The Ultimate Guide to Computational Design

Basics of Computational Design

Computational Design and the tools and processes behind it are revolutionizing today’s building industry. It has created a paradigm shift in the way we think and the way we work. Every facet of the AEC industry will eventually be affected by Computational Design, and some have called it the “defining moment” of this decade.

We define Computational Design as an algorithmic problem-solving methodology that uses digital capabilities to develop solutions. This plays off the concept of Computational Thinking and allows us to expand our understanding of the topic without limiting our opportunities of its application.

Through this problem-solving methodology, the building industry is beginning to diminish some of the largest, most pressing challenges companies face, while transforming existing processes.

When your company uses Computational Design, it can benefit in many different ways, such as:

• Increase the value of utilization and return on investment by automating tasks.
• Improve quality of design and designing better solutions for your customers.
• Remain agile in finding the best design solutions.
• Simulate designs for analysis.
• Accomplish more with less.
• Take on larger and more complex projects.
• Generate more revenue on a per-project basis.

There’s a multitude of trends occurring in Computational Design. The pace of change and innovation in this facet of the building industry is extraordinarily high. As processing power grows, so do our capabilities to perform computationally intensive tasks. Increasingly sophisticated computing methods allow for more powerful tools to be added to the building design process. Computational Design is becoming more and more effective and valuable.

Computational Design can help you achieve better business outcomes through productivity gains, efficiency increases and overall better designs.

As building information modeling (BIM) processes mature, and companies are looking for new and different ways to differentiate their services, using Computational Design for enriched design analysis is moving to the forefront. When incorporated into a business strategy, this process can give companies a much-needed competitive advantage.

Organizational Vision and Strategy

The importance of having an organizational vision and strategy around Computational Design can’t be overstated. The very nature of the way we work and design is changing. There are things that need to be reimagined and redesigned when it comes to your job.

To get a better understanding of what those things are, watch the exclusive Applied Software on-demand webinar, “Develop an Organizational Vision and Strategy with Computational Design.”

Computational Design Business Strategy and Outcomes

A business strategy helps us define our business, establish our goals and give the company purpose. With that strategy, we can establish a clear vision and define how we intend to develop a competitive advantage within the market.

Throughout the building and manufacturing industries, company leaders continue to develop their business strategies as they attempt to keep pace with the ever-changing world. As the industry continues to change and evolve, having a plan for how to evolve alongside it can help keep your business relevant and competitive.

Computational Design is one of those evolving concepts that is empowering the industry to challenge long-held assumptions about the way we work; therefore, to maintain a competitive advantage, it should influence the business strategy. To realize the full benefits of what Computational Design can offer, it is critical to develop a functional strategy that supports the overall business strategy.

Rowing harder doesn’t help if the boat is headed in the wrong direction.” – Kenichi Ohmae

Business Outcomes

Understanding the business outcomes and compelling reasons to adopt Computational Design as a problem-solving methodology is the first step in developing a business strategy. The three business outcomes that are most compelling are efficiency, quality and growth potential.

Efficiency: Increase the value of utilization by automating tasks and do more with less.

Quality: Ability to pursue and simulate the better design solutions.

Growth Potential: Take on larger and more complex projects, leading to more revenue generated on a per project basis.​

Developing a Business Strategy

There are three key components that the experts at Applied Software recommend when developing a business strategy for Computational Design:

• Gap analysis
• Strategy roadmap
• Evaluation plan

Gap Analysis

When developing a gap analysis, it’s important to understand the current state of the firm. What are the challenges and obstacles the team is facing as they continue to maintain a competitive advantage?

The next step is to identify the ideal state – the goals – making sure these properly align with the overall business goals.

Once you know the start and the end, identify the gaps that exist between the current and ideal states.

Finally, develop a strategy to address each gap you’ve identified.

Strategy Roadmap

Once the gap analysis has been completed, the next step is to develop a strategy roadmap, which will be used to aid in implementation. The strategy roadmap is often designed as a phased approach with key milestones and objectives. Although you are developing a roadmap, the journey will often be cyclical versus linear; therefore, it’s important to consider how the aspect of continuous improvement factors into your business strategy.

Having a strategy and a roadmap are critically important, but if you are not accomplishing your goals laid out in the roadmap, then it means little. This is where the final component of business strategy development comes into play.

