Artificial Intelligence

Get Smart: Though still a nascent technology, AI promises to transform the design process and the built environment.
Architectural Record
By Clifford A. Pearson
1 AIA LU/Elective; 0.1 ICC CEU; 1 IIBEC CEH; 0.1 IACET CEU*; 1 AIBD P-CE; AAA 1 Structured Learning Hour; This course can be self-reported to the AANB, as per their CE Guidelines; AAPEI 1 Structured Learning Hour; This course can be self-reported to the AIBC, as per their CE Guidelines.; MAA 1 Structured Learning Hour; This course can be self-reported to the NLAA.; This course can be self-reported to the NSAA; NWTAA 1 Structured Learning Hour; OAA 1 Learning Hour; SAA 1 Hour of Core Learning

Learning Objectives:

  1. Outline the history of AI.
  2. Describe the possible design-process advantages and efficiencies of AI.
  3. Describe ways AI is being deployed to create architectural components, buildings, and cities that are responsive to environmental conditions and users’ needs.
  4. Discuss the privacy and security concerns associated with the collection of vast amounts of data necessary to use AI as a design tool.

This course is part of the Business and Technology of Architecture Academy

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In the past decade or so, software for architects has evolved from CAD to scripted geometry engines like Rhino and parametric BIM platforms like Revit—moving from the representation of buildings (in plan, section, elevation) to more responsive systems that show the impact of one change on the rest of the project. Thanks to faster and cheaper computers and the enormous computing power and storage capacity of the cloud, AI systems are now able to encode information and relationships in increasingly complex layers. Because they’re able to process vast amounts of data accessible from internet-based sources, they can create statistical correlations that approximate learning, says Phillip Bernstein, author of a forthcoming book on AI and an associate dean at the Yale School of Architecture.

Of course, most architects are nowhere close to doing any of that in their practices, instead using platforms like BIM to create drawings, rather than connecting them to data to create insight, notes Bernstein. Architects will be pushed to adopt AI tools, he says, mostly by clients who are already using large data sets, machine learning, and predictive simulation to manage their operations and facilities. Steve McConnell, managing partner at NBBJ, which has designed buildings for major tech companies like Amazon and Tencent, echoes this sentiment. By using data to drive their designs, architects can show the value of what they do to clients who run their own businesses with data-driven processes, points out McConnell.

McConnell says AI will also enable architects to “move upstream” in the building-development process by giving them the analytical tools to serve as “strategic partners” to clients—helping them identify business opportunities, for example, even before project planning has begun. Architects, though, “need to reimagine their skills and what they bring to the table.”

The promise of AI is not only that it will provide a business advantage, points out Bernstein. The technology has the potential to help architects address “profound problems,” such as reducing waste and embodied carbon in projects, making public spaces more equitable, and enhancing building performance, he says.


IN PRISTINA, KOSOVO, Carlo Ratti Associati used AI to better understand the city and then design a series of temporary interventions, including a community “living room” on a vacant lot.

A few design firms are developing their own proprietary AI tools to help them work on big projects. Gensler, for example, rolled out its Nform “ecosystem” of data-driven software—much of which employs algorithms and AI—in the summer of 2020. Developed by the firm’s design technology studio, the in-house package of software addresses design issues at different scales—from floor plan to master plan to sustainability strategy. By harnessing algorithms to large data sets, the new software gives designers rapid feedback on the impact of changes to interior-space plans or building configurations, so design decisions can be made much faster and earlier in the process. “We want to augment the power of our designers and make them more agile,” says Marc Syp, Gensler’s director of computation.

Some firms have introduced their AI tools commercially. One such software application is cove.tool which optimizes designs based on multiple parameters, including daylighting, energy use, code compliance, and cost. Launched in 2017 by Atlanta-based architects Patrick Chopson and Sandeep Ahuja as an outgrowth of their sustainable-design consulting firm, Pattern R+D, cove.tool is now their primary focus. The cloud-based app uses machine learning to process data collected from many different sources—such as construction-cost-data company RSMeans, building-product manufacturers, public databases, and the app’s users themselves—to model energy use versus cost at every stage of design. It is intended as a holistic tool, obviating the need to employ an array of stand-alone software programs to analyze designs for daylighting, shadows, HVAC, and such, says Ahuja. The company plans to add electrical, plumbing, and structural analysis to the tool soon. “The idea is to give architects all of the data they need at every step along the design process,” says Ahuja.

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Originally published in Architectural Record
Originally published in December 2021