Chloride intrusion into thermally damaged self-compacting concrete
Bibliographic record
Abstract
The post-heating resistance of limestone self-compacting concrete (SCC) against chloride intrusion is investigated considering key parameters such as water-to-cement ratio (0.4, 0.45, and 0.5), relative humidity, and elevated temperature (300 °C and 400 °C). The SCC mixtures were proportioned to conform to universal specification with regard to different workability requirements. Chloride profiles were determined for post-heated and companion prismatic (100 mm × 100 mm × 250 mm) specimens, kept at room temperature. Consequently, diffusion coefficients were determined based on Fick’s steady state formula. Post-heating damage was quantified, as well, using various techniques such as ultrasonic pulse velocity waves, resonant frequency, compression test measurements. The results indicated significant reductions in compressive strength and estimated dynamic modulus ranging from 20 to 60% and 10 to 40%, respectively, with a corresponding increase in chloride diffusion coefficient reaching 80%. Both temperature and relative humidity levels had tangible impact on post-heating damage of SCC, hence percentage increase in chloride diffusion coefficient. The empirical models developed in this work showed excellent correlation between various damage indices and the percentage increase in diffusion coefficient. Furthermore, the electrical charge passing through SCC compared very well with the percentage increase in diffusion coefficient.
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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) | 0.000 | 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".