Modeling the reliability of wood tension members exposed to elevated temperatures
Bibliographic record
Abstract
The merit of approaching fire safety design from the standpoint of reliability is the impetus of this paper. Reliability, a direct function of time to failure, is a measure of performance that falls naturally under a performance-based code. The objectives of this study focus on advancing our understanding of the structural behavior of light-frame wood members subject to tension and elevated temperatures, and on the time to failure under a given stress and temperature history. A model based on linear damage accumulation theory was developed to predict the time to failure. This model is based on a kinetic theory for strength as a function of temperature and stress, coupled with a kinetic term, to express the pyrolytic process as a form of damage. The model, which requires the short-term strength as an input, fits well to experimental data on nominal 2X4 structural lumber tested at three different rates of tension loading, and at 150, 200, and 250°C, and room temperature. The model also predicts, with reasonable accuracy, the behavior of lumber under constant-load at 250°C. It predicts that lower-grade material generally has a lower reliability index; however, those differences are insignificant as far as current design practices are concerned. The reliability is sensitive to variability in temperature but not to variability in stress.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".