Determining alkali leaching during accelerated ASR performance testing and in field exposed cubes using cold water extraction (CWE) and µXRF
Bibliographic record
Abstract
Considerable leaching of alkali metals was documented, both in concrete samples exposed to accelerated laboratory testing and field samples. CWE allowed to quantitatively determine the free alkali metal profiles as a function of the depth. However, CWE cannot account for the changes in the paste content towards a cast surface leading to a seemingly increase in alkali metals. The μXRF allowed to distinguish paste and aggregates. It allowed thereby to determine qualitative Na and K profiles in the cement paste phase of the concrete samples. The laboratory exposed samples showed a clear leaching profile into a depth of about 15 mm after 21 weeks of exposure at 60°C. Corresponding numbers for the 12 years field exposed cube were 50-60 mm. Alkali sorption by alkali silica gel was detected using the μXRF. For the laboratory exposed samples, the prisms prepared with Portland fly ash cement leached less alkali compared to the prisms prepared with ordinary Portland cement, as expected. The leaching in the middle of the prisms estimated based on the μXRF profiles agreed rather well with the level of alkali leaching determined based on the analysis of the leachate (i.e. the water below the samples during exposure).
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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".