Sulfate and Acid Attack Resistance of Iron Slag and Recycled CoarseAggregate Concrete
Bibliographic record
Abstract
This study specifically explores the impact of mutual inclusion of 'iron slag (IS) and recycled concrete aggregates (RCA)' in non-traditional concrete (NTC) aiming to estimate the resistance against sulfate and acid attacks.The newness of this experimental study is in exploring of role of IS along with RCA in NTC for abovementioned durability properties, which have been comparatively underexplored.Natural sand (NS) and natural coarse aggregates (NA) were replaced with IS and RCA at constant (30%) and varying (25%-100%) amount respectively.In all, six (6) numbers of NTC were tested for sulfate and acid attack resistance while compressive and tensile tests were performed in general support till 90 days of standard curing.The resistance against sulfate and acid attack was measured in relation to variation of mass and corresponding strength performance of designed NTC.The highest increase in mass (by 6%) was noted for NTC with 30% of IS and 100% of RCA.Likewise, for the same NTC the reduction in mass due to acid resistance was limited to 17%.While NTC with IS (IS30RCA0) only and 25% of RCA (IS30RCA25) emerged as equivalent performers in terms of resistance and strength outcomes.These findings not only demonstrate a positive potential of mutual inclusion of IS and RCA in NTC but also present a promising start towards utilization of sustainable construction materials.
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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 | 0.000 |
| 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) | 0.001 | 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".