Reinforcement learning for architectural design-build: Opportunity of machine learning in a material-informed circular design strategy

Abstract

This paper discusses the potentials of reinforcement learning in game engine for design, implementation, and construction of architecture. It inaugurates a new design tool that promotes a material-informed design-build workflow for architectural design and construction industries that achieves a comprehensive circular economy. As a proof of concept, it uses the project "Reform Standard", a machine-learning-based searching system that designs new shell structures composed of existing wasted materials, as a demonstration to discuss how reinforcement learning, machine vision and automated searching algorithm in the game engine can promote a material-aware design and converts wastes into construction materials. The demonstrator project sorts and transforms irregular chunks of wasted broken plastics into a new form. Instead of recycling those wastes in an energy-intensive process, the game engine is capable of finding the intricacy and new machine-oriented aesthetics in those otherwise neglected wastes. Furthermore, future research directions such as robotic-aided construction are discussed by exposing the potentials and problems in the demonstrated project. Finally, the future circular strategy is discussed beyond the demonstrated tests and local uses. The standardization of material, legislation and material lifecycle needs to be comprehensively considered and designed by architects and designers during conceptual design phase.