Evaluation Plan

The evaluation plan document helps the team define what success looks like. It consists of a detailed breakdown of how each goal will be tracked. During the process, you need to determine the metrics you want to measure, along with how and when to measure them.

As Computational Design becomes more mainstream within the industry, those who have not considered how it will impact their overall business will quickly lose the competitive advantage they once had.

Developing a strategy that supports the overall company strategy will position any firm to realize the full benefits of this industry-changing problem-solving methodology.

Developing Operational Strategies

The convergence of computational tools in a maturing BIM world is fundamentally reshaping the building and manufacturing industries and, again, changing the way we design and work.

This convergence has empowered the industry to challenge long-held assumptions about the way we work. It has given us the opportunity to reimagine some of these conditions. Systems facilitating key elements of computational processes – like automation, integration and simulation – are becoming increasingly ubiquitous and important factors in determining business outcomes.

The competitive landscape of the industry continues to evolve. This is compelling many organizations to position themselves for the coming decade and the future of the industry by embracing Computational Design. As a problem-solving methodology, Computational Design not only provides a competitive advantage in today’s market, it will be a necessity in tomorrow’s.

At Applied Software, a dedicated team of industry experts is focused on progressing the practice and pedagogy of Computational Design in the context of organizations. This research and consulting group is working with companies across the entire spectrum of the building and manufacturing industries. They are developing and executing the strategic roadmaps that are key to implementing and evolving the practice of Computational Design.

Through careful implementation focused on the right tools, processes and skills development, customers can transform their companies and position themselves for the imminent future of our industry as Computational Design takes hold.

How to Implement Computational Design

Many companies choose to partner with an expert when adding Computational Design to their workflows. This problem-solving methodology, situated at the intersection of human and machine, is fundamentally reshaping the building and manufacturing industries using Dynamo, Grasshopper®, and Rhino.Inside®.Revit.

By partnering with the Computational Design experts of Applied Software for instance, your company can develop and define strategies, tackle specific business problems and get training in cutting-edge software.

For those who are committed to developing formal operational strategies around Computational Design, Applied Software has launched a Computational Design Roadmap Workshop.

This workshop develops and refines formal organizational strategies around Computational Design. Through a comprehensive evaluation process, Applied Software works in lockstep with you to formulate a strategic report that focuses on achieving critical business outcomes and objectives by reimagining and redesigning existing workflows.

Solutions Development

The Applied Software Computational Design team helps solve challenges.

Anthony Zuefeldt – Senior Solutions Specialist with a deep background in AEC technologies and workflows.

Marcello Sgambelluri – Internationally recognized BIM leader and contributor to education and implementation of BIM technology.

Donnell Grantham – Senior Solutions Specialist with a Masters in Civil Engineering and experience as a project engineer and structural designer.

Christopher Riddell – Host of The AEC Disruptors Podcast and Director of Innovation at Applied Software; former BIM manager with ten years of architectural experience; promotes the use of Dynamo to extend the power of Revit®.

Make your repetitive tasks a thing of the past by building solutions and automating tasks like tagging, documentation and content creation.

Team Development and Training

• Rhino.Inside.Revit
• Grasshopper and Rhino
• Dynamo
• Advanced Coding
• Node Development

Implementation Overview

For a thorough overview of what’s involved in implementing Computational Design, you can watch the on-demand webinar “Computational Design: A Guide to Implementation.”

As a review, Computational Design involves algorithms, decomposition of a design, pattern recognition, and abstraction. Essentially, in the process of Computational Design a designer uses software to instruct a computer to solve design problems (creation, fabrication, interaction, analysis), and the resulting design is computed faster and with more options.

Implementation Phases

1. Strategy

A strategy for Computational Design should always be the first phase of implementation.

Implementation is challenging; admitting that and preparing for it will better position your company to succeed. Since Computational Design is a game-changing force that can unlock next level productivity gains in AEC firms, it’s surprising that some companies try to take a shortcut around developing a proper strategy. A well thought out strategy can avoid struggles with firmwide adoption.

Your strategy needs to be in place before you can implement successfully. The strategy roadmap, with support at the leadership level, needs to align with your business goals and include a plan for the way you want your implementation to proceed.

If your company struggles with this step, watch the on-demand Applied Software webinar about Computational Design implementation strategies and methods. The presenters explain successes and challenges in effectively utilizing Computational Design.

2. Implementation

Implementation is the second phase in establishing a Computational Design process in your company.

