Background

The article "Generative Design is Doomed to Fail" by Daniel Davis critically examines the limitations of generative design, arguing that it simplifies the design process in a way that's unrealistic and inefficient. Davis contends that the need to manually create complex algorithms and the overwhelming number of low-quality options generated make this approach less effective than traditional design methods. Additionally, the difficulties in evaluating and selecting the best designs from a large pool, along with the industry's slow adoption of this technology, suggest that generative design may not revolutionize the field as some proponents suggest.

Challenge and Solution

This project is going to answer two of the issues of Generative Design that had mentioned in article.

For addressing the issues of "What you can measure isn't what matters" and "Designers don't work like this" in generative design, you could explore developing a system that integrates more human-centered and flexible metrics, along with creating an iterative design process that mimics how designers actually work. This could involve creating algorithms that not only generate options but also learn from each iteration based on designer feedback, thus adapting and improving over time. Such a system could help bridge the gap between the quantitative output of generative design and the qualitative judgment required in professional design settings.

Tryitout

The demo is a questionnaire for potential customers of the Bank Cafe shop.

System Diagram

Project Statement

Generative Design, while encountering challenges in its integration into the broader spectrum of architectural design, exhibits significant potential for enhancing specific phases, as demonstrated in my ongoing project. In particular, I have found it to be a valuable tool in expediting the Test Fit process of the Schematic Design phase.

In the context of my current project, Generative Design has proven instrumental in rapidly generating and evaluating multiple design alternatives. By inputting specific parameters and constraints, the tool facilitates the exploration of diverse spatial configurations, allowing for quick identification of optimal solutions. This not only accelerates the design iteration process but also ensures a more informed decision-making approach.

The ongoing nature of my project serves as a real-time case study, showcasing how Generative Design contributes to a faster and more effective design process. While acknowledging its limitations in addressing the entirety of architectural design, the focused application of Generative Design in the Test Fit phase underscores its potential as a powerful ally in achieving both efficiency and design excellence.