Modelling the Response of Timber Beams Under Fire
Bibliographic record
Abstract
Abstract A fundamental requirement for analysing timber structures under fire is to consider the degradation of material properties with temperature. Therefore, the objective of this study is to propose a model that accounts for the variation of the thermo-physical properties, the development of char, and its evolution with temperature. This model integrates a sequential coupling of heat transfer analysis with structural response. The degradation of the material properties is accounted for through the regulatory approach recommended in Eurocode 5. The stress analysis employs an elasto-plastic model with nonlinear isotropic hardening. Implementation of the model is achieved within the Abaqus suite of finite element software using external subroutines. The model's predictions align well with experimental data, accurately reproducing both thermal and structural responses. Specifically, the model accurately predicts temperature profiles, displacements, and the depth of the charred layer, which initiates above 300 °C. Additionally, for rectangular sections, it was observed that exposure of all faces to fire results in a non-rectangular residual section. Furthermore, employing the temperature-dependent thermal property curves suggested by EC5 yields satisfactory results when predicting the fire resistance of softwood timber structures.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".