The Applied Software on-demand webinar on Computational Design implementation describes a framework you can work from as you implement better ways of doing things.

The success of a Computational Design implementation depends on support and cooperation of leadership. With that, you can build strategic alignment and begin to implement.

For a successful implementation, certain steps are necessary:

• Plan the culture shift that will occur.
• Democratize usage.
• Deploy a development process.

Implementation Guidelines

Following are guidelines and three key steps to a successful Computational Design implementation:

1. Plan Your Culture Shift

Successful Computational Design implementation requires a culture shift, challenging the status quo of the way things have been done.

Most importantly, you must educate the staff and raise awareness of your strategy and plan. They need to be committed to the implementation.

Develop a communication strategy to create visibility and spur excitement with the staff. This might include lunch-and-learn events, an intranet or monthly update memos. Let everyone know how Computational Design will help the company move forward.
Everyone should be encouraged to provide continuous feedback and ideas. Use the 70/20/10 ratio for usage and adoption:

• 70% of the staff should be educated on using Computational Design.
• 20% should have the skillset to do basic troubleshooting and editing.
• 10% – the select group – should have the skillset to create and lead future solutions.

As things unfold, evaluate how your culture shift is going.

What questions are being asked?
Is leadership engaged?
Is a pipeline developing with staff ideas to improve the process?
Are they comfortable with the process?
100% of the staff should be encouraged to provide feedback and ideate on future solutions.

2. Democratize Usage

The Computational Design process needs to be accessible to and usable by everyone to achieve long-term success and firmwide adoption. Monitoring data helps with learning and development, while feedback should be encouraged.

Since adoption could be difficult, be ready to encounter some resistance. The more inclusive your process is, the more successful it will be. To democratize usage, ease of access and use are critically important. Tools need to be straightforward, familiar and relevant. Easy access might be provided through a shared collaboration space, add-ons or third-party tools. Keep in mind that different users will solve problems differently.

Embed usage analytics into your Computational Design tools. Metric and data tracking are essential to this process. The ability to review trends will help determine whether the implementation is running successfully. In addition, building custom design automation tools creates an opportunity to embed usage analytics functions that can generate valuable datapoints and insights into your process.

You can use data tracking and metrics to measure how well the learning – and later the implementation – is going. PowerBI is one example of a way to represent your data visually. You can see macro trends about how the tools are being used and whether they are performing as planned.

Implementing Computational Design requires an investment of time – time that may have otherwise been billable. You need to make the commitment to that investment to make your company better in the long run. Compare the time input to the time savings. You build a tool one time, and it gets used many times afterward.

3. Deploy a Development Process

Creating and deploying a development process is an important aspect of implementation because of the impact it has on future Computational Design tool development.

Create a space for team members to submit ideas and get continuous feedback from everyone about their likes and dislikes. Learn from the end users about how to improve solutions.

Use an Agile Development Process (ADP) for effective Computational Design project management and application development. ADP is an iterative and cohesive method of driving the entire lifecycle of a project. It will help you avoid problems with your tool when you go live. In the ADP process you:

• Perform feasibility testing and understand context
• Prototype
• Refine and address bugs
• Educate
• Continuously improve your process

Monitor the answers to these questions:

How long is it taking to develop a tool?
Are you getting ideas from the entire group of users?
Is the beta testing group engaged, and do they know what they are testing for?
Is the beta testing capturing bugs prior to a tool’s release?

Stay in touch with the ways your industry is evolving and learn from trends and developments.
Investigate what others are doing. You should not have to develop and create everything from scratch. Look at case studies in the industry to help guide your process.

When you implement Computational Design, it’s not just about being an engineer or an architect. It’s about being better at what you do. Set a goal to continuously learn how to get better. In essence, there’s a lot to learn from Computational Design and a lot to gain from it if implemented correctly. As long as questions are asked and addressed, striving to grow makes many problems conquerable.

Components of Computational Design

When trying to understand Computational Design, it’s important to understand that it’s a problem-solving methodology, and there are many different components and concepts that fall under the term. Think of it as the umbrella; below it, concepts like generative design, machine learning or artificial intelligence are helping to drive specific Computational Design solutions.

