Structural response model for wood stud wall assemblies: theory manual
Bibliographic record
Abstract
To minimize the costly consequences of fire, load-bearing wood-framed assemblies, such as other building fire barriers, are required to exhibit acceptable fire resistance to contain the fire within the compartment of fire origin. This containment will delay fire spread to other compartments, as well as provide for safe evacuation and rescue operations. The fire resistance of wood-frame assemblies can be evaluated by subjecting loaded assemblies to the standard ULC/S101-M891 tests or using calculation methods.The National Research Council of Canada (NRC) and the Canadian wood industry have joined in a collaborative effort to develop an analytical model to predict the fire resistance of lightweight wood-frame wall assemblies exposed to standard and real fires. The model is comprised of two sub-models: a heat transfer sub-model and a structural response sub-model. This report describes the theoretical framework of the structural response sub-model, which predicts the time to failure and deflection of structurally-loaded wood-framed wall assemblies. This report also presents a sample example of a simulation through the comparison between experimental and analytical predictions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".