Acoustic prediction and testing for "Basajaun" EU Project demo building using neural network, prediction for innovative wooden partition wall with composites and bio-based insulation
Bibliographic record
Abstract
"Basajaun" is a major European innovation action about sustainable building with wood. The main objective is to demonstrate how wood construction chains can be optimized to foster both rural development and urban transformation whilst being connected with sustainable forest management in Europe. The project scrolls the full value chain of wood construction in Europe, and it include a demo project in south of France demonstrating implementation of various innovations, among which the studied innovative facade made of pultruded composites, structural insulated panels (SIP) plywood, wooden cladding and bio-based insulation. In building projects, partition walls are commonly designed for mechanical stability, thermal insulation and fire safety, in this article authors focus on acoustic aspects starting from design, optimization, lab testing, implementation specifications and in situ validation. For the design and optimization phase, authors developed prediction tool based on Artificial Intelligence. Programming and data feeding of the tool is described, and predictions are compared to laboratory measurements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".