Thoughts on Democratizing Generative Design

“The landscape of technology is changing before our eyes,” said Anthony Zuefeldt in the 2020 inaugural episode of AEC Game Changers Podcast. Anthony, Senior Specialist at Applied Software, joined a group of industry leaders in a discussion about generative design. With a background in Computational Design and proficiency in Autodesk Dynamo and Grasshopper®, Anthony has unique insights into the technology of generative design and what can be achieved with the tools already available in the world of AEC.

“When you really take a step back and take stock of what’s happening…it’s staggering. It really is,” he said. “Innovation is exploding, and the way we work is really changing before our eyes.” In the AEC industry, advancements are taking place almost too quickly to take stock of. “We’re kind of at the forefront of a massive paradigm shift in the way we design,” he said.

In regard to making generative design accessible to everyone, or democratizing it, Anthony explained we “need to grasp that we’re in a rapidly maturing boom era. It’s important to understand the confluence of factors that have catalyzed the concept of generative design and make it increasingly accessible and powerful.”

At its core, generative design is a human-based strategy that augments human capabilities by using algorithms to automate design logic. You have design ideas, plus building codes, plus sustainability, plus a budget. Using a computer, the algorithms can control and manage much larger portions of functionality, data and analysis than a person can. It can then present you with hundreds or even thousands of design options.

Another important issue that Anthony introduced during the discussion is acknowledging that generative design is hard. Although it has existed as a concept for a long time, only now are we starting to fully grasp it. “We’ve been talking about this since before I was born, for decades and decades,” he said, adding that it really hasn’t manifested in a meaningful way until the last ten years.

To be successful, Anthony reminds us that we need to be intentional. “We have to frame the problem to be solved in the parameters to be manipulated,” he said. “We have to be very targeted with this. But on the balance of that, we have to make it repeatable for many generative design solutions so it doesn’t go too far.”

The future of generative design is something that many in the AEC industry are anxious to see, including Anthony. “We’ll get to see things that we never conceived of, and by doing so, we get to go and try it. We can actually make better designs. I think that’s the thing that everybody wants,” he said. “For us, it’s continually saying, ‘what’s the next step?’ and being able to take this a little bit further with some extra effort to gain a lot more.”

In the long run, generative design will yield great results. We can already see how much progress has been made in the past few decades, and we are expectant that there will be a great deal more.

Just the past decade has marked tremendous progress in these emergent workflows. The advent of open application programming interfaces (APIs) in BIM technology platforms is transforming the way the AEC industry works.

In the another podcast episode, Bridging the Gap with Lilli Smith, the present and future of generative design are clarified for those who would like more information about it. In conjunction with Computational Design tools like Dynamo, the promise of increasingly sophisticated and powerful generative design solutions becomes more and more tangible in this changing design landscape.

The Role of Dynamo in Generative Design

In the last few years, generative design has made waves as a potential disruptor in the design process. The basic premise of generative design is that through an algorithmic iterative design process, designers can produce a range of potential design solutions. This unlocks prospective outcomes that were previously impossible to produce by human means alone.

Autodesk Dynamo originated as a standalone, open-source visual programming environment for designers to explore conceptual designs without needing to create physical prototypes. Dynamo was designed to be accessible to anyone: non-programmers and programmers alike.

No longer a standalone product as of January 31, 2022, it is now available in:

• Revit (as Dynamo for Revit, with versions 2017 – 2019) – Lets you customize your building information workflows. Enables rapid design iteration and broad interoperability.
• Civil 3D® (as Dynamo for Civil 3D since version 2022) – Process simple, repetitive or complex tasks quickly and efficiently.
• Advance Steel (with versions 2017-2022) – Generate complex structures using native beam and plate elements.
• Robot Structural Analysis Professional (Dynamo for RSA).
• FormIt Pro (Dynamo for FormIt) – Solid modeling environment for conceptual designs; produces real-time changes.
• Alias (Dynamo Extension) – Comes with sample scripts. Generate quick concept variations; reduce repetitive modeling.
• Dynamo Sandbox – A free download of the core Dynamo technology that is not integrated into any other software product. It has limited functionality and is used mostly to provide feedback on new features, development and testing.

Dynamo enables users to create visual logic to control the way data (numbers, text, etc) is processed in Revit. It gives users the ability to visually script behavior, define custom pieces of logic and script using various textual programming languages. These custom algorithms are composed of elements connected together to define relationships and sequences of actions.

