers could access the maximum accuracy. This limitation was removed in 2000. In the AEC world, most GNSS-based devices still combine satellite information with terrestrial-based corrections or augmentations to compensate for various imperfections and improve accuracy. 

With a robust network of satellites, GNSS data can be captured by numerous devices, including smartphones, tablets and other consumer products. For professional AEC use, more sophisticated GNSS receivers are used to capture 3D point data more accurately. These devices can be manually positioned or mounted on vehicles for mobile use.

In addition to providing a convenient way to collect survey data, engineers use GNSS in many other AEC applications, such as automated construction layout, real-time guidance of construction equipment (machine control), tracking construction equipment and materials, monitoring worker safety and performance, and capturing project progress.

Selecting a method

With numerous data collection choices available, selecting the best method for any given project might seem like a daunting process. In addition to the individual methods described previously, sometimes multiple methods can be used together to achieve the desired results. And for small projects, sometimes conventional tools might still provide the most practical solution.

While there are no hard and fast rules for selecting the best method or methods, proper consideration of key factors and input from experienced professionals can simplify the process. Key factors to consider include:

• Level of accuracy — If the data will be used for final design and modeling purposes, greater accuracy will be required than if the data will only be used for planning purposes.
• Area of coverage — For large areas with high point-density requirements, airborne LiDAR might provide the best results. For smaller footprints with intricate facilities, ground-based laser scanning might be a better choice. Photogrammetry and GNSS can also be considered for projects of various sizes, either individually or in conjunction with one of the other projects.
• Availability of existing data — If a project owner already has data of the appropriate level of accuracy and in the vicinity of the project, but just needs additional coverage, sometimes sticking with the previous data collection method makes the most sense.
• Availability of services — Whether or not the project team has ready access to the various methods can play a part in the decision.
• Budget — Like it or not, sometimes cost plays a key role in selecting a method.

The decision-making team may need to be a multi-discipline group, considering planning, design, construction, and operational needs. An experienced geospatial professional should also be part of the decision-making process.

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That was the key takeaway from of Bentley Systems’ Year in Infrastructure 2024, the annual conference from the software developer focused on infrastructure design, construction and operations.

“AI is a new paradigm shift, transforming every industry, and infrastructure is no exception,” said Nicholas Cumins, CEO of Bentley Systems, in his keynote address at Year in Infrastructure, which took place in October in Vancouver, Canada.

If you missed the conference, here’s a recap of Bentley’s views on AI and other infrastructure engineering trends.

How AI can impact infrastructure

“Just imagine the sheer scale of data that is created in the design, the construction and the operations phase,” Cumins said. “It makes infrastructure a prime area where AI can have the greatest impact.”

AI-driven insights can enable infrastructure asset operators to predict when maintenance is needed before failures occur. AI can analyze digital twins of infrastructure assets such as bridges, energy transmission networks, roads and dams. It can identify issues and recommend preventative action, avoiding breakdowns and safety actions, and even reduce their carbon footprint.

But there’s a caveat, according to Cumins. “The reality is, that in order to take advantage of AI and all the innovations, you need to get control of your data,” he said. Bentley believes it has the offerings that will enable infrastructure stakeholders to make the most out of this emerging technology.

Advancements in digital twin technology

Bentley anticipates that digital twins will be crucial for AI to enable smarter and more connected infrastructure. To that end, the developer has enhanced its iTwin platform with new features to integrate real-time data and improve communications between design models and operational data. They believe this will enable infrastructure professionals to better predict performance, optimize maintenance schedules, and make their asset management strategies more robust.

At Year in Infrastructure 2024, Bentley demonstrated how AI could be integrated into a digital twin through its new product, OpenSite+. A Bentley executive used the software’s generative AI copilot to design a hotel, validate the design, check the geospatial context of where the hotel was to be located, and make real-time changes to the design—all by simply talking to it. OpenSite+ also uses AI to automate the drawing process for a project.

Bentley also says it has enhanced its MicroStation 2024 software to help designers create digital twins as a natural part of their design work, through features including Python scripting support, integrating GIS data into the design, and enabling real-time collaboration on digital twins.

Bentley has also been working on incorporating 3D geospatial data into its digital twin platform. The company recently acquired Cesium, a 3D geospatial platform company whose 3D Tiles standard has been adopted as the Open Geospatial Consortium community standard. The combination of iTwin and Cesium technologies enable an infrastructure asset owner to, for example, collect drone photos, build a reality model from them in iTwin, run the model through AI analytics to detect cracks, process the analytic data through a 3D tiling pipeline into 3D Tiles format, and disseminate the files into any of Cesium’s runtimes.

