Integrating acoustic simulation in architectural design workflows: the FabPod meeting room prototype
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it