Drawings have been the traditional medium architects use to organise ideas and explore design options. When designing, we intuitively think about associations and interrelations between different design constraints and possible design solutions. Translating this cognitive process onto paper poses many limitations. When translating design intent to paper, it’s hard for designers to manage these associative relationships, and it is up to them to ensure the design’s internal consistency. This limitation spurred the need to have a set of standards in the form of predefined design solutions and tectonic systems, known as typologies.
Typologies allow the designer to use drawings to explore and iterate over the design solution space within a set of structural and formal constraints, making it easier to manage associative relationships. Architects and engineers have long relied on typologies in the form-finding process. Still, given the structural and formal constraints typologies introduce, it becomes hard to innovate and limits the possible design options to a small solution space. Various approaches have been explored in the past that try to bypass the use of typologies. For example, in the 19th-century, architects such as Antoni Gaudí started to explore physical form-finding methods, where physical forces could drive the final form. These approaches have remained a niche within the industry.
Even as digital tools became widely available, they did not directly translate into new modes of design thinking. CAD (computer-aided design) was initially translations of traditional drawings’ additive logic to a digital environment. It hasn’t been until more recently that software and tools that allow for, and help manage, associative relationships have proliferated. This adoption was fueled by both an increase in computational resources and new approaches to design, such as those implemented in the early 20th century by the Italian architect and structural engineer Sergio Musmeci embracing the role of science as a creative tool. Today, Building Information Modeling (BIM) and tools such as dynamo and grasshopper make it possible for designers to take a more computation approach, defining processes and relationships that guide the design process rather than a final output. These new design methods became known as parametric design. Architectural firms such as Zaha Hadid Architects have pushed the boundary of what is possible with these new approaches and tools.
However, parametric design has created its own set of limitations. Optimisation routines, which are often used in parametric design, work well when solving specific sub-problems such as sizing structural elements or optimising typologies but fall short in more complex applications. Additionally, the space of possible design solutions generated by these tools and algorithms quickly becomes too big for anyone to explore in any meaningful way. To tackle this, researchers from the Department of Architecture of ETH Zürich have recently proposed a new framework for generating design options that are novel, diverse, and structurally informed.
The proposed framework, published in the International Journal of Architectural Computing, goes beyond typologies and optimisation routines by taking advantage of recent advances in machine learning and leveraging these with feedback from designers, an approach known as “human in the loop” machine learning. The framework is loosely based on a generic scheme of design operations formalised by Israeli architect Rivka Oxman. It consists of 4 procedures outlined in their paper: generation, clustering, evaluation, election, and regeneration. Researchers propose three main algorithms related to these operations: combinatorial equilibrium modeling is used in the generation stage (similar to Geometric Equilibrium through 3D Graphical Statics approach), self-organising maps to clusters the outputs, and gradient-boosted trees to learn user preferences and incorporate subjective criteria during the election stage. The framework is flexible, and these specific algorithms could be replaced by different ones depending on specific needs. Data Aided Design had the chance to speak to Karla Saldaña Ochoa, the paper’s principal author, to discuss the work in more detail.