Bentley is also partnering with Google, which has adopted the 3D Tiles standard in Google Maps, to incorporate photorealistic 3D tiles and geospatial data from 2,500 cities in 49 countries into a Cesium-powered data ecosystem compatible with real-time 3D engines such as Cesium JS, Unreal, Unity and Nvidia Omniverse.

With these partnerships, Bentley aims to facilitate the use of AI in digital twin environments to create better designs, adjust them in real-time, and by using geospatial data, ensure the right decisions can be made to optimize an infrastructure asset at any point in its lifecycle.

The case for open infrastructure data

Another highlight of Year in Infrastructure 2024 was a call-to-action to make data more open and accessible, which Bentley sees as crucial to unlocking the full potential of AI.

“The infrastructure world is complex and, frankly, it’s often disconnected,” said Mike Campbell, chief product officer at Bentley, during his presentation at the conference. “And to make our existing systems more resilient and adaptable to population growth and climate change, we need to connect people with data.”

Complex infrastructure projects often involve multiple organizations, multiple teams, multiple engineering disciplines and multiple stakeholders working together for a long time. This complexity makes it impossible to rely on any one single system or single vendor.

Instead, infrastructure projects need an ecosystem where data is flexible, interoperable and easy to integrate across different tools and platforms. Bentley says its open applications are designed with this in mind, enabling users to edit models from other vendors and other software products while enabling collaboration across teams.

“A road, bridge or dam could be in operation for 50 years or more, undergoing repairs, upgrades and expansions,” said Campbell. “During this time the software and platform used to manage the asset will evolve.”

By ensuring that the data is open, asset owners and operators are able to adopt new technologies and innovations, while still being able to rely on their own historical data. Bentley encourages the industry to adopt its open source data schema for infrastructure so the sector does not have to keep starting from scratch with their data.

No single vendor can tackle the task alone, which is why open, flexible data systems are a better alternative over the long run. “The future of infrastructure engineering is open, it’s flexible, collaborative and built on a foundation of data that you can share securely,” said Cumins.

Sustainability in infrastructure

Another central theme at Year of Infrastructure 2024 was sustainability. There is increasing demand on existing and future infrastructure to be able to handle population growth while being resilient to—and perhaps even mitigating—climate change. Infrastructure designers, builders and operators also have to account for increased regulatory pressures such as the carbon accounting measures introduced in the U.S. and Europe, which can add time and cost to their projects.

However, the wide variety of methods and tools to calculate embodied carbon means that data is often not transparent, presenting a challenge to designers. Another challenge is the time required to calculate embodied carbon, which can be lengthy when the data needs to go through rigorous verification and data cleansing before it can be used. Because of these factors, accurate carbon data is often not available until late in the design phase—resulting in lost opportunities to reduce carbon in the design.

Bentley believes that the digital twin is ideally suited to meet those challenges. The technology can incorporate data and calculate the trade-offs between economic, environmental and social outcomes related to their projects, improving decision making at all stages of a project.

Bentley unveiled a new Carbon Analysis tool for the iTwin platform that they say can rapidly compute embodied carbon to help engineers minimize carbon and understand the required trade-offs. Continuous calculations during the design phase enable users to generate accurate carbon reports much earlier in the life of the project, and any updates to the design model can instantly show the updated carbon footprint of the project.

The Carbon Analysis tool supports over 30 mainstream design file formats within iTwin and integrates with external lifecycle assessment tools, making it easier to report carbon data to project stakeholders and designers. In turn, it’s easier to explore alternative designs, materials or construction methods. By enabling small adjustments early on and throughout the design process, infrastructure projects can reduce their carbon footprint.

Year in Infrastructure 2024 set out Bentley’s vision of the infrastructure sector in the coming decades, with a particular focus on the AI-powered solutions the company believes will help designers, builders and operators meet the challenges and opportunities that are ahead.

“Let’s use AI, our generation’s paradigm shift, to improve outcomes for the built and natural environment,” said Cumins.

The post Bentley’s Year in Infrastructure 2024: The AI paradigm shift appeared first on Engineering.com.

Over the five years since the fire, hundreds of millions of euros have been spent and around 250 companies and hundreds of experts have worked to bring the Paris icon back to life. Although many of those that lent their expertise to the restoration were experts in traditional craftsmanship—carpenters, roofers, art restorers and so on—engineers and digital technology served a critical role as well.

One company that has lent their software, workforce and skills to the restoration is Autodesk. Back in 2021 Engineering.com spoke with Autodesk about how they planned to create a building information modeling (BIM) model of the cathedral with Autodesk Revit. That model ended up being used in more ways than anticipated, and even resulted in changes to Revit itself.

Creating the BIM model of Notre-Dame

Putting together a BIM model of a structure with the complexity of Notre-Dame was only possible because 3D scans of the cathedral had been made prior to the fire.

