Residual Load Bearing Capacity of Structures Exposed to Fire
Bibliographic record
Abstract
When fire occurs in buildings, depending on the relative severity of the fire, there might be fire-induced damage which affect the performance of building elements. While collapse of buildings, in fires, may be rare events it is not uncommon to have damage or distortion in various structural elements. The extent of damage to a structure is dependent on the fire intensity, duration of fire, geometry, materials used in construction and the load intensity. In many cases the structural members might have substantial strength and often can be restored to its original shape through repairs. Before undertaking such repairs an assessment of the building has to be carried out to determine the extent of damage and the residual load-bearing capacity of structural members. In this paper, the application of computer program SAFIR for determining the residual load bearing capacity is illustrated though three case studies; a simply supported beam, a column and a restrained beam. The three elements were modeled in two configurations, steel and reinforced concrete, and were designed to have the same fire resistance ratings. The analysis was carried out in a scenario that includes heating under a natural fire, cooling down to ambient temperature and then loading to failure. Results from three case show that the load bearing capacity was hardly modified, by the fire damage, in a simply supported beam, was increased by the effect of an axial restraint, especially in the concrete beam, and was reduced in the column, especially in the concrete column.
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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 | 0.001 | 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) | 0.001 | 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".