Experimental investigation on low-cement concrete at elevated temperature with preloaded conditions
Bibliographic record
Abstract
Abstract Concrete is one of the most widely used construction materials globally. Experimental and numerical observations have revealed that failure of concrete structures may occur not only during the heating phase but also during the decay phase of a fire. With the global imperative to reduce CO2 emissions from cement production, traditional concrete is increasingly being replaced with low-cement alternatives. However, there remains a lack of experimental testing regarding the effects of additional supplementary cementitious materials in concrete during and after fire. This paper presents preliminary findings from elevated temperature compressive tests conducted on concrete with three different mixes, two of which involved 40% and 50% cement replacement. The experimental programme indicates that, regardless of the mix type, internal temperatures recorded in the cylinders were minimally affected by differing mix proportions under identical heating scenarios. Additionally, the paper explores the influence of preloading on both the magnitude of peak thermal expansion and the time to reach peak thermal expansion. It is observed that a reduction in cement content results in comparatively more rapid thermal expansion. Furthermore, during the decay phase, the contraction rates are similar regardless of preloading conditions or the reduction of cement content, for identical heating and cooling scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".