Integrating acoustic simulation in architectural design workflows: the FabPod meeting room prototype
Bibliographic record
Abstract
Sound is an important part of our experience of buildings. However, architects design largely using visually based techniques and largely for visual phenomena. Aiming to address this problem, the research presented in this paper proposes four digital design workflows that integrate acoustic computer simulation into architectural design. These techniques enable architects to design for both visual and acoustic criteria. The goal is to develop rapid and accessible workflows for architects that allow acoustic performance to be tuned as geometry and materials are modified at the scale of the room, and also at the scale of the surface. The discovery and testing of these techniques takes place within the design of the FabPod, a semi-enclosed meeting room situated within an open-plan working environment. The project builds on previous research investigating the design principles, the acoustic performance, and the fabrication methods of hyperboloid surface geometry. Four design workflows were developed: two of these investigate the acoustic performance of the room and use existing acoustic simulation software, and the other two workflows investigate the acoustic performance of the surface and use custom-written scripts to calculate and visualize sound scattering. This paper presents the background to the study, outlines the digital workflows, describes how they integrate acoustic simulation, and shows some of the data produced by these simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".