As a structural engineer, I’ve watched how AI is transforming various industries with excitement. But I’ve also noticed our field’s hesitation to adopt these technologies — and for good reason. We deal with safety-critical systems where reliability is a requirement.

In this article, I’ll show you how we can harness AI’s capabilities while maintaining the reliability we need as engineers. I’ll demonstrate this with an AI agent I created that can interpret truss drawings and run FEM analysis (code repository included), and I’ll give you resources to create your own agents.

The possibilities here have me truly excited about our profession’s future! I’ve been in this field for years, and I haven’t been this excited about a technology’s potential to transform how we work since I first discovered parametric modelling.

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What makes AI agents different?

Unlike traditional automation that follows fixed rules, AI agents can understand natural language, adapt to different situations, and even solve problems creatively. Think of them as smart assistants that can understand what you want and get it done.

For example, while a traditional Python script needs exact coordinates, boundary conditions, and forces to analyse a truss, an AI agent can look at a hand-drawn sketch or AutoCAD drawing and figure out the structure’s geometry by itself (see image below). It can even request any missing information needed for the analysis. This flexibility is powerful, but it also introduces unpredictability — something we engineers typically try to avoid.

The rise of specialised AI agents It’s 2025, and you’ve probably heard of ChatGPT, Claude, Llama, and other powerful Large Language Models (LLMs) that can do amazing things, like being incredibly useful coding assistants. However, running these large models in production is expensive, and their general-purpose nature sometimes makes them underperform in specific tasks.

This is where specialised agents come in. Instead of using one large model for everything, we can create smaller, fast, focused agents for specific tasks — like analysing drawings or checking building codes. These specialised agents are:

• More cost-effective to run
• Better at specific tasks
• Easier to validate

Agents are becoming the next big thing. As Microsoft CEO Satya Nadella points out, “We’re entering an agent era where business logic will increasingly be handled by specialised AI agents that can work across multiple systems and data sources”.

For engineering firms, this means we can create agents that understand our specific workflows and seamlessly integrate with our existing tools and databases.

The engineering challenge

Here’s our core challenge: while AI offers amazing flexibility, engineering demands absolute reliability. When you’re designing a bridge or a building, you need to be certain about your calculations. You can’t tell your client “the AI was 90% sure this would work.”

On the other hand, creating a rule-based engineering automation tool that can handle all kinds of inputs and edge cases while maintaining 100% reliability is a significant challenge. But there’s a solution.

Bridging the gap: reliable AI agents

We can combine the best of both worlds by creating a system with three key components (see image below):

1. AI agents handle the flexible parts – understanding requests, interpreting drawings, and searching for data.
2. Validated engineering tools perform the critical calculations.
3. Human in the loop: You, the engineer, maintain control — verifying data, checking results, and approving modifications.

Let me demonstrate this approach with a practical example I built: a truss analysis agent.

Engineering agent to analyse truss structures

Just as an example, I created a simple agent that calculates truss structures using the LLM Claude Sonnet. You give it an image of the truss, it extracts all the data it needs, runs the analysis, and gives you the results.

You can also ask the agent for any kind of information, like material and section properties, or to modify the truss geometry, loads, forces, etc. You can even give it some more challenging problems, like “Find the smallest IPE profile so the stresses are under 200 MPa”, and it does!

The first time I saw this working I couldn’t help but feel that childlike excitement engineers get when something cool actually works. Here is where you start seeing the power of AI agents in action.

It is capable of interpreting different types of drawings and creating a model, which saves a lot of time in comparison with the typical Python script where you would need to enter all the node coordinates by hand, define the elements and their properties, loads, etc.

Additionally, it solves problems using information I did not define in the code, like the section properties of IPE profiles or material properties of steel, or what is the process to choose the smallest beam to fulfil the stress requirement. It does everything by itself. N.B. You can find the source code of this agent in the resources section at the end.

In the video below, you can see the app I made using VIKTOR.AI

How does it work: an overview

Now let’s look behind the screen to understand how our AI agent works, so you can make one yourself.

In the image below you can see that in the centre you have the main AI agent, the brains of the operation. This is the agent that chats with the user and accepts text and images as input.

Additionally, it has a set of tools at its disposal, including another AI Agent, which it uses when it believes they are needed to complete the job:

• Analyse Image: AI Agent specialised in interpreting images of truss structures and returning the data needed to build the FEM model.
• Plot Truss: A simple Python function to display the truss structures.
• FEM Analysis: Validated FEM analysis script programmed in Python.

The Main agent

The Main agent is powered by Claude 3.7 Sonnet, which is the latest LLM provided by Anthropic. Basically, you are using the same model you are chatting with when using Claude in the browser, but you use it in your code using their API, and you give the model clear guidelines on how to behave and provide it with a set of tools it can use to solve problems.

You can also use other models like ChatGPT, Llama 3.x, and more, as long as they support tool calling natively (using functions). Otherwise, it gets complicated to use your validated engineering scripts.

