Ai Generative Design Architecture

[Malcolm Lozada]

GenerativeDesign in Autodesk Revit is a new feature now available through the AEC Collection with Revit 2021. Now you can quickly generate design alternatives based on your goals, constraints, and inputs to give you higher performing options for data-driven decision making.

Generative design is used to provide practitioners the ability to quickly explore, optimize, and make informed decisions to complex design problems. Think of generative design software as an assistant that helps with creating, testing, and evaluating options.

Get started with Generative Design in Revit, available in Revit 2021 to all users with a subscription to AEC Collections, as well as EBA customers. Assign users through Autodesk Account and then access the feature through the Manage tab in Revit. Single-product subscribers running Revit 2021 can access and build on the code through Dynamo for Revit and use it to create custom design studies using Revit data and geometry.

Trial and error. On a napkin, on tracing paper, or on a black CAD background, much of an architect's work is to make and redo lines, shapes, objects, and images. Discard, start over, repeat. Between an initial idea and a final project is a long and exhausting path. This difficulty lies in the root of designing as a process of making infinite decisions, where a change can influence countless other elements and consequently is an exercise in choosing benefits and concessions. These choices can take a number of forms, from determining how much area to cover while minimally impacting the environment to fitting as many work tables in an office as possible without losing good circulation. Each require many studies or considerations to arrive at the most suitable option. For example, the position of a window, even if it looks great on a the faade, can make the location of the bed in a dormitory unfeasible or increase the building's energy consumption.

Of course, there are always tight deadlines and budgets throughout any project, with the client often in a hurry and willing to devote limited time to thinking about every possible combination or the precise appropriateness of each decision. This stage is where, increasingly, the concept of Generative Design has appeared in architecture.

For example, Brazilian architect Guto Requena used generative design to create stools whose volumes were shaped by the rhythm and melody of popular Brazilian music. The resulting organic shapes were then cut into pieces of marble. In the Netherlands, startup MX3D joined forces with Laarman Lab, Heijmans, Autodesk and several other supporters to create a pedestrian bridge produced with 3D printed steel. The team worked with generative algorithms to produce successive design iterations under a given set of parameters. After determining a shape, digital simulations of the bridge were performed, removing excess material by mixing structural calculations with geometric manipulation, teaching the algorithm to recognize which parts of the bridge were least crucial. In other words, the project used Generative Design to combine the possibilities of the machine's 3D printer with various tests of shapes and design possibilities using minimal parameters.

Another example is the research project Evolving Floor Plans, which explores speculative and optimized architectural organizations using generative design. Classrooms and the circulation of people in a hypothetical school were generated through a genetic algorithm programmed to minimize walking time, use of corridors, and other parameters. The floor plan "evolves" from genetic coding using indirect methods, such as contracting graphs and growing corridors, using an algorithm inspired by ant colonies.

However, generative design will not always generate complex and highly organic shapes. It can contribute to the repetitive and boring design processes that we are already very used to. Last year, Sidewalk Labs announced the development of a generative design tool that uses machine learning and computer design to create urban planning scenarios. Using geographic information, urban guidelines and regulations, street layouts, orientation, weather patterns and building heights as input data, the tool generates a series of possible scenarios for architects and planners to evaluate and refine their final product. With machine learning, the system has the ability to improve the task and generate improved projects as it accumulates experience.

For the design of the Autodesk offices in Toronto, Generative Design played an essential role. The process was initiated by collecting opinions from employees and managers about work styles and location preferences, which were transformed into data. Consequently, six primary measurable parameters were defined:

Evidently, there were elements that could not be altered, such as vertical circulation, bathrooms and plumbing, and the building structure. Working around these constants, the process was automated to explore thousands of layout configurations from hundreds of combined variables, obtaining the performance classification of each of the options for the indicated parameters. It is worth noting that after the space is reoccupied, and the productivity or frequency of use of each space is observed, it is possible to validate or change some of the parameters to make the model even more accurate for this or other projects. Also, if any of the parameters change, such as increasing or decreasing the number of teams or even if a new auditorium is needed, they can be included in the database to create new iterations.

In a project based on the concept of Generative Design, the computer is no longer just a place to graph the project, or even to register materials and geometries. Rather, it becomes a co-author of the project, presenting multiple design alternatives including classifying them from the most to the least suitable according to prerequisites and premises designated by the designer. Yet while computers can help organize and prioritize these decisions, they cannot actually make them. Only people can make the final decision. A new tool in Revit uses Generative Design to bring the scale, speed, and precision of algorithmic problem-solving to design decision-making.

[1] Official website of Celestino Soddu and Enrica Colabella architects. Available at this link.

[2] Ebook Autodesk. Demystifying Generative Design For Architecture, Engineering, and Construction. Available at this link.

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Recently my friend Daniel Davis posted an article called Generative Design is Doomed to Fail. It is no doubt a fascinating article written by Daniel, and has some very poignant analogies, and logic on why Daniel thinks that Generative design is not...

I have seen on the internet a conference by three professors in an american university showing the project of the World Trade center Twin towers. And they laughed about the architecture they think to be a huge ordinary office building.

They laugh, but I laugh about their ignorance. It was just a very subtile architecture, mixed with discreet but revolutionary engineering new concepts.

You have two period in tall building history about technics, from the first pyramid to the Twin Towers, and from the Twin towers until now.

What is not covered here is generative design in more purely function driven applications, what we might call Generative Engineering. Designing parts and mechanical assembly by inputting the physical needs and letting the algorithm cook up an optimized shape that performs the required function with a minimum of material. This process combined with new manufacturing techniques like multi axis machining and 3D metal printing yield some amazing, sometimes counter intuitive solutions, to mechanical problems. These fluid, bone like, designs that resemble biomimicry often have a beauty all their own. This is an exciting aspect of Generative Design to me, where the subtle human psychology of architecture that an algorithm does not appear well suited for are not a part of the process and an AI can focus on doing what it good at, crunching data and returning ideas quickly.

Designing parts and mechanical assembly by inputting the physical needs and letting the algorithm cook up an optimized shape that performs the required function with a minimum of material. This process combined with new manufacturing techniques like multi axis machining and 3D metal printing yield some amazing, sometimes counter intuitive solutions, to mechanical problems. These fluid, bone like, designs that resemble biomimicry often have a beauty all their own.

Maybe there is a misunderstanding on my part, but topology optimization is an application of algorithmic design, it is not algorithmic design.

I think that no artistic creation can be obtained with the method which is presented. I have doubts about the application presented as current at Zaha Hadid.

In architecture, drawing is a technical and artistic expression that involves creating visual representations using various analog instruments. While drawing remains relevant and current in practice today, efforts have been made to carry out architectural tasks and studies more efficiently. The drafting machine, a significant development in this regard, enabled precise strokes using fewer instruments. However, the emergence of computational tools, such as computer-aided drafting (CAD), has revolutionized the workflow by leveraging the advantages offered by computers. Architects can now play a more direct and creative role in the design process, reducing their reliance on time-consuming drawing and repetitive tasks. Moreover, workflow enhancements have fostered more effective collaboration among different stakeholders in the architectural process.

The use of computers has set the stage for a digital revolution in architecture, catalyzing the emergence of new tools and platforms ranging from 3D printing to metaverse space design. In digital and physical environments, it is evident that designing spaces is essential to address financial, technical, and human requirements. Consequently, the ongoing technological revolution in architecture has brought forth various generative design tools and platforms that cater to different aspects, such as fit testing, feasibility studies, and comprehensive building and interior design.

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