Machine Learning for Human Design: Developing Next Generation Sketch-Based Tools

Abstract

Formal computational approaches in the realm of engineering and architecture, such as parametric modelling and optimization, are becoming increasingly powerful, allowing for systematic and rigorous design processes. However, these methods often bring a steep learning curve, require previous expertise, or are unintuitive and unnatural to human design. On the other hand, analog design methods such as hand sketching are commonly used by architects and engineers alike. They constitute quick, easy, and almost primal modes of generating and transferring design concepts, which in turn facilitates the sharing of ideas and feedback. In the advent of increasing computational power and developments in data analysis, deep learning, and other emerging technologies, there is a potential to bridge the gap between these seemingly divergent processes to develop new hybrid approaches to design. Such methods can provide designers with new opportunities to harness the systematic and data-driven power of computation and performance analysis while maintaining a more creative and intuitive design interface. This thesis presents a new method for interpreting human designs in sketch format and predicting their structural performance using recent advances in deep learning. Furthermore, the thesis will also demonstrate how this new technique can be used in design workflows including performance-based guidance and interpolations between concepts.