Reliability analysis of timber columns under fire load using numerical models with equivalent section temperature
Bibliographic record
Abstract
This paper presents a modelling method for timber columns exposed to fire using the equivalent section temperature (EST), aiming to reduce the computational cost of the sequential thermal–structural analysis. The EST is to use a single temperature value across the section that can provide the same compression strength or bending stiffness as the original temperature field. The temperature–time curves, displacement curves, and fire resistances of the developed model and experimental tests are compared. The developed column models are further validated by a large test dataset. The reliability of timber columns under fire is evaluated based on the developed numerical model and trained surrogate model Polynomial Chaos Kriging (PCK). The random variables are considered for thermal and structural analysis and the failure probability of the column with increasing exposure time is calculated through different reliability assessment methods. • The equivalent section temperature (EST) method is developed for timber columns exposed to fire to simplify the numerical models for sequential analysis. • The timber column models using EST method are validated based on a large test dataset. • The performances of timber column models with and without EST are compared. • The reliability analysis is carried out using different assessment approaches based on the developed numerical models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".