Dynamo is a programming environment that requires no programming experience. Download the free resource, “Ultimate Guide to Autodesk Dynamo.”
Download

A bridge to the future of design, Dynamo serves as the engine to the Generative Design Tool inside Revit. It offers powerful opportunities for revamping and automating the design process in digital workflows.

Dynamo is a tool that makes data integration easier and can access project information in a straightforward manner. It also supports powerful data reporting workflows. Using Dynamo, it’s possible to extract granular data from multiple applications and compile them into a single data repository. This can then be connected to dashboard tools like Power BI to provide insights into projects.

It is used to author ‘studies’, which are essentially the programmatic instructions for Revit to run specific generative design analysis in the form of parametric conceptual designs.

As a computational tool, Dynamo is versatile and powerful. It is also rapidly evolving and growing more powerful with each version. To stay competitive in the AEC space, companies need to consider Computational Design tools like Dynamo and understand how they can impact their workflows.

Manual Design→Automated Design→Generative Design→What’s Next?

Design automation is the ability to script computational processes that reflect the creative and analytical methods of human driven design using specific rules and instructions. That’s a mouthful that boils down to taking a manual process and letting a computer help you do it faster. Time is spent up front making the script which will save time in the long run by automating repetitive tasks.

Autodesk Dynamo works great for design automation in architecture and engineering. Using Dynamo, designers can create visual and graphical logic to explore conceptual Revit designs that update parametrically. Projects can be visualized using precision surface and solid geometry, and iterations can be explored and analyzed faster.

Generative design uses the rules laid out with design automation in order to create many versions and variations of possible designs. Instead of one outcome being evaluated at a time, the process can be completed at a much faster rate with computer assistance. These include options the user may not have even thought of due to the limited ability to create numerous iterations and compare them. Generative design helps lead to design optimization by comparing different outcomes of parameter variables to the given goals.

Often you will hear generative design referred to as co-design. The computer and the human collaborate to produce an outcome. People set the rules and constraints, and the computer uses its processing power to iterate through and optimize based on the given constraints.

As described by Applied Software Developer Carlo Marsden, “Generative design also reduces cost, not only through optimization of performance and material use, but also by reducing the time dedicated to finding optimal solutions. Dynamo plus Autodesk Generative Design is a common technology combination for achieving generative design. It uses multiple runs of Dynamo scripts to graph values of parameters and show different outcomes.”

Autodesk used this technology a few years ago to generatively design the layout of two of the three office floors in its new Toronto office. Referred to as Project Discover, the design approached multiple goals – daylight, space adjacencies, square footages, and form constraints.

Design automation and generative design are both user-defined processes where the user is setting the rules and flow of the design parameters. Marsden says, “Numerous times I’ve encountered people who conceptually thought of generative design as a means of letting a computer come up with a design for them, but it’s actually the user(s) telling the computer how to design and letting the computer create the possible outcomes within given parameters.” So it’s not “artificial intelligence” that spontaneously comes up with a design. The capabilities are limited to the rules and data supplied by the user.

The next step? Machine learning is the next logical stage beyond generative design. With it, the code uses the results of generative design to “predict” optimal outcomes from the design patterns encountered. No industry is exempt from machine learning and development of disruptive technologies; see the Applied Software blog for “How Manufacturing is Evolving.”

Intersection Between Humans and Machines

A 2000 abstract by Frank Ulrich at the University of Koblenz states, “Delegation has been an important concept in different areas of computer science for a long time.” During the 2022 Digital Agility Summit breakout session on Computational Design, “The Intersection between Humans and Machines,” an addition was added to that premise: “It was a natural step for us to delegate to computers.” Computational Design is a natural outgrowth of the human process of design.

Computational Design represents a paradigm shift in the way designers think and work. Through it, an algorithm is trained to solve problems. Machine learning is a way to analyze data and help make decisions, either supervised, unsupervised or reinforced.

During a Digital Agility Summit breakout session, Anthony Zuefeldt and co-presenter Christopher Riddell explained how nuanced algorithms are used to generate improved design options for AEC professionals. When the algorithms are trained using quality data, the accuracy of predictions and the design options become better over time. Thus, Computational Design is a game-changing force that improves productivity and increases efficiency.

Using design layout templates built into Powerpoint as an example, Anthony explained that even this commonly used machine learning dataset is getting better and better at offering options and producing presentation layouts for us.