For example, here’s how we get an answer from Claude using Python (see image below).

Let’s break down these key components:

• SYSTEM MESSAGE: This is a text that defines the agent’s role, behaviour guidelines, boundaries, etc.
• TOOLS_DESCRIPTION: Description of what tools the agent can use, their input and output.
  messages: This is the complete conversation, including all previous user and assistant (Claude) messages, so Claude knows the context of the conversation.

Tools use

One of the most powerful features of Claude and other modern LLMs is their ability to use tools autonomously. When the agent needs to solve a problem, it can decide which tools to use and when to use them. All it needs is a description of the available tools, like in the image below.

The agent can’t directly access your computer or tools — it can only request to use them. You need a small intermediary function that listens to these requests, runs the appropriate tool, and sends the results back. So don’t worry, Claude won’t take over your laptop… yet

The Analyse image agent

Here’s a fun fact: the agent that analyses truss images is actually another instance of Claude! So yes, we have Claude talking to Claude (shhh…. don’t tell him ). I did this to show how agents can work together, and honestly, it was the simplest way to get the job done.

This second agent uses Claude’s ability to understand both images and text. I give it an image and ask it to return the truss data in a specific JSON format that we can use for FEM analysis. Here is the prompt I use.

I’m actually quite impressed by how well Claude can interpret truss drawings right out of the box. For complex trusses, though, it sometimes gets confused, as you can see in the test cases later.

This is where a specialised agent, trained specifically for analysing truss images, would make a difference. You could create this using machine learning or by fine-tuning an LLM. Fine-tuning means giving the model additional training on your specific type of data, making it better at that task (though potentially worse at others).

Test case: book example

The first test case is an image of a book (see image below). What’s interesting is that the measurements and forces are given with symbols, and then the values are provided below. You can also see the x and y axis with arrows and numbers, which could be distracting.

The agent did a very good job. Dimensions, forces, boundary conditions, and section properties are correct. The only issue is that element 8 is pointing in the wrong direction, which is something I ask the agent to correct, and it did.

Test case: AutoCAD drawing

This technical drawing has many more elements than the first case (see image below). You can also see many numerical annotations, which could be distracting.

Again, the agent did a great job. Dimensions and forces are perfect. Notice how the agent understands that, for example, the force 60k is 60,000 N. The only error I could spot is that, while the supports are placed at the correct location, two of them should be rolling instead of fixed, but given how small the symbols are, this is very impressive. Note that the agent gets a low-resolution (1,600 x 400 pixel) PNG image, not a real CAD file.

Test case: transmission tower

This is definitely the most challenging of the three trusses, and all data is in the text. It also requires the agent to do a lot of math. For example, the forces are at an angle, so it needs to calculate the x and y components of each force. It also needs to calculate x and y positions of nodes by adding different measurements like this: x = a + a + b + a + a.

As you can see in the image below, this was a bit too much of a challenge for our improvised truss vision agent, and for more serious jobs, we need specialist agents. Now, in defence of the agent, the image size was quite small (700 x 600 pixels), so maybe with larger images and better prompts, it would do a better job.

An open-source agent for you

I’ve created a simplified version of this agent that demonstrates the core concepts we’ve discussed. This implementation focuses on the essential components:

• A basic terminal interface for interaction
• Core functionality for truss analysis
• Integration with the image analysis and FEM tools

The code is intentionally kept minimal to make it easier to understand and experiment with. You can find it in this GitHub repository. This simplified version is particularly useful for:

• Understanding how AI agents can integrate with engineering tools
• Learning how to structure agent-based systems
• Experimenting with different approaches to truss analysis

While it doesn’t include all the features of the full implementation, it provides a solid foundation for learning and extending the concept. You can use it as a starting point to build your own specialised engineering agents. See video below.

Conclusions

After building and testing this truss analysis agent, here are my key takeaways:

1) AI agents are game changers for engineering workflows

• They can handle ambiguous inputs like hand-drawn sketches
• They adapt to different ways of describing problems
• They can combine information from multiple sources to solve complex tasks

2) Reliability comes from smart architecture

• Let AI handle the flexible, creative parts
• Use validated engineering tools for critical calculations
• Keep engineers in control of key decisions

3) The future is specialised

• Instead of one large AI trying to do everything
• Create focused agents for specific engineering tasks
• Connect them into powerful workflows

4) Getting started is easier than you think

• Modern LLMs provide a great foundation
• Tools and APIs are readily available
• Start small and iterate

Remember: AI agents aren’t meant to replace engineering judgment — they’re tools to help us work more efficiently while maintaining the reliability our profession demands. By combining AI’s flexibility with validated engineering tools and human oversight, we can create powerful solutions that respect our professional responsibilities.

I hope you’ll join me in exploring what’s possible!

