Rectification of “restrained vs unrestrained”
Bibliographic record
Abstract
Summary For furnace testing of fire‐resistant floor and roof assemblies in the United States, the ASTM E 119 standard (and similarly the UL 263 standard) permits two classifications for boundary conditions: “restrained” and “unrestrained.” When incorporating tested assemblies into an actual structural system, the designer, oftentimes a fire protection or structural engineer, must judge whether a “restrained” or “unrestrained” classification is appropriate for the application. It is critical that this assumption be carefully considered and understood, as many qualified listings permit a lesser thickness of applied fire protection for steel structures (or less concrete cover for concrete structures) to achieve a certain fire resistance rating if a “restrained” classification is confirmed, as compared with an “unrestrained” classification. The emerging standardization of structural fire engineering practice in the United States will disrupt century‐long norms in the manner to which structural behavior in fire is addressed. For instance, the current edition of the ASCE/SEI 7 standard will greatly impact how designers consider restraint. Accordingly, this paper serves as an exposé of the “restrained vs unrestrained” paradigm in terms of its paradoxical nature and its controversial impact on the industry. More importantly, potential solutions toward industry rectification are provided for the first time in a contemporary study of this paradigm.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".