Automated architectural space layout planning using a physics-inspired generative design framework
This addresses the time-consuming and repetitive manual process of space layout planning in architecture, though it appears incremental as it builds on existing generative and evolutionary methods.
The authors tackled the problem of automated architectural space layout planning by developing a physics-inspired generative design framework, which generates a wide variety of design suggestions applicable to complex problems.
The determination of space layout is one of the primary activities in the schematic design stage of an architectural project. The initial layout planning defines the shape, dimension, and circulation pattern of internal spaces; which can also affect performance and cost of the construction. When carried out manually, space layout planning can be complicated, repetitive and time consuming. In this work, a generative design framework for the automatic generation of spatial architectural layout has been developed. The proposed approach integrates a novel physics-inspired parametric model for space layout planning and an evolutionary optimisation metaheuristic. Results revealed that such a generative design framework can generate a wide variety of design suggestions at the schematic design stage, applicable to complex design problems.