Health and comfort oriented automatic generative design and optimization of residence space layout: An integrated data-driven and knowledge-based approach

Abstract

During the initial design phase, space layout design plays a critical role in ensuring the ultimate performance of a residence. However, physical performance like energy consumption is usually prioritized, while health and comfort performance of residence is less explored. This study proposes an integrated data-driven and knowledge-based approach for automatic generative design of residence space layout focusing on health and comfort performance. First, explicit knowledge about key parameters of the layout design is obtained through quantitative analysis of large-scale health data. Second, with health and comfort as performance oriented, adjacency preference and noise score are introduced as objective functions and automatic generative design is carried out to obtain multiple layout alternatives. Finally, multi-objective deviation analysis is conducted to determine the optimal layout scheme. This study offers a practical and efficient tool through the whole design process, including pre-parameters analysis, during-process generation, and post-scheme evaluation, which contributes to improve decision-making.