Report of RILEM TC 301-ASR: Modelling the impact of SCMs, alkali level and w/b ratio on alkali concentration in pore solution
Bibliographic record
Abstract
Abstract Supplementary cementitious materials (SCMs) can mitigate alkali-silica reaction by lowering the alkali metal concentration in the pore solution. This is a theoretical study on the applicability of a thermodynamic model (GEMS) and the empirical Taylor model to predict the required replacement level of portland cement (PC) by SCMs to achieve an alkali metal concentration below 300 mmol/L. The SCMs investigated are silica fume (SF), metakaolin (MK), fly ash (FA) and slag. The impact of the alkali content of the PC and the w/b ratio on the required replacement level is modelled and compared to experimental pore solution concentrations. Both models predict a similar impact of the SCM replacement level on the distribution of alkali between the pore solution, C–S–H and unreacted material. The thermodynamic model predicts little impact of the alkali content of PC and the w/b-ratio on the required replacement level, i.e., 20% SF, 20% MK, 40–50% FA and 60–70% slag. This is contrary to the Taylor model, which predicts that the replacement levels of FA and slag ranges from 7 to 58% when increasing the alkali content from 0.47 to 0.93% and from 80 to 10%, when increasing the w/b ratio from 0.3 to 0.9. The required replacement levels for SF and MK vary between 2 and 19% when increasing the alkali content from 0.47 to 0.93%, and from 40 to < 5% when increasing the w/b ratio from 0.3 to 0.9. The main difference between the two models is how they account for the uptake of alkali metals by the C–S–H.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".