Resources

• Simple Truss Analysis agent Project repository: GitHub
• Claude: Learn promping, tool calling and multimodality (free course)
• LLama 3.2: Learn promping, tool calling and multimodality (free course)
• Claude documentation on tool use
• Claude documentation on vision

About the author

Anande Bergman is a product strategist and startup founder who has contributed to multiple successful tech ventures, including a globally-scaled engineering automation platform.

With a background in aerospace engineering and a passion for innovation, he specialises in developing software and hardware products and bringing them to market.

Drawing on his experience in both structural engineering and technology, he writes about how emerging technologies can enhance professional practices while maintaining industry standards of reliability.

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In the world of infrastructure design, traditional processes have long been plagued by inefficiencies and fragmentation. That’s the view of engineer turned software developer Andreas Bjune Kjølseth, CEO of Norwegian startup Infraspace. “Going from an idea to actually having a decision basis can be a quite tedious process,” he explains.

Four years ago, Kjølseth left his career in civil engineering consulting and founded Infraspace, to develop a brand new generative design tool for civil infrastructure alignments – road, rail or power networks. In his years as an engineer and BIM manager Kjølseth was left frustrated by the limitations of traditional processes. Civil engineers commonly must navigate multiple software tools, explains Kjølseth – sketching in one platform, generating 3D models in another, using GIS for analysis on land take and environmental impact, and then manually assembling, comparing and presenting alternatives.

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Infraspace aims to unify this fragmented workflow within a single, cloudbased platform. The software is primarily designed to tackle the initial phases of linear civil infrastructure projects, using an outcome-based approach, as Kjølseth explains. “Users can define where they want the generative AI engine to explore alternatives and define the outcomes, such as, ‘I want options with the least possible construction costs, shortest travel time or length, and the least land take in certain areas.’ Then the algorithm will quickly explore opportunities to make better solutions.”

The Infraspace cloud platform generates thousands of alternatives within minutes, enabling engineers to explore options they might not have considered if done manually.

Design options are displayed via an intuitive web-based interface, featuring a 3D model alongside an analytics dashboard with key performance indicators (KPIs) such as cost, route length, land take, and cut-and-fill volumes.

The system can also be used to assess the environmental impact of proposed designs, including carbon footprint, viewshed, noise, and which buildings or areas might be affected.

Based on this information engineers can quickly compare and evaluate multiple design alternatives, then use the software to refine designs further. As the software is cloud based, this makes it easier for multiple stakeholders to understand the consequences quicker, explains Kjølseth

“The typical project manager often has limited access to advanced CAD, BIM or analysis software. With Infraspace they can quickly log into their projects in their browser and see the 3D models together with the analytics instantly,” he says. “It’s also possible to invite external stakeholders into the project to explore a selected number of alternatives.”

Project seeds

To start a project, users can pull in data from various sources, such as Mapbox or Google, or upload custom digital terrain models, bedrock surface models, or GIS data.

The design can then be kickstarted in several ways. An engineer could simply define the start and end point of an alignment, then let the software work out the best alternatives based on set goals. Alternatively, an engineer can define geometric constraints—such as sketching a corridor or marking environmentally protected areas as off-limits.

Users can define where they want the generative AI engine to explore alternatives and define the outcomes, such as, ‘I want options with the least possible construction costs, shortest travel time or length, and the least land take in certain areas’ – Andreas Bjune Kjølseth, CEO, Infraspace

The system is not limited to blank slate designs. It can also import alignments from traditional infrastructure design tools like AutoCAD Civil 3D and use them as a basis for optimisation. As Kjølseth explains, some engineers are even just using the platform for its analytical capabilities, to get fast feedback on traditionally crafted designs. The software offers import / export for a range of formats including LandXML, IFC, OBJ, BCF, glTF, DXF and others.

Adaptability across geographies

Infraspace is not hard coded for specific national design standards, but as Kjølseth explains, the platform captures the fundamental mechanisms of infrastructure design. It allows engineers to define geometric constraints, set curve radii, specify vertical alignment parameters, and adapt to different project types including roads, railways, and power transmission lines. It can handle projects with varying levels of design freedom, from short access roads to expansive highway corridors.

Designed by engineers, for engineers

For civil engineers seeking to streamline their design process, reduce environmental impact, and explore more design options, faster, Infraspace offers an interesting alternative to traditional fragmented workflows. Most importantly, with a team combining civil engineering expertise and software development skills, it’s clear the company understands the nuances of infrastructure design.

While Infraspace is currently focused on early-stage design and optimisation, its ambitions extend beyond. “We will continue to add more features as we go,” says Kjølseth. “I see that generative design as a concept and the platform we have, can definitely be applied to many use cases — during the latter stages of a project, and to even more complex problems.”

Main image: The generative AI engine can deliver thousands of design options in minutes

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Bentley Systems wasn’t wasting time at its recent YII conference, hammering home its ‘open’ approach to data. The message from Bentley’s execs was clear: the company is putting its weight behind openness— the sub text being that some of its competitors are not.

The emphasis was hard to miss. In the first two keynote presentations alone, led by new CEO Nicolas Cumins and CTO Julien Moutte, the word ‘open’ was used a staggering 60 times.

