3 Computational Design Trends Redefining AEC Design

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As consumers, we live in a landscape of digital change. Innovations are constantly evolving: new products, offerings, technologies, services, and rapid iterations. Consider the sheer volume of new apps on your cellphone; while it may already be overwhelming,  new apps spring up every day.

upturned hand surrounded by points of light

This change is manifesting in the AEC industry in the form of computational design. Following are three computational design trends redefining AEC design:

  1. Earlier Design Intelligence

Sophisticated design intelligence is expanding into the earliest stages of the design process. Designers can now leverage tools that will give them comprehensive, refined feedback when they are scoping projects. And these tools are improving every day. They enable designers to make decisions that are better, faster and more astute at the beginning of a project, when those decisions have the greatest impact. As Anthony Zuefeldt, Computational Design expert with Applied Software, has said more than once, this level of impact cannot be achieved by a human alone. One example, the Internet – for better and worse, exhilarating and terrifying – has fundamentally changed our world.  

Highlighted by Anthony during a webinar on computational design, one tool that enables earlier design intelligence is TestFit, which tests and determines optimal configurations of layouts with rapid prototyping. On the TestFit website, it is described as the “ultimate building configurator.” Using algorithms and co-creation tools, TestFit helps users solve site plans for commodity buildings, i.e. mixed use, industrial, multifamily, and others. This could involve the best yield on a building lot or the most spaces in a parking lot. TestFit leverages Dynamo, an interoperability device, as a connecting tool.

Anthony also mentioned two Gensler computational design tools. Blox works during predesign, providing real-time cost estimating, in addition to automated optimization of design and layouts. The interior prototyping tool gFloorz includes multifloor data intelligence.


The team at Applied Software is deeply involved in this space and encourages a continual dialog around innovation and computational design. If you want to join in, reach out to the experts of Applied Software to participate in this conversation.


  1. Increasing Interoperability

Interoperability is the ability to connect to more and more specialized applications. This concept was used by Autodesk, for instance, in developing its Autodesk Construction Cloud platform, upon which a variety of tools can be used. The Gensler tools described above are BIM applications that can be used on top of Revit. Anthony explained that Rhino.Inside.Revit allows users to connect Rhino inside the memory space of Revit. Rhino geometry can be brought into Revit as Revit elements, with a bidirectional workflow. He described Rhino as one of the best products on the market for interoperability, because it enables advanced analysis and decisions.

Image: Food4Rhino.com
  1. Surrogate Modeling

On the cutting edge of innovation in some industries is the practice of adding machine learning tools to processes. In engineering, the surrogate model is a method to determine an outcome that is difficult to measure. Most engineering design problems require simulations to decide constraints, for instance finding the most energy efficient building orientation. But real time modeling – a model of the outcome – can be accomplished with a surrogate.

The surrogate modeling workflow is: (i) model design decisions; (ii) simulate iterations; (iii) train surrogate; (iv) inform design.

Surrogate modeling can mimic the behavior of simulations, with data as accurate as a simulation would provide. This enables a data driven, real-time modeling approach. The user can make design moves with real-time feedback on impacts of, for instance, energy performance and structural load analysis. A prototype does not need to be rendered. The resulting accuracy is close enough to make intelligent design decisions.  

In AEC design, looking at all the possible outcomes is impossible for humans working alone. Considering the potential for time savings alone that computational design tools enable, it begs the question, are designers working in the best possible way? Computational design allows designers to question existing workflows and explore new ones.


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