Performance of high-strength concrete in fire
Bibliographic record
Abstract
This article presents some key results of studies carried out by NRC's Institute for Research in Construction, comparing the performance of normal-strength and high strength concrete columns in fire situations. Particular attention is devoted to the problem of spalling encountered with high-strength concrete. As the use of high-strength concrete in buildings and other structures has increased, so too have concerns about its fire performance - especially the problem of spalling. As a result, the National Research Council's Institute for Research in Construction (IRC) is carrying out experimental and numerical studies to address these concerns. High-strength concrete (HSC) finds applications in the construction of bridges, offshore structures and infrastructure projects because of its improved structural performance, particularly in strength and durability, compared with normal-strength concrete (NSC). Its use has been extended to buildings in recent years, especially for columns. Its higher compressive strength allows for smaller columns, thus reducing costs. Smaller columnstake up less space, which can have huge financial implications for structures like parking garages as more cars can be accommodated on each floor, thus increasing profits. Design professionals are always concerned about providing appropriate fire resistance for structural members. The most recent CSA standard for the design of concrete structures for buildings ' CSA-A23.3-M94 ' provides detailed guidelines for the design of HSC structural members. However, neither the standard nor the 1995 edition of the National Building Code of Canada provides specific guidelines for evaluating the fire performance of HSC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".