Compatibility study between building information modeling and acoustic simulation software
Bibliographic record
Abstract
Geometric and non-geometric data from Building Information Models (BIM) need to be manually reconstructed in acoustic simulation software to examine building acoustic performance. This process is time-consuming, error-prone, and the accuracy of acoustic analysis results will depend on the expertise of the user. The process is also unidirectional (i.e., from BIM to the acoustic software); meaning that simulation outputs can’t be easily retrieved back to the BIM. Thus, over the last decade, several attempts have been made to integrate acoustic analysis into BIM-related software during the conceptual design phase. This paper aims to facilitate the interoperability between BIM and acoustic simulation engine by improving the information exchange between them. A case study with an educational space at Concordia University in Montreal is carried out to validate the developed system and examine the practicality and efficiency of this process in action. The paper will investigate: 1) extraction of geometry data from the BIM software; 2) enriching the BIM with data regarding acoustic absorption coefficients, via an external database; 3) calculation of acoustic qualities including reverberation time, and 4) visualization of the simulation results in the BIM software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".