Effect of Temperature on the Early-age Hydration and Setting Behaviour of Mixes Containing GGBS
Bibliographic record
Abstract
Over the past years there has been an increasing trend to use supplementary cementitious materials in concrete to improve its sustainability credentials and durability properties. Perhaps amongst the most popular supplementary cementitious materials is ground granulated blast-furnace slag (GGBS). While the hydration and setting characteristics of mixes with GGBS cured under standard conditions (20°C) has been adequately investigated, the effect of temperature on heat of hydration and setting and the interrelation of these properties has not been evaluated for GGBS containing mixes. In this study, the heat of hydration and setting behaviour of mixes with various levels of GGBS (0, 20, 35, 50 and 70%) cured under elevated temperatures (20, 30, 40, 50 and 60°C) is determined. Elevated curing temperature accelerates the hydration reactions and can significantly reduce the setting time of GGBS-containing mixes. The investigation of heat of hydration at very early ages, can provide an indication of the initial and final setting times of cementitious mixes. The “apparent” activation energy used to characterise temperature sensitivity of cementitious systems, is calculated based on heat of hydration and setting time measurements. It was found that the “apparent” activation energy increases with GGBS content for both heat of hydration and setting behaviour. The value of “apparent” activation energy differs significantly depending on the material property that is considered, such as compressive strength, heat of hydration or setting time.
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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 | 0.000 | 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 | 0.001 |
| 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".