Adaptive modularity: deep reinforcement learning for optimized modular housing massing

Abstract

The advancement of prefabrication technology has positioned modular housing as a precise, sustainable construction method that reduces greenhouse gas emissions and resource intensiveness while enabling a circular economy and fostering energy efficiency in meeting increasing housing demands. The design of massing configurations from modular units presents challenges due to the complexity of optimizing spatial arrangements to simultaneously balance architectural, structural, and environmental performance criteria and constraints. This thesis addresses the challenge of modular housing design, tackling the considerable complexity of optimizing spatial arrangements. The aim is to improve the design process through Deep Reinforcement Learning (DRL) to balance architectural criteria and constraints. The thesis investigates how DRL can enhance modular housing design by optimizing spatial configurations, overcoming the limitations of human-driven iterations, and leveraging the benefits of a feedback-driven system. It proposes a simulation environment based on a Cellular Automata (CA) voxel space, where an AI agent, trained using the Proximal Policy Optimization (PPO) algorithm, autonomously places massing cells to generate optimized housing configurations. These configurations are evaluated based on multiple performance objectives, such as ensuring efficient space utilization by maximizing the number of housing units within the buildable area, minimizing circulation space while maintaining accessibility to all units, and promoting the creation of housing units that are more compact in shape. This thesis focuses on the effort to optimize modular mass configuration for housing design. It outlines the steps taken in the process, the challenges faced along the way, and evaluates the success of the final configurations that were developed.