Evaluation of Current ASTM Standards for ASR Prevention When Fine Lightweight Aggregates Are Used
Bibliographic record
Abstract
Abstract Previous research has shown that using fine lightweight aggregate (FLWA) can be a promising strategy to mitigate alkali-silica reaction (ASR) in mortar and concrete. However, limited studies focused on assessing current ASTM standards for their applicability in evaluating the efficacy of ASR mitigation using FLWAs. In this study, three commercially used FLWAs (expanded shale, clay, and slate) were investigated in mortar and concrete mixtures with reactive aggregates of different levels of reactivity. ASTM test methods, including ASTM C289-07, Standard Test Method for Potential Alkali-Silica Reactivity of Aggregates (Chemical Method) (Withdrawn 2016); ASTM C1260-14, Standard Test Method for Potential Alkali Reactivity of Aggregates (Mortar-Bar Method) (Superseded); and ASTM C1293-18, Standard Test Method for Determination of Length Change of Concrete due to Alkali-Silica Reaction (Superseded), were completed. Fine normal-weight aggregates were replaced by the FLWA at 25 % and 50 % by volume in the concrete mixtures, and 25 %, 50 %, and 100 % in mortar mixtures. Results showed that ASTM C1260-14 and ASTM C1293-18 can be used to evaluate the mitigation efficacy when pre-wetted FLWAs were used. The ASTM C289-07 test is not a reliable test method to study the reactivity of the FLWAs, but the results can be used to indicate the alkali-consuming ability of the FLWAs. All three FLWAs were effective in reducing ASR-induced expansion in both ASTM C1260-14 and ASTM C1293-18. The investigated FLWAs were especially effective in the concrete when moderately reactive aggregates, as classified by ASTM C1778-14, Standard Guide for Reducing the Risk of Deleterious Alkali-Aggregate Reaction in Concrete (Superseded), were used. For concrete with a highly reactive aggregate or very highly reactive aggregate, other mitigation strategies may need to be combined with FLWAs to effectively mitigate 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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".