While AI also got a strong showing with 47 mentions, it was obvious that Bentley was speaking to customers, and not trying to impress investors. The message? Openness isn’t just a token word; it’s a cornerstone of Bentley’s strategy. “Your data is your data always,” said Cumins.

Moutte outlined the three pillars of Bentley’s open approach: open standards where everybody that was granted access to the data can read and understand it; open source, where every developer can leverage that data; and open APIs, where users are free to take their data out whenever they want, instead of just being able to query it.

Cumins also touched on the complexity of infrastructure projects, which involve multiple organisations, teams, and disciplines “This complexity makes it impossible for you to rely on any single system or single vendor. Instead, you need an ecosystem that enables flexibility, integration and interoperability across different tools and platforms,” he said.

He also addressed a common concern among customers: “Don’t get locked in,” he said. “Make sure you retain control of your data.”

As AEC firms navigate the transition from files to a data-lake world, this message should resonate more than ever. With proprietary files (DWG / DGN / RVT) at least drawings and models could be accessed via Open Design Alliance (ODA) libraries. With proprietary databases, access is granted only via APIs, which are under the control of the vendor.

“We are not creating another silo,” said Cumins.

Looking ahead, Bentley is focused not just on the present, but on the long-term future. Infrastructure is built to last. “Our users must assure that this data, their data, remains accessible for decades to come, and we believe this is only possible with a truly open approach,” said Moutte.

At the heart of Bentley’s open strategy is the iTwin platform, a suite of APIs and services designed to help AECO firms develop digital twin applications for designing, building, and operating infrastructure assets. iTwin integrates data from various sources—specs, drawings, CAD and BIM models, reality captures, sensor data, inspection records, and more.

The platform is built on a schema specifically designed for infrastructure. While the majority of this schema is open-source, one component — the Parasolid geometry modelling kernel —is proprietary, as it is owned by Siemens.

As Cumins explained, “The schema goes beyond basic data exchange, ensuring that data isn’t just accessible, but its meaning can also be understood, whether you’re dealing with materials or structures or subsurface data.”

He emphasised that this schema helps engineers, constructors, and organisations maximise the value of their data. “We’re not keeping it to ourselves. We actively encourage others in the industry to adopt the schema. This is about moving the entire infrastructure sector forward together.”

Artificial Intelligence (AI)

Cumins described AI as a “paradigm shift” for the infrastructure sector, highlighting the massive scale of data generated during the design, construction, and operational phases. “It makes infrastructure a prime area where AI can have the greatest impact,” he said.

Bentley’s investment in AI dates back several years. In 2018, the company acquired machine learning and IoT developer AIworx, which former CTO Keith Bentley described as an “acqui-hire,” meaning it was made primarily for the talent. Since then, that talent has driven several successful AI use cases, primarily in asset operations. These include automatic object classification in reality meshes and using computer vision, IoT sensors, and machine learning for predictive maintenance—detecting issues before they lead to failures.

Many of these technologies now fall under Bentley Asset Analytics, a new product line that uses AI to provide insights into the condition of infrastructure assets. “This approach is especially important for critical infrastructure like bridges and dams, where monitoring and maintenance are key to ensuring long-term safety and performance,” said Mike Campbell, Chief Product Officer, Bentley Systems.

The portfolio includes Blyncsy, which utilises crowd-sourced dashcam footage and machine learning models to automate roadway maintenance and asset inventory. “AI can help identify roadside assets and assess their conditions, everything from a broken stop sign to a faulty streetlight to a fresh pothole,” explained Mike Schellhase, VP of asset analytics at Bentley. “These insights feed directly into the infrastructure digital twin, the iTwin, and are presented in iTwin Experience to show the latest conditions and context.”

Another product, OpenTower iQ, uses drone imagery, data, and AI to manage telecom towers throughout their lifecycle, handling everything from data acquisition and visualisation to structural analysis, site design, and maintenance.

Bentley plans to expand these solutions to cover a broader range of asset types, offering owner-operators increasingly advanced AI-powered tools.

Now, the company is pushing AI further into the design phase, using generative AI to automate repetitive tasks.

The first of a new generation of AI-powered design tools is OpenSite+, which is used for civil site design (including roadways, parking lots, and buildings).

The software features a co-pilot experience that taps into knowledge stored in documentation, specifications, and 3D site models through natural language interactions. “We can ask questions like, ‘Can I build a hotel in this area?’ or ‘Do I have enough parking to meet my requirements?’” said Francois Valois, VP of civil infrastructure at Bentley.

“At this stage [in the design process], we don’t know if our layout is optimal, so we build a neural network that evaluates thousands of alternatives to find the best one, optimising costs while meeting engineering requirements,” Valois explained. The software also offers AI-powered earthwork optimisation, which Bentley has enhanced by wrapping its current engine in a neural network to make the process significantly faster.

