Utilization of Numerical Techniques to Predict the Thermal Behavior of Wood Column Subjected to Fire Part C: Sensitivity Analysis
Bibliographic record
Abstract
Abstract. Theoretical studies have been carried out to predict the fire resistance of different configuration of wood columns as done in Part A and Part B of this set of research papers. This theoretical study is a continuation of previous studies which were carried out at National Research Council Canada. Mathematical models to calculate the temperatures, deformations and fire resistance of the columns have been developed for Part A and Part B. Calculated results are compared with those measured in several tests. The results indicated that the model is capable of predicting the fire resistance of wood columns with an excellent accuracy. By using the model, the fire resistance of wood columns can be evaluated for any value of the significant parameters such as load, physical dimensions, mechanical properties and even the chemical properties without the necessity of experiment. This research paper carried out sensitivity analysis of a square column at elevated temperature in order to know the effect of some parameters towards the mechanical strength of the column. The parameters that have been studied are initial moisture content, Young’s modules, initial compression stress, specific gravity, thermal conductivity and the physical dimension of the column cross section. Both of the temperature history versus time for the selected elements and the fire resistance has been analyzed using existing results. All the analyses have been carried out for the square cross-section under axial
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".