“It was crucial for rebuilding,” Nicolas Mangon, VP of AEC industry strategy at Autodesk, told Engineering.com. “They decided to rebuild as it was before. So if there was no scan, there were no drawings, there was nothing.”

The France-born Mangon led the restoration project for Autodesk. Working with contractors and a core team of around 15 Autodesk employees, the team focused on using their BIM technology to meet the ambitious deadlines that had been set out for restoration.

It was not easy to develop a complex BIM model like that for Notre-Dame, but the Autodesk team had a window of time to do so. When Mangon visited the cathedral in 2022, the level of lead was still 10 times higher than what humans can be exposed to (all workers and visitors had to wear extensive protection equipment). The process of removing gave Autodesk the time they needed to build the model.

“We hired a company that had 10 to 12 people full time just creating the model for over a year,” Mangon said.

When the restoration teams were ready for the next stage, the BIM model was ready too.

“We saved them a lot of time,” Mangon said. “And they could use BIM and the value of it and instantly they had ROI.”

Adapting modern software to the 1200s

Built primarily in the 1100s and 1200s, Notre-Dame’s design and construction is much different than modern buildings created with the help of 3D design software. But that also means modern BIM tools find older buildings to be a bit of a challenge.

Revit has built-in intelligence and rules that help with making walls straight and aligning elements like columns, beams and the floor. These are typical features that usually make the user’s life easier, but they didn’t quite work for Notre-Dame.

“In Notre-Dame, nothing was straight. It was impossible to do anything,” Mangon said. “So we had to add capabilities to remove some of the logic in Revit to be able to support these kinds of projects in the future. Now we think that this type of technology could be used for a broader scope than just buildings from the last 50 years.”

How the Notre-Dame BIM model was used

Once the restoration team had the BIM model, it was time to put it to use. Here are 4 ways it was used in the restoration:

Scaffolding

Notre-Dame’s repairs required extensive use of scaffolding inside and outside of the building. The cathedral is also covered in complex geometry that can be difficult to perfectly match with scaffold. The BIM model was an important resource for planning the temporary structure.

“They spent a lot of time using the model to design the scaffolding digitally. Every single bar, every location of the scaffolding was predefined months before it was installed,” Mangon said.

Planning construction

The crane at a construction site is one of the most critical pieces of equipment. The BIM model was used to ensure that the crane could be fully used on the job site and reach all deliveries, no matter if they arrived via boat on the Seine River or via truck on nearby roads.

“They used the model to know exactly on which day, which materials were arriving and where the truck needed to park,” Mangon said. “They simulated every minute of the construction process digitally.”

Planning with the BIM model extended to creating instructions for individual workers as well. Many of the processes used to originally craft Notre-Dame are not in use any more, requiring extensive planning with the tradespeople. The BIM model was a key tool at every phase of planning out their work.

Lighting

As a tourist hotspot, the original Notre-Dame had few chances to close and make changes. That meant that the historically dark building remained fairly dim. The restoration provided the opportunity to renovate the building, and specifically, its lighting.

To understand what kind of lights needed to be put inside and where they would be located, engineers again turned to the BIM model.

“It’s very easy in the BIM model to add a direct light or diffuse light, and put it on top, at the bottom, on the side, or wherever. You can really simulate the way it’s going to look. So that was a byproduct of also using BIM for the project,” Mangon said.

Restoration of surroundings

The restoration and reopening was also a chance to improve Notre-Dame’s surroundings. Autodesk’s team scanned the surroundings of the cathedral, creating a model that included the utilities and buildings in the area.

“[The city of Paris] used this digital twin for the architectural contest. So four different companies actually bid on the renovation of the surroundings, including a new museum, new parking, new areas, and they used the digital twin that we created,” Mangon said.

The future of Notre-Dame

Although Notre-Dame reopened this weekend, restoration work continues. The cathedral’s website reports that the restoration of the chevet and sacristy will happen in 2025 and installation of stained glass windows will occur in 2026.

The cathedral’s small stairs and tough-to-navigate spaces mean the BIM model will continue to be used for these projects and for future maintenance and restoration. To enable this, the massive digital asset will be given to the church in Autodesk Construction Cloud.

As the project nears its end, Mangon’s biggest takeaway from the restoration is that none of the work his team has done would have been possible without the initial scans of Notre-Dame.

“I think it’s important to scan historical landmarks,” Mangon said. “If a disaster happens… at least we have something to start with.”

He pointed to the importance of the movement to help preserve Ukraine’s cultural heritage by 3D scanning monuments. The war in Ukraine has destroyed numerous structures, but with these scans, they are not gone forever.

“In the future, technology could help bring to life some of these destroyed structures,” Mangon said.

The post The comeback of Notre-Dame appeared first on Engineering.com.