Images: MDPI.com

Anthony demonstrated a sample of computer learning for architectural design using “Architext,” which is a platform to generate residential floorplans based on simple text prompts:

• Typology – house with two bedrooms and two bathrooms.
• Enumeration – house with five rooms.
• Adjacency – kitchen is adjacent to a bedroom, living room is not adjacent to a bathroom.
• Location – house with a bedroom in the northeast side.

The rate of progress we’re seeing in this space is exponential. Machine learning tools will help designers understand the impact of their decisions with real-time feedback, not days or weeks afterward as was the case historically with the design-feedback-redesign process. With schedules getting shorter all the time, designers don’t have the time to go through the back-and-forths. The point is, all our projects could go through this simulation and review process – to guide design rather than react to it.

Just as it was a natural step to delegate design components to computers, the resulting real-time process that it enables will result in better, more accurate design options for improved productivity and efficiency.

The Difference Between Generative Design and Parametric Design

View the full breakdown in this article

Generative design and parametric design are subsets of Computational Design, which uses input parameters and constraints in the process of advancing a design to a preferred outcome.

How they are different

Parametric design uses parameters and constraints to solve a design problem, while generative design applies algorithms to those same parameters to generate hundreds or thousands of possible design variations to review and choose from.

Parametric Design

The term parametricism was established by the architectural theorist Patrick Schumacher in 2008. A partner at Zaha Hadid Architects, Schumacher published a series of works that defined and explained the core tenets of parametricism. This design style is exceptionally diverse in its expression of form, focusing on developing spatial relationships between elements and forms. The design style is made possible by the process of parametric design.

In its simplest form, parametric design is an interactive and iterative process that contains a set of parameters and a set of outcomes. The parameters are inputted by the user and, as they are adjusted and manipulated, the design will follow suit.

Using parametric design, a designer or engineer can make changes to the project in real-time, and the model is updated automatically. In this way, a designer can explore many options before choosing a foinal design.

Parametric design is faster than traditional design, reducing design work from weeks to mere days. It enables a design team to test multiple solutions and save certain designs for reuse on later projects.

While parametric design has been used in the gaming and movie industries for a while, it continues to gain popularity in other areas, such as industrial and architectural design.

Generative Design

Generative design is a design optimization technique. Often referred to as a co-design process, a designer introduces parameters and constraints, while the system uses algorithms to perform the optimization process in pursuit of the best solution.

Along with inputting the parameters and constraints, the designer will establish the evaluation metrics to rank the results.

To get the desired results, the designer must be properly educated in inputting the precise constraints and parameters in order to obtain good output. Once the inputs and success metrics have been established, the system will run to calculate its first results. Using the metrics provided by the user, the results are ranked in order of desirability and fit, rejecting the poor solutions.

Then, using what it learned, the computer chooses the features with the highest scores to create another set of results. Using this iterative approach, designers can test thousands of options, while also learning about risks for each design of the project.

Together

Parametric and generative design have a big role to play in the future of design.

In addition to saving time and increasing productivity, they are a reliable way to lower project costs and anticipate risk. The two concepts build on each other, as both require a human designer to input the required parameters and constraints and establish the success metrics. In a world where we are asked to do more with less, both can improve the process of designing projects and giving the customer precisely what they want.

Computational Design Trends

Comprehensive Webinar

(view a full webinar on the subject)

Even if you are already familiar with Computational Design – an essential problem-solving methodology that many architects use in their projects – you may be interested in how it is impacting the AEC and manufacturing industries. Computational Design represents the next evolutionary step in revolutionizing productivity.

Tune in to the Applied Software on-demand webinar with Computational Design strategist Anthony Zuefeldt. You’ll learn about key trends and recent innovations that surround it, learn how we define it, and take a look at the Computational Design process.

As adoption and utilization rates increase, it’s important for us to better understand what impacts Computational Design is having on our industry.

Conference Highlights: Digital Agility Summit

Digital Agility Summit on-demand breakout session: “Computational Design: The Intersection between Human and Machine”

Computational Design represents a paradigm shift in the way we think and work. Every facet of the AEC industry will eventually be affected by it, and some have called it the “defining moment” of this decade. This game-changing force can unlock next level productivity gains in AEC firms.

Access this breakout session through the 2022 Digital Agility Summit Agenda. The educational discussion will explain the many ways Computational Design can help you achieve better business outcomes through productivity gains, efficiency increases and overall better designs.

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