The Effects of Supplementary Cementitious Materials (SCMs) on the Residual Mechanical Properties of Concrete after Exposure to High Temperatures—Review
Bibliographic record
Abstract
Fire events remain a severe hazard despite significant progress in the construction industry. The high-temperature resistance of concrete structures highly depends on the properties of their ingredients. The present study provides an analytical review of the available experimental studies investigating the influence of high temperatures on the residual mechanical characteristics of concrete, namely compressive and tensile strength, as well as the modulus of elasticity. This paper primarily focuses on the influences of supplementary cementing materials, including silica fume (SF), fly ash (FA), and ground granulated blast furnace slag (GGBFS), as well as different aggregate types. The literature findings show that the mechanical characteristics of concrete are degraded dramatically due to heat-caused changes in concrete. In addition, the mechanical properties of concrete are highly influenced by the types of aggregate and the amount of supplementary cementing materials at both high temperatures and room temperature. The inclusion of FA and GGBFS improves the high-temperature resistance of concrete. However, high contents of FA and GGBFS result in adverse effects on the properties of concrete. This review will provide a basis for future studies and enhance the knowledge of the behaviour of reinforced concrete structures subjected to high temperatures.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".