For engineering firms, artificial intelligence (AI)- driven tools and other intelligent technologies are more than just a novelty or a luxury; they’re a near-imperative to keep pace in today’s highly competitive business environment, according to a newly released benchmarking report for the architecture, engineering, and construction (AEC) industries.

Findings from the 2024 edition of the AEC Inspire Report from Unanet, the business software company for which I serve as executive vice president for AEC, underscores just how important it is for firms to integrate technologies like AI across their operations, from business development to project execution to strategic planning. “One thing is certain,” the report asserts. “tech-advanced [AEC] firms that can harness the full potential of emerging technologies are the ones best positioned to accelerate growth, overcome challenges, and navigate the unknown. Such companies are not only operating for today; they are prepared for tomorrow.”

Based on survey responses collected this past spring from more than 330 senior-level AEC executives, the report (available for free download here) provides a revealing look at the trends, best practices, strategic priorities, and other dynamics shaping these three industries. It gives engineering firms the means to measure themselves against their peers across the industry.

AEC findings

The results highlight a strong sense of optimism across the AEC industries and an increasingly clear business case for firms to embrace technologies like AI. For example:

• Most AEC firms feel good about the current business environment. A large share — 86% — of respondents hold an optimistic business outlook, and 42% say they’re “very optimistic.”

• A winning business climate. Most firms, 58%, report a proposal win rate of more than 50%, while a much larger share, 72%, project a win rate above 50% for the year ahead, another sign of growing optimism. Those most confident in their future are firms that leverage technology because they are more likely to have keen insights into all aspects of their company’s resources, projects, and pipelines. These firms are better positioned to weather challenges and economic unpredictability while having greater confidence in their ability to forecast their business and manage resources.

• Despite a generally positive outlook, 39% of AEC firms are concerned about the economy. Operational efficiency and talent recruiting and retention are other issues that are particularly concerning.

• M&A (merger and acquisition) is on the menu, especially on the buy side. Half of surveyed AEC firms say acquisitions are of interest to their company in the year ahead, while just 5% are interested sellers. Among engineering firms, 40% say they’re interested buyers.

On the technology front, “it may be tempting to stay the course, to tackle change in slow increments,” states the report, “but this approach will not serve for much longer.”

Close to half of AEC firms — 48% — qualify as “tech-advanced” because they meet

at least three of the following criteria:

• Data-driven, regularly using data for business management, decision-making, and performance assessment.
• Cloud-dominant, with more than 50% of tools and applications based in the cloud.
• Fully integrated, with complete integration of platforms and applications across all systems.
• AI-mature, as active users of AI with comprehensive firm-wide policies and procedures in place to guide and govern AI usage.

More than half of AEC firms are using AI to some extent, while another one-third are open to using it but are not currently doing so. Our report reveals a strong business case for firms to implement AI:

• Close to one-third of firms — 31% — are using AI with policies and guidelines in place as guardrails. However, 26% use AI without formal oversight policies, unnecessarily inviting legal, security, and compliance risks.
• Architecture firms are twice as resistant to implementing AI as construction and engineering firms.
• AI-mature firms are much more prolific project proposal producers, averaging 263 per year compared to 144 for less AI-savvy firms. They also win more projects and expect higher future win rates than less AI-savvy firms.

To deliver these kinds of benefits, AI requires firms to establish a strong foundation that includes not only internal policies to guide AI usage but also robust employee training on AI and high-quality data, underpinned by clear data stewardship policies. The report states, “Organizational data governance is foundational to AI implementation, and AI implementation is a must in today’s data-driven reality.”

Findings Specific to Engineering Firms

Engineering firms show deep concern about the current state of their workforce. Compared to their counterparts in architecture and construction, engineering firms struggle more with recruiting and more frequently list recruiting as a top human resource challenge. Although they share the AEC industry’s overall sense of business optimism, the workforce issue is pressing enough for many to turn down work for want of labor. As the report notes, firms can attract and retain talent by offering employees access to cutting-edge technology in their day-to-day work and by partnering with local colleges and trade schools.

A lack of sophisticated forecasting practices exacerbates the talent shortfall. Engineering firms most frequently rely on Excel spreadsheets to forecast labor resources and are less likely to be able to predict their growth rate. Troublingly, one-third of engineering firms say they cannot project their growth for the coming year.

Engineering firms also appear deliberate in adopting AI and supporting AI policies. Less than one-quarter of those we surveyed said they’re using AI with policy guardrails in place. As for the areas in which they expect to realize the most benefit from using AI, data analysis and content generation top the list.

Just how important are AI and digital technologies generally to success? For engineering firms, the report concludes, “Technological transformation is essential to maintaining competitive footing and operational resilience in the face of a growing talent shortage.”

About the author

Akshay Mahajan is Executive Vice President, AEC, at Unanet, a company that creates business software solutions for architecture, engineering and construction firms, and government contractors. For more information, visit https://unanet.com/.

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