Another key feature is automated drawing production, a hot topic in AEC as it can save so much time and money (read this AEC Magazine article). According to Bentley, drawing production can account for up to 50% of a site design project’s time, and Bentley’s AI-powered tools automate annotation, labelling, and sheeting, optimising the placement of labels and dimensions, and according to organisational standards.

Currently, drawing production isn’t built directly into OpenSite+, but rather, it’s routed through OpenSite Designer, an existing power-platform based product based on MicroStation. However, as Campbell explained by going down this route, the technology can then be made available in other power platform-based applications. Watch this space.

This process will take time, as the large language model (which Campbell playfully wants to call a “large drawing model”) has so far only been trained on tens of thousands of site plans and only from North America. “We’ve got to do that for the UK, for Australia, for all of the other places, because the standards are all different,” Campbell said. “Roadway plans have a different look and feel, right? So, we’ve got to build a model for that.”

As to where the training data has come from, Campbell explained that most of it is sourced from a licensed open-source library, though some customers have granted Bentley explicit permission to use their data. “We’re not just going and taking plans from random [customer] accounts,” he said, adding that Bentley keeps track of the provenance of every data set used. If a company decides to withdraw its data, Bentley will retrain the model accordingly.

In the future, customers will be able to train the AI model on their own data. “Say you’ve got 6,000 site plans—you can put them into your own local, retrained version of the model, and that will be yours and yours alone.”

As for the future of AI at Bentley, Campbell hinted at broader applications. “Instead of using AI to generate a site, could we use AI to generate other designs, a bridge design, for example, optimise a road for – not just the obvious ones like curvature and safety, but things like carbon impact and cost and time.”

“Civil engineers can imagine a certain section of the design space. AI can imagine a much broader design space,” Campbell added. “The good news is that in engineering, we’re governed by rules of physics and standards and safety factors and all those kinds of things, so that limits it, but still, it’s bigger than what a typical engineer can think about.”

Desktop deployment

Beyond AI, one of the most significant aspects of OpenSite+ is that it’s a completely new type of application. It’s iTwin-native, so it writes directly to an iModel without needing to go through intermediary formats like DGN. Unlike many modern applications, it’s also a desktop application, rather than running in a browser.

“We’re not yet convinced that all of these engineering workloads are going to be able to work on the cloud,” explained Campbell. “We also want to be able to take advantage of local compute. And we’ve also got our eye on these new AI processors that are coming out – NPUs – and we want to be able to take advantage of that.”

OpenSite+ works both online and offline, syncing changes (deltas) when connectivity is restored. Bentley is taking the same desktop/iTwin-native approach with its new visualisation tool, Advanced Visualization, which is built on Unreal Engine (more on this later).

Campbell acknowledges that more tools are in the pipeline, either going through validation or still in the research phase, and they will all follow this same framework.

With many new AEC software tools from startups running in the browser, we asked Campbell if this would influence Bentley’s acquisition strategy. “Not necessarily, but it would be a consideration,” he said. “We certainly look at architecture [of the software].”

He also admitted that technical integration has become a key focus for Bentley, which is one reason why acquisitions have slowed over the last couple of years. The priority now is ensuring that products work seamlessly together. “The idea of convergence and integration, ensuring that data flows smoothly across applications and the lifecycle, and informs the infrastructure digital twin—that’s the goal,” Campbell emphasised.

Back into BIM?

Bentley once held a strong position in architecture, especially in the early 2000s with MicroStation and GenerativeComponents, a pioneering computational design tool that preceded Grasshopper.

However, in recent years, the company has shifted its focus away from this market. Given the growing interest in next-generation BIM tools, the question arises: does Bentley have aspirations to get back into BIM?

“Our focus right now is absolutely on horizontal infrastructure, and there’s plenty there to keep us busy,” said Campbell.

“But I’m reluctant to say never, especially in light of the sentiment of the broader AEC ecosystem. In particular, I’m thinking about open letters, I’m thinking about the AEC spec [AEC Future Software Specification]. I’m thinking about what their vision is for the tools they’ll use in the future. And I’m thinking about the tools that we’re building. And when I read that spec and I look at our strategy, it’s not dissimilar. It’s not a slam dunk, but we’re in the ballpark.

“The data lake, the elements of openness, design in context and at scale. That’s in the AEC spec.”

Geospatial – giving assets context

Last month, out of the blue, Bentley Systems acquired Cesium, developer of an industry cherished 3D geospatial platform – Cesium ion – with an ecosystem of open standards, including Cesium.JS and 3D Tile technology. (To learn more about the acquisition read this AEC Magazine article).

“With the acquisition of Cesium, we are now able to provide a 3D geospatial view of infrastructure,” said Cumins. “We are effectively changing the vantage point of an infrastructure digital twin, from the engineering model of the infrastructure asset to the planet Earth, upon which we geolocate the engineering model and all the necessary data from the surrounding built and natural environments.”

