Machine learning can be a powerful tool to aid in the creation of adaptive architecture models and developing interesting possibilities in design that consider principles of a circular economy.
Switching architecture to become a circular economy poses new challenges in how we organize construction, utilization and recycling of our living spaces. Architects will not only design the building as an end product but also its entire timeline – how it’s conceived, how it’s used and what will happen with the materials after disassembly. In this context, machine learning can aid the architects in a range of ways among others:
- Predicting the usage patterns (wear and tear, maximizing use phase)
- Responding to the occupant’s needs (efficiency improvements)
- Intelligent scrap material evaluation (addressing recycling)
During this workshop, the participants will get a chance to learn about machine learning in the design context, with the applications ranging from the clustering of elements to space planning applications for generating interior architecture responsive to user needs. For that purpose a parametric model will be developed, which will serve as a data source. Using unsupervised and supervised learning techniques, the participants will then perform a range of tasks addressing the model solution space.