Housing Innovation Lab

In architectural practice, speculative design exploration and building performance assessment are frequently disconnected from one another, although there is significant value in the early integration of performance feedback for promoting more sustainable design outcomes. Parametric modeling environments bridge the gap between design and building performance simulation. However, simultaneous assessment of multiple building performance indicators in fast-paced early design stages is still challenging. This research explores how to obtain multi-disciplinary performance feedback from minimal massing model input and examines how this data can be used to dynamically evaluate early design variants.

Sustainability and material efficiency are essential considerations in architecture. However, they are often evaluated late, leaving out optimization potentials inherent in architectural choices. Easy-to-use computational tools facilitate the integration of performance parameters into design decision-making, but since different simulation environments require different geometric inputs, the simultaneous consideration of multiple constraints is not feasible with reasonable modeling effort. This paper capitalizes on existing simulation tools and presents a novel procedure, AutoFrame, that converts architectural massing models into structural simulation input models to streamline daylight simulation, embodied, and operational carbon assessment in schematic design. Three reference buildings are used for the validation of the approach, and a speculative case study demonstrates how the multi-disciplinary performance feedback guides design decisions while maintaining the flexibility of early design exploration.