However, as Cumins explained, perhaps the most significant aspect of the acquisition is that Cesium as a company, perfectly aligns with Bentley’s vision of open standards and interoperability.

“The combination of Cesium plus iTwin enables developers to seamlessly align 3D geospatial data with engineering, subsurface, IoT, reality, and enterprise data to create digital twins with astonishing user experiences that scale from vast infrastructure networks to the millimetre-accurate details of individual assets—viewed from land, sky, and sea, from outer space to deep below the Earth’s surface to support engineering workflows.”

Cesium’s 3D Tile technology makes light work of huge geospatial datasets by streaming only what the user needs for any given view.

It uses a concept called hierarchical level of detail (HLOD), where you basically have a tree of tiles – the root being the least detailed version, the branches adding more detail, and the leaves having the highest resolution.

3D Tiles can handle a whole smorgasbord of 3D geospatial data including point clouds, reality models (derived from photogrammetry), and 3D buildings. Through its recent AECO Tech Preview Program it can also handle BIM models (IFC and Autodesk Revit), complete with metadata which can be used for querying, filtering, styling, and analytics.

The long-term plan is to unite the iTwin and Cesium Ion platforms, but as Patrick Cozzi, CEO of Cesium and now chief platform officer at Bentley, explained nothing will be done without input from the community. The potential is limitless, he said. “We can add voxels for biometric visualisation, for subsurface, we can add Gaussian splats for higher visual quality for point clouds; we can do temporal tiles to help show the change in construction sites over time.”

Building on this geospatial focus, Bentley announced a new strategic partnership with Google which will integrate Google’s comprehensive repository of 3D geospatial data with Cesium and Bentley’s iTwin platform.

“Consider a large urban development project where multiple infrastructure systems are used – roads, bridges, energy and water networks – that must be coordinated across various stakeholders,” said Cumins. “By integrating Google’s vast 3D geospatial data with Bentley Cesium technology and iTwin platform stakeholders can visualise their assets, both existing and plan in full real-world context.”

Advanced Visualization

Cesium 3D Tiles and Google 3D Tiles play a key role in Advanced Visualization, a new product from Bentley powered by Unreal Engine, designed to overcome the challenges of creating immersive, interactive, and photorealistic infrastructure experiences.

This software integrates seamlessly with iTwin for live access to up-to-date project data, enabling users to navigate massive models in real time using Cesium 3D Tiles.

As Greg Demchak, VP, emerging technologies group at Bentley explained, users can enrich their scenes with additional context and content: “Context in the form of Google 3D tiles, and content in the form of easy to place trees, cars, scale, figures and equipment.”

Advanced Visualization functions either as an out-of-the-box solution or as a flexible platform for building custom applications and is currently in Early Access.

Up front carbon analysis

One of the key challenges in achieving sustainability on infrastructure projects is the time-consuming nature of carbon reporting, which is typically handled by specialists.

Kelvin Saldanha, associate director at WSP, elaborated: “Once you’re ready to measure carbon, it needs to go through a rigorous quantity take-off process, including design compilation and data cleansing, before that data can be used in third-party software.”

This process can cause significant delays. As Saldanha noted, “A lot of times that delay means that the design team doesn’t know what the carbon score is until the design is very mature, and you kind of miss the opportunity to reduce your carbon score.”

He emphasised that the best opportunity to make a meaningful impact on carbon reduction is in the early stages of a project’s design cycle.

WSP has been testing new carbon analysis capabilities in iTwin Experience through Bentley’s Early Access Program, which Saldanha said gives the design team much-needed transparency in the earlier phases of a project. “Continuous calculations during the design process allow for accurate carbon reports to be generated much earlier in the project lifecycle,” he added.

What truly sets Carbon Analysis apart, according to Saldanha, is the software’s visualisation capabilities. “Instead of sifting through spreadsheets and tables, designers can now exchange data directly with EC3, view heat maps, and interact with them to quickly identify where they’ve got carbon intensive features on their project, and then they can target those.”

Bentley’s new Carbon Analysis capabilities are available to iTwin Experience users at no additional cost, though a separate licence is required for carbon assessment calculators like EC3 or OneClickLCA.

Conclusion

While many AEC software companies talk openness, few demonstrate the same level of commitment as Bentley – though open standards, open source and open APIs.

At YII, Bentley’s executives made this abundantly clear with deliberate, impactful messaging: “Don’t get locked in,” “Your data is your data,” and “We’re not creating another silo.”,

This was a strong signal to customers that the keys to their data must remain in their hands, a crucial point as the AEC industry transitions from file-based systems to data lake environments.

Of course, Bentley’s approach isn’t purely altruistic; as a company with shareholders now, it stands to benefit from the industry moving towards storing data in its model wrapper.

And while third parties can choose to use the open specification to create an iModel / iTwin independently, Bentley has a suite of mature data management, authoring and analysis tools and services ready to go.

But with this open approach it’s clear that Bentley is playing the long game—a smart strategy considering the long lifecycle of infrastructure assets.

Cumins pointed out that the software and platform used to manage these assets will evolve considerably over time. “So, by ensuring that our systems remain open, we allow organisations to adopt new technologies and innovations while still being able to access and build on their historical data,” he said.

Of course, it’s possible that future platforms may not even be developed by Bentley, but the key takeaway for customers investing in the Bentley iTwin ecosystem is this: they will always have the freedom to choose.

The post Bentley Systems: the promise of data freedom appeared first on AEC Magazine.

Civils.ai has launched a new online training course, the ‘Construction AI Specialist’, which explains the fundamentals of how AI applications work and how users can build their own Construction AI tools from scratch, with no-code ‘AI Agent’ tools and with Python scripts.

Examples includes an AI to create a bill of quantities from CAD PDFs, generating an Excel spreadsheet of the BOQ; an AI to analyse PDF construction contracts, creating a table of risky clauses and a draft renegotiation letter; an AI to write site inspections from photos, creating a draft Word document; and an AI to restyle buildings with photos + prompt, using diffusion models and image segmentation.

The course also includes 12 months access to all the features on Civils.ai so users can start applying what they’ve learnt and get to grips with what is really going on under the hood of applications like Civils.ai.

According to founder Stevan Lukic, the company is also thinking of adding a few more bonus tutorials in the future, including an AI Borehole Digitiser, an AI for 2D floorplan to 3D IFC BIM model creation, and an AI for CCTV analysis from camera feeds.

The post Civils.ai launches ‘Construction AI’ training course appeared first on AEC Magazine.

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Cover story: towards open systems
We explore Autodesk’s new approach to openness and note that, with its recent Nemetschek announcement, things seem a little different

NXT BLD / NXT DEV event previews
At AEC Magazine’s annual events in London on 25-26 June you’ll not only see what the future holds for AEC technology but you can have a say in how it unfolds

Skema: BIM workflow compression
Skema is one of a handful of new tools from design-oriented start-ups that is engineered to work with existing BIM software to shrink project timescales

Dassault Systèmes (DS) in AEC
A market leader in manufacturing, DS is developing a new generation of AEC tools, which aim to cross the chasm between digital design and digital manufacture

Twinview (digital twins)
We explore Space Group’s Twinview, one of the most advanced BIM digital twin offerings available today

Safeguarding contractors from Cyberattacks
It’s every construction firm’s biggest nightmare: criminals taking control of their data and holding them to ransom

Nvidia Omniverse spreads its wings
With new Cloud APIs, Nvidia is extending the reach of Omniverse beyond the core demographic of designers and artists

XR: streaming to a headset near you soon
All-in-one XR headsets have proved very popular for AEC design review. But for realism and complexity, 3D models must be processed externally, and pixels streamed in

Review: Scan micro workstation
This compact 8-litre workstation might not bring much new to the table in terms of chassis, but it’s hard not to take notice when the price is so aggressive

Review: Nvidia RTX 2000 Ada
This entry-level pro viz GPU is a great option for small workstations

The post AEC Magazine May / June 2024 Edition appeared first on AEC Magazine.

Oasys Giraphe is a new geotechnical graphing software designed to makes it easier for users to create, customise, update, and interpret graphs. It is integrated with Seequent’s OpenGround geotechnical information management solution.

According to Oasys, with few clicks, users can retrieve OpenGround data and plot it on any of the geotechnical graphs in Giraphe’s predefined library.

Custom graphs can also be built using custom queries to sample fields from project data grids in OpenGround, or from customer data upload via CSV file.

Graphs can be arranged in collections for easy reference, quick creation and refreshing to help streamline their export for use in reports and presentations.

“In planning our implementation of OpenGround at Arup it became clear that we needed to find a way of rolling over our customised geotechnical graphing capability from gINT,” said Mark Skinner, Geotechnical portfolio product manager at Oasys.

“Building Giraphe provided us with a way of doing this, and at the same time with a great opportunity to capitalise on technological advances.”

The post Oasys Giraphe geotechnical graphing tool launches appeared first on AEC Magazine.

A joint effort between Autodesk, Infotech, the American Association of State Highway and Transportation Officials (AASHTO), and the Montana Department of Transportation (Montana DOT) has led to improved interoperability between Autodesk Civil 3D and AASHTO’s construction contract solution, AASHTOWare Project.

The improved interoperability will support digital project delivery from design and documentation to estimation and asset management. Departments of Transportation (DOT) across the United States and Ministries of Transportation in Canada will soon have access to it.

“With state transportation agencies in full pursuit of digital transformation, we’re excited to continue our work with Autodesk and provide an interoperable solution to those organisations,” said Chad Schafer, chief revenue officer, Infotech, the official AASHTOWare project contractor.

“This integration will help bridge the gaps in data and workflow between departments to ensure successful digital project delivery.”

The AASHTOWare Project integration closes a workflow gap by enabling state DOTs to take quantities directly from Civil 3D without manual entry, which can be error-prone and time-consuming.

According to Autodesk, they can use GIS information in design, push the design information to project execution with AASHTOWare Project, pull actual quantities back to as-builts, and push data back into GIS for asset management.

“Interoperability between Civil 3D and AASHTOWare Project will help us connect our design phase to our field construction operations,” said Patrick Lane, digital delivery project manager for the Montana Department of Transportation. “It will save time, save costs, and ultimately, enable us to be more accountable to the taxpayers who fund our transportation projects.”

The post Transportation boost for Autodesk Civil 3D appeared first on AEC Magazine.

Civils.ai, the civil engineering focused AI tool that applies a large language model (LLM) to help reduce the time it takes to search for information in construction project documents, has delivered several new updates in its 2.0 release. This includes usability and management improvements and specific new functionality for geotechnical engineering.

The software works by users uploading PDF project documents, such as site reports, contracts or design codes. The AI then processes the data so it can generate answers to specific questions posed by users relating to their project. This could be querying deliverable due dates, through to extracting information on geological conditions from site investigation reports.

The tool is developed by a team led by former Arup and Morgan Sindall civil engineer Stevan Lukic.

According to Lukic, the key difference between his system and ChatGPT (the most well-known example of LLM), is that Civils.ai allows some fine-tuning of responses, whereby site-specific data not contained within the datasets of OpenAI can be temporarily stored in the AI’s memory, using technology known as a vector database.

The user is then able to ask questions and search through the database and retrieve an answer to the question.

“To avoid incorrect or false data being retrieved and misunderstood by the user, the exact page and section of the document is also retrieved and displayed for the user to check and verify against the original document that the answer to the question is correct,” explains Lukic.

According to Lukic, Civils.ai has more than 15k monthly users, including those from Aecom and Stantec. Major contractors in the UK are also considering the platform.

The post Civil engineering AI co-pilot updated appeared first on AEC Magazine.

Geological modelling specialist, Seequent, has released Slope3D, a slope stability analysis tool for geotechnical engineers and engineering geologists.

According to Seequent, Slop3D offers a practical approach for capturing slope failure mechanisms for simple to complex geotechnical models.

Building on the capabilities of Seequent’s GeoStudio 2D Slope/W product, Slope3D is billed as an intuitive limit equilibrium solution for analysing rock and soil slopes in mining and civil projects – for example, hillslopes, open pit mines, and engineered structures such as dams and levees.

“Ensuring the safety and reliability of engineered projects is at the heart of geotechnical engineering,” said Chris Kelln, director, technical solutions for GeoStudio.

“We specifically designed Slope3D to empower geotechnical and geological engineers to make confident decisions, improve safety, reduce project risks and costs, and ultimately design better infrastructure.”

Slope3D is part of Seequent’s GeoStudio 2023 release. It connects directly with Seequent’s geological modelling software, Leapfrog, via Seequent Central, and integrates with GeoStudio’s Seep3D. According to Seequent, this creates a seamless workflow with smooth data exchange and simpler data management to improve project accuracy and outcomes.

Seequent was acquired by Bentley Systems in 2021 for $1.05 billion.

Seequent’s products include Geosoft for 3D earth modelling and geoscience data management, GeoStudio for geotechnical slope stability and de-formation modelling, and Leapfrog for 3D geological modelling and visualisation.

Leapfrog appears to have particular relevance to infrastructure projects. The software is designed to replace traditional 2D subsurface modelling and simulation processes. According to Bentley, the usage of the software, often in conjunction with Bentley’s software offerings, has been growing consistently in civil infrastructure sectors.

The post Slope3D for geotechnical engineering unveiled by Seequent appeared first on AEC Magazine.

US engineering design and consultancy firm WSB has launched a digital construction management solution and advisory service, based on Bentley Systems Synchro.

The aim of the service is to help civil infrastructure firms overcome the challenges of adopting model-based digital workflows and harnessing the power of ‘construction digital twins’.

WSB joined the Bentley Digital Integrator Program for construction to help develop the service.

“Owners and construction firms realise that new digital workflows are needed to meet infrastructure demands. Applying these digital workflows successfully requires a deep understanding of technology, processes, and data,” said Carsten Gerke, senior vice president of strategic partnerships with Bentley Systems.

“The Bentley Digital Integrator Program is built around combining technology with subject matter expertise for improved infrastructure. WSB joining the program provides a leapfrog opportunity for all our transportation users.”

Key services include enabling a single source of truth by connecting project, contract, and document management to the future of design—a 3D/4D/5D constructable model—as well as the ability to create constructable models from current 2D plan sets, which allows the transition to a single source of truth for all stakeholders.

Bentley Synchro is a construction management software that supports the entire civil construction lifecycle with ‘office-to-field’ workflows. It is designed to give firms insight into project performance, productivity, and financial health and forms the construction service of the Bentley Infrastructure Cloud.

Find this article plus many more in the March / April 2023 Edition of AEC Magazine

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