Fire resistance research needs for high performing materials
Bibliographic record
Abstract
In recent years, there has been a growing interest in the use of highperforming materials (HPM), such as high strength concrete (HSC) and fibre-reinforced polymers (FRP), in civil engineering applications. HPM areoften used as structural members in buildings, without fully addressing the fire related issues. At present, there is very little information available on the performance of HPM under fire conditions. Many of the HPM have special characteristics and hence, traditional fire protection measures, as well as conventional fire resistance assessment methods (prescribed in standards), may not be applied to enhance or evaluate their fire resistance. There is an urgent need for the development of fire resistance design guidelines, for the wider application of HPM in buildings and other infrastructure projects where fire resistance requirements are to be satisfied. The research needed for the development of such guidelinesinclude: improved methods for fire resistance assessment; data on material properties (thermal, mechanical, deformation) as a function of temperature; fire resistance experiments on large/full scale structural systems; validated numerical models and parametric studies. The output from this research will be simplified design guidelines that can be incorporated into codes and standards to facilitate integration of fire resistance design with structural design. Undertaking of this research, followed by technology transfer, should lead to wider use of HPM, and result in cost-effective and fire resistant structural systems.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".