Thermal Impact on the Physical and Transfer Properties of Slag Cement and Portland Cement Concretes
Bibliographic record
Abstract
The cement industry confronts significant environmental challenges, primarily due to extensive raw material and energy consumption, and consequential substantial greenhouse gas emissions such as carbon dioxide.Escalating energy expenses and stringent environmental regulations mandate the reduction of industrial emissions through the incorporation of industrial by-products like blast furnace slag.In this study, a comparative analysis was conducted to evaluate the physical and transport properties of concrete made with slag cement versus that made with Portland cement, particularly after exposure to high-temperature conditions.Specimens, cured for 90 days at 20℃ in water, underwent a series of four heating-cooling cycles at incremental temperatures of 160, 300, 400, and 650℃, with a consistent heating rate of 1℃/min.Various durability indicators, including mass loss, water-accessible porosity, gas permeability, capillary water absorption coefficient, and chloride ion apparent diffusion coefficient, were measured.It was observed that an increase in the temperature of exposure led to a reduction in weight, porosity, permeability, diffusivity, and capillary water absorption in both concrete types.Notably, the slag cement concrete exhibited marginally superior durability parameters compared to the Portland cement concrete, with the exception of porosity.Empirical correlations derived from the experimental data between porosity, water absorption, and gas permeability facilitate the assessment of the apparent diffusion coefficient in fire-damaged concrete incorporating blast furnace slag, up to a temperature of 650℃.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".