Deep Generative Design
Deep reinforcement learning for performance-based design assistance
J.F.H. Lemmens (TU Delft - Architecture and the Built Environment)
A Charalampos – Mentor
M Turrin – Graduation committee member (TU Delft - Design Informatics)
C.C.J. van Engelenburg – Coach (TU Delft - Building Knowledge)
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Abstract
Architects play a pivotal role play in the sustainable development of the built environment, shaping spaces that minimize environmental impact while enhancing human well-being. However, due to the escalating complexity of design requirements, designers are becoming underequipped to solve today’s design challenges. Solving such problems involves balancing multiple different co-dependent factors, such as spatial configuration, daylight satisfaction and embodied carbon. Currently this is addressed through inter-disciplinary collaboration where different parties focus on their own specialisation. As a result, a significant gap exists between design and building performance analysis leading to underperforming and expensive solutions.
An artificial intelligence-powered platform could aid in the design process by bridging the gap between different disciplines. Such a platform could ensure that all aspects of a project are considered holistically, reducing the risk of conflicting decisions that cause lacking performance and unnecessary expenses. Simply consolidating information, as done with building information modelling (BIM), is not sufficient. Instead, the platform must provide performance-driven recommendations, offering deeper insights when conflicts between different disciplines arise.
This work aims to develop a framework through which deep learning methods can be applied to create floorplans informed by performance-based criteria and user guidance. Since functionality is heavily dependent on site-conditions and requirements laid out by the client, the relationship between form and function must be uncovered dynamically. To achieve this, inspiration will be drawn from the field of reinforcement learning which allows the training of a neural network without the use of training data.