Comparing Properties of Concrete Containing Electric Arc Furnace Slag and Granulated Blast Furnace Slag
Bibliographic record
Abstract
For sustainable development in the construction industry, blast furnace slag has been used as a substitute for cement in concrete. In contrast, steel-making slag, the second largest by-product in the steel industry, is mostly used as a filler material in embankment construction. This is because steel-making slag has relatively low hydraulicity and a problem with volumetric expansion. However, as the quenching process of slag has improved recently and the steel making process is specifically separated, the properties of steel-making slag has also improved. In this context, there is a need to find a method for recycling steel-making slag as a more highly valued material, such as its potential use as an admixture in concrete. Therefore, in order to confirm the possibility of using electric arc furnace (EAF) oxidizing slag as a binder, a comparative assessment of the mechanical properties of concrete containing electric arc furnace oxidizing slag, steel-making slag, and granulated blast furnace (GBF) slag was performed. The initial and final setting, shrinkage, compressive and split-cylinder tensile strength of the slag concretes were measured. It was found that replacing cement with EAF oxidizing slag delayed the hydration reaction at early ages, with no significant problems in setting time, shrinkage or strength development found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".