Field and Laboratory Investigations on the Use of Fly Ash and LI-Based Admixtures to Prevent ASR in Concrete
Bibliographic record
Abstract
Since the early 1990’s, CANMET (Department of Natural Resources Canada) is carrying out a comparative field and laboratory research program to investigate the efficacy of laboratory test procedures for properly predicting the long-term efficacy of supplementary cementitious materials (SCM) in controlling expansion due to alkali-silica reaction (ASR). Binary and ternary concrete systems, i.e. fly ash (Class F), lithium-based admixtures, fly ash / Li-based admixtures, were selected with a variety of alkali-silica reactive aggregates. The expansive behaviour of the various combinations listed above was investigated in the laboratory using concrete prisms stored under accelerated test conditions (38oC and 100% RH). Exposure blocks cast from the above mixtures were placed outdoors at the CANMET facilities located in Ottawa (Canada). This paper compares the results of expansion testing in the laboratory against that of exposure blocks after 15 years outdoors. The results are also analysed in view of providing recommendations for the use of such materials / combinations for the manufacture of concrete that will be at a minimum risk of developing deleterious expansion and cracking due to ASR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".