Enhancing Compressive Strength of Sulfate-Rich Concrete Using Electromagnetic Fields
Bibliographic record
Abstract
Concrete deterioration due to sulfate attack is one of the factors for early failure.Considering that sulfates in aggregates are the most important problem related to concrete in the cities of the Middle East and especially Iraq, so there was a need to develop a device to enhance the strength of concrete.In the current experimental research, we sought to discover how to treat and improve the performance of fresh concrete containing high percentages of sulfate salts in sand, using a magnetic field generation device with an electrical principle made locally.The work included the beginning of casting 27 models of concrete cubes that were treated using different magnetic intensities and examined to select the highest strength, which was 3000 Gauss, which was adopted in this research.Two groups of mixtures were prepared and poured using 72 cubes and prisms.The first group was divided into two reference mixtures not treated with a magnetic field.Two types of sand with different sulfate ratios with resistant Portland cement were used.The second group was prepared using the same method and working conditions, except for treating the concrete mixture with electromagnetic fields.The results showed an encouraging improvement in the compressive strength of magnetically treated concrete, where the rates of increase in compressive strength were (5.45, 5.5, 5.7%) with 0.15% SO3, and (7.2.7, 4.6%) with 0.6%.SO3 for a period of (7.28 and 90) days, respectively, compared to control mixtures.This technology can be applied during the